Note

Go to the end to download the full example code.

Train/Test split: Regression๏ƒ

๐Ÿš€ In this tutorial, weโ€™ll explore regression using a train/test split. Specifically, weโ€™re using the TabularCrossmodalMultiheadAttention model.

Data:

The data we are using is 500 rows of the MNIST dataset, split into top and bottom halves as our two tabular modalities. The bottom halfโ€™s values have been inverted to make the task more difficult. The prediction labels (the number shown in the image) has been changed into a continuous variable (1.0, 2.0, 3.0, etc.) and had some noise added to it. So the labels look more like 1.05, 2.02, 3.01, etc.

Key Features:

  • ๐Ÿ“ฅ Importing a model based on its path.

  • ๐Ÿงช Training and testing a model with train/test split.

  • ๐Ÿ“ˆ Plotting the loss curves of each fold.

  • ๐Ÿ“Š Visualising the results of a single train/test model using the RealsVsPreds class.

import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import os

from fusilli.data import prepare_fusion_data
from fusilli.eval import RealsVsPreds
from fusilli.train import train_and_save_models

# sphinx_gallery_thumbnail_number = -1

1. Import the fusion model ๐Ÿ”๏ƒ

Weโ€™re importing only one model for this example, the TabularCrossmodalMultiheadAttention model. Instead of using the import_chosen_fusion_models() function, weโ€™re importing the model directly like with any other library method.

from fusilli.fusionmodels.tabularfusion.crossmodal_att import (
    TabularCrossmodalMultiheadAttention,
)

2. Set the training parameters ๐ŸŽฏ๏ƒ

Now weโ€™re configuring our training parameters.

For training and testing, the necessary parameters are: - Paths to the input data files. - Paths to the output directories. - prediction_task: the type of prediction to be performed. This is either regression, binary, or classification.

Some optional parameters are:

  • kfold: a boolean of whether to use k-fold cross-validation (True) or not (False). By default, this is set to False.

  • num_folds: the number of folds to use. It canโ€™t be k=1.

  • wandb_logging: a boolean of whether to log the results using Weights and Biases (True) or not (False). Default is False.

  • test_size: the proportion of the dataset to include in the test split. Default is 0.2.

  • batch_size: the batch size to use for training. Default is 8.

  • multiclass_dimensions: the number of classes to use for multiclass classification. Default is None unless prediction_task is multiclass.

  • max_epochs: the maximum number of epochs to train for. Default is 1000.

# Regression task
prediction_task = "regression"

# Set the batch size
batch_size = 32

# Setting output directories
output_paths = {
    "losses": "loss_logs/one_model_regression_traintest",
    "checkpoints": "checkpoints/one_model_regression_traintest",
    "figures": "figures/one_model_regression_traintest",
}

# Create the output directories if they don't exist
for path in output_paths.values():
    os.makedirs(path, exist_ok=True)

# Clearing the loss logs directory (only for the example notebooks)
for dir in os.listdir(output_paths["losses"]):
    # remove files
    for file in os.listdir(os.path.join(output_paths["losses"], dir)):
        os.remove(os.path.join(output_paths["losses"], dir, file))
    # remove dir
    os.rmdir(os.path.join(output_paths["losses"], dir))

3. Specifying input file paths ๐Ÿ”ฎ๏ƒ

Weโ€™re using the MNIST dataset for this example, and the CSV files are stored in the _static/mnist_data directory with the documentation files.

data_paths = {
    "tabular1": "../../_static/mnist_data/mnist1_regression.csv",
    "tabular2": "../../_static/mnist_data/mnist2_regression.csv",
    "image": "",
}

4. Training the fusion model ๐Ÿ๏ƒ

Now weโ€™re ready to train our model. Weโ€™re using the train_and_save_models() function to train our model.

First we need to create a data module using the prepare_fusion_data() function. This function takes the following parameters:

  • prediction_task: the type of prediction to be performed.

  • fusion_model: the fusion model to be trained.

  • data_paths: the paths to the input data files.

  • output_paths: the paths to the output directories.

Then we pass the data module and the fusion model to the train_and_save_models() function. Weโ€™re not using checkpointing for this example, so we set enable_checkpointing=False. Weโ€™re also setting show_loss_plot=True to plot the loss curve.

fusion_model = TabularCrossmodalMultiheadAttention

print("method_name:", fusion_model.method_name)
print("modality_type:", fusion_model.modality_type)
print("fusion_type:", fusion_model.fusion_type)

dm = prepare_fusion_data(prediction_task=prediction_task,
                         fusion_model=fusion_model,
                         data_paths=data_paths,
                         output_paths=output_paths,
                         batch_size=batch_size)

# train and test
single_model_list = train_and_save_models(
    data_module=dm,
    fusion_model=fusion_model,
    enable_checkpointing=False,  # False for the example notebooks
    show_loss_plot=True,
    metrics_list=["r2", "mae", "mse"]
)
Loss Curves for TabularCrossmodalMultiheadAttention
method_name: Tabular Crossmodal multi-head attention
modality_type: tabular_tabular
fusion_type: attention

Training: |          | 0/? [00:00<?, ?it/s]
Training:   0%|          | 0/13 [00:00<?, ?it/s]
Epoch 0:   0%|          | 0/13 [00:00<?, ?it/s]
Epoch 0:   8%|โ–Š         | 1/13 [00:00<00:00, 51.32it/s]
Epoch 0:   8%|โ–Š         | 1/13 [00:00<00:00, 50.39it/s, v_num=tion]
Epoch 0:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 57.59it/s, v_num=tion]
Epoch 0:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 57.07it/s, v_num=tion]
Epoch 0:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 60.36it/s, v_num=tion]
Epoch 0:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 59.97it/s, v_num=tion]
Epoch 0:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 61.47it/s, v_num=tion]
Epoch 0:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 61.16it/s, v_num=tion]
Epoch 0:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 61.82it/s, v_num=tion]
Epoch 0:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 61.57it/s, v_num=tion]
Epoch 0:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 62.57it/s, v_num=tion]
Epoch 0:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 62.36it/s, v_num=tion]
Epoch 0:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 63.08it/s, v_num=tion]
Epoch 0:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 62.90it/s, v_num=tion]
Epoch 0:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 63.40it/s, v_num=tion]
Epoch 0:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 63.23it/s, v_num=tion]
Epoch 0:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 63.72it/s, v_num=tion]
Epoch 0:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 63.58it/s, v_num=tion]
Epoch 0:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 63.71it/s, v_num=tion]
Epoch 0:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 63.58it/s, v_num=tion]
Epoch 0:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 64.08it/s, v_num=tion]
Epoch 0:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 63.96it/s, v_num=tion]
Epoch 0:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 64.26it/s, v_num=tion]
Epoch 0:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 64.15it/s, v_num=tion]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 64.59it/s, v_num=tion]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 64.49it/s, v_num=tion]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.25it/s, v_num=tion, val_loss=6.740]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.02it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 0:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:   8%|โ–Š         | 1/13 [00:00<00:00, 62.51it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:   8%|โ–Š         | 1/13 [00:00<00:00, 61.25it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.66it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.01it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.88it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.43it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.98it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.62it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.25it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.97it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.42it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.18it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.06it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.85it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.14it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 64.96it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.25it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.08it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.14it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 64.98it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 64.83it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 64.70it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 64.98it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 64.86it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.40it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.29it/s, v_num=tion, val_loss=6.740, train_loss=12.00]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.93it/s, v_num=tion, val_loss=4.320, train_loss=12.00]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.70it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 1:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:   8%|โ–Š         | 1/13 [00:00<00:00, 65.15it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:   8%|โ–Š         | 1/13 [00:00<00:00, 63.82it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 65.97it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 65.26it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 66.11it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.61it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.98it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.61it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.04it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.74it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.93it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.69it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.26it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.06it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.55it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.37it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.65it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.49it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.43it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.28it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.55it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.42it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.61it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.49it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.89it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.77it/s, v_num=tion, val_loss=4.320, train_loss=5.570]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.98it/s, v_num=tion, val_loss=3.860, train_loss=5.570]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.74it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 2:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:   8%|โ–Š         | 1/13 [00:00<00:00, 65.58it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:   8%|โ–Š         | 1/13 [00:00<00:00, 64.07it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.95it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.23it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.25it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.77it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.75it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.38it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.95it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.65it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.05it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.81it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.15it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.94it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 64.78it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 64.59it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 64.88it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 64.72it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.06it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 64.91it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.24it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.11it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.12it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.00it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.44it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.32it/s, v_num=tion, val_loss=3.860, train_loss=4.010]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.85it/s, v_num=tion, val_loss=3.020, train_loss=4.010]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.59it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 3:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:   8%|โ–Š         | 1/13 [00:00<00:00, 65.90it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:   8%|โ–Š         | 1/13 [00:00<00:00, 64.55it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.09it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 62.45it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 63.72it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 63.26it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.02it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 63.67it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.50it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.22it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.04it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.81it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.97it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.77it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.34it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.16it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.61it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.45it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.78it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.64it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.64it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.51it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.75it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.63it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.01it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.90it/s, v_num=tion, val_loss=3.020, train_loss=3.060]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.16it/s, v_num=tion, val_loss=3.800, train_loss=3.060]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.85it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 4:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:   8%|โ–Š         | 1/13 [00:00<00:00, 60.63it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:   8%|โ–Š         | 1/13 [00:00<00:00, 59.48it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 62.19it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 61.55it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 63.26it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 62.78it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.46it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.14it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.31it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.06it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.37it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.15it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.59it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.40it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.72it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.55it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.96it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.81it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.58it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.44it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.50it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.38it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.56it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.44it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.85it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.74it/s, v_num=tion, val_loss=3.800, train_loss=2.270]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.85it/s, v_num=tion, val_loss=2.820, train_loss=2.270]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.60it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 5:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:   8%|โ–Š         | 1/13 [00:00<00:00, 64.19it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:   8%|โ–Š         | 1/13 [00:00<00:00, 62.80it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.36it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.68it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.03it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.57it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.79it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.44it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.03it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.74it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.23it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.99it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.29it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.08it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.34it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.16it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 64.71it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 64.54it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 64.70it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 64.55it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 64.70it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 64.57it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 64.54it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 64.42it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 64.28it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 64.17it/s, v_num=tion, val_loss=2.820, train_loss=1.800]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.67it/s, v_num=tion, val_loss=3.370, train_loss=1.800]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.39it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 6:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:   8%|โ–Š         | 1/13 [00:00<00:00, 64.75it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:   8%|โ–Š         | 1/13 [00:00<00:00, 63.35it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 65.13it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.42it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.45it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.96it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.60it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.24it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.05it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 63.73it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.18it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 63.96it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.21it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.00it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 63.63it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 63.45it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 63.77it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 63.60it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 63.83it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 63.68it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 63.98it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 63.85it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 63.71it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 63.58it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 63.96it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 63.84it/s, v_num=tion, val_loss=3.370, train_loss=1.240]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.53it/s, v_num=tion, val_loss=2.770, train_loss=1.240]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.29it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 7:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:   8%|โ–Š         | 1/13 [00:00<00:00, 64.52it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:   8%|โ–Š         | 1/13 [00:00<00:00, 63.17it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 62.48it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 61.73it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 62.89it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 62.44it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 62.90it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 62.55it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 62.88it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 62.60it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 63.26it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 63.02it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 63.03it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 62.82it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 63.29it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 63.12it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 63.64it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 63.48it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 63.91it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 63.76it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 63.83it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 63.69it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 63.96it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 63.84it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 64.23it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 64.11it/s, v_num=tion, val_loss=2.770, train_loss=1.000]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.83it/s, v_num=tion, val_loss=3.100, train_loss=1.000]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.60it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 8:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:   8%|โ–Š         | 1/13 [00:00<00:00, 62.91it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:   8%|โ–Š         | 1/13 [00:00<00:00, 61.60it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.23it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.50it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.24it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 63.76it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.66it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.31it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.80it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.50it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.58it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.32it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.88it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.66it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 64.98it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 64.80it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 64.97it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 64.79it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 64.28it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 64.13it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 64.44it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 64.31it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 64.61it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 64.49it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 64.85it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 64.74it/s, v_num=tion, val_loss=3.100, train_loss=0.745]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.03it/s, v_num=tion, val_loss=2.580, train_loss=0.745]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 58.78it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 9:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:   8%|โ–Š         | 1/13 [00:00<00:00, 65.50it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:   8%|โ–Š         | 1/13 [00:00<00:00, 64.01it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.62it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 62.91it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.30it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 63.77it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 64.12it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 63.74it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.33it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.03it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.71it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.47it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.07it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.87it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.41it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.24it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.22it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.06it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.37it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.22it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.43it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.29it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.56it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.44it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.57it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.46it/s, v_num=tion, val_loss=2.580, train_loss=0.565]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.97it/s, v_num=tion, val_loss=2.560, train_loss=0.565]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.72it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 10:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:   8%|โ–Š         | 1/13 [00:00<00:00, 66.69it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:   8%|โ–Š         | 1/13 [00:00<00:00, 65.29it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 66.59it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 65.86it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 66.65it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 66.14it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.42it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.05it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.58it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.29it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.77it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.52it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.96it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.75it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.68it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.50it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.82it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.66it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.96it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.80it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.20it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.08it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.17it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.05it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.55it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.45it/s, v_num=tion, val_loss=2.560, train_loss=0.459]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.86it/s, v_num=tion, val_loss=3.110, train_loss=0.459]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.62it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 11:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:   8%|โ–Š         | 1/13 [00:00<00:00, 68.53it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:   8%|โ–Š         | 1/13 [00:00<00:00, 67.20it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.41it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 66.77it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.46it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.02it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.89it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.55it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 69.28it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.97it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 69.56it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 69.33it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 69.12it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.92it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 69.23it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 69.05it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 69.24it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 69.08it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 69.40it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 69.24it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 69.20it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 69.07it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 69.30it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 69.19it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 69.61it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 69.50it/s, v_num=tion, val_loss=3.110, train_loss=0.311]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 63.49it/s, v_num=tion, val_loss=2.590, train_loss=0.311]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 63.24it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 12:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:   8%|โ–Š         | 1/13 [00:00<00:00, 65.69it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:   8%|โ–Š         | 1/13 [00:00<00:00, 64.49it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.99it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.27it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.93it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.48it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.86it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.53it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.70it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.43it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.70it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.48it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.05it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.86it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.26it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.06it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.37it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.21it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.17it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.03it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.37it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.25it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.51it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.40it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.84it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.73it/s, v_num=tion, val_loss=2.590, train_loss=0.195]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.12it/s, v_num=tion, val_loss=2.980, train_loss=0.195]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.86it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 13:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:   8%|โ–Š         | 1/13 [00:00<00:00, 68.51it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:   8%|โ–Š         | 1/13 [00:00<00:00, 67.16it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 68.42it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.42it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.97it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.54it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.04it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.71it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.94it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.67it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.08it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.84it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.32it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.12it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.43it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.25it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.22it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.01it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.75it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.59it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.56it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.43it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.55it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.43it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.36it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.26it/s, v_num=tion, val_loss=2.980, train_loss=0.149]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.56it/s, v_num=tion, val_loss=2.460, train_loss=0.149]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.32it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 14:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:   8%|โ–Š         | 1/13 [00:00<00:00, 68.04it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:   8%|โ–Š         | 1/13 [00:00<00:00, 66.63it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.90it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.10it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.88it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.40it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.63it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.27it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.56it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.27it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.72it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.49it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.90it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.69it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.55it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.37it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.75it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.59it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.84it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.70it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.92it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.79it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.65it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.53it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.97it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.86it/s, v_num=tion, val_loss=2.460, train_loss=0.163]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.06it/s, v_num=tion, val_loss=2.650, train_loss=0.163]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.79it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 15:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:   8%|โ–Š         | 1/13 [00:00<00:00, 66.52it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:   8%|โ–Š         | 1/13 [00:00<00:00, 65.15it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.65it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.02it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.64it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.15it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.46it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.14it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.15it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.88it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.70it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.47it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.35it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.16it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.73it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.54it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.44it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.29it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.46it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.31it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.02it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.89it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.07it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.94it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.37it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.27it/s, v_num=tion, val_loss=2.650, train_loss=0.134]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.45it/s, v_num=tion, val_loss=2.550, train_loss=0.134]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.18it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 16:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:   8%|โ–Š         | 1/13 [00:00<00:00, 61.54it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:   8%|โ–Š         | 1/13 [00:00<00:00, 60.41it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.88it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.24it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.82it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.37it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.76it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.40it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.44it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.16it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.78it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 64.55it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.99it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.80it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.21it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.05it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.65it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.49it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.75it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.62it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.10it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.98it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.36it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.25it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.78it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.67it/s, v_num=tion, val_loss=2.550, train_loss=0.115]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.92it/s, v_num=tion, val_loss=2.490, train_loss=0.115]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.68it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 17:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:   8%|โ–Š         | 1/13 [00:00<00:00, 68.85it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:   8%|โ–Š         | 1/13 [00:00<00:00, 67.53it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 69.05it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 68.36it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 69.20it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.73it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.46it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.12it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.85it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.58it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 69.03it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.79it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.95it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.74it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.94it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.76it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.58it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.42it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.51it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.36it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.63it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.51it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.76it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.65it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.78it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.67it/s, v_num=tion, val_loss=2.490, train_loss=0.106]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.80it/s, v_num=tion, val_loss=2.660, train_loss=0.106]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.54it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 18:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:   8%|โ–Š         | 1/13 [00:00<00:00, 66.65it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:   8%|โ–Š         | 1/13 [00:00<00:00, 65.35it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.59it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 66.91it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.84it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.37it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.42it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.08it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.02it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.75it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.27it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.04it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.03it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.84it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.90it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.74it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.23it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.08it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.29it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.15it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.45it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.31it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.32it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.20it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.65it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.54it/s, v_num=tion, val_loss=2.660, train_loss=0.0819]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.71it/s, v_num=tion, val_loss=2.200, train_loss=0.0819]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.46it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 19:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:   8%|โ–Š         | 1/13 [00:00<00:00, 69.42it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:   8%|โ–Š         | 1/13 [00:00<00:00, 68.07it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.43it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 66.76it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.91it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.43it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.15it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.79it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.22it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.92it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.25it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.00it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.53it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.31it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.54it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.35it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.59it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.43it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.68it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.54it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.36it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.22it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.53it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.42it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.94it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.82it/s, v_num=tion, val_loss=2.200, train_loss=0.0614]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.12it/s, v_num=tion, val_loss=2.550, train_loss=0.0614]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.87it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 20:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:   8%|โ–Š         | 1/13 [00:00<00:00, 65.52it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:   8%|โ–Š         | 1/13 [00:00<00:00, 64.27it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.16it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 66.50it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.87it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.36it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.32it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.98it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.67it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.39it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.28it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.04it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.30it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.10it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.43it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.25it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.51it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.35it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.23it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.09it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.39it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.27it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.47it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.35it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.60it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.48it/s, v_num=tion, val_loss=2.550, train_loss=0.0535]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.31it/s, v_num=tion, val_loss=2.370, train_loss=0.0535]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.07it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 21:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:   8%|โ–Š         | 1/13 [00:00<00:00, 69.40it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:   8%|โ–Š         | 1/13 [00:00<00:00, 68.06it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 69.51it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 68.82it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 69.49it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 69.02it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.42it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.05it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.41it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.12it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.42it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.18it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.49it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.29it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.65it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.47it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.32it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.16it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.99it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.83it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.88it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.75it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.76it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.63it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.65it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.52it/s, v_num=tion, val_loss=2.370, train_loss=0.0513]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.76it/s, v_num=tion, val_loss=2.270, train_loss=0.0513]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.51it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 22:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:   8%|โ–Š         | 1/13 [00:00<00:00, 68.00it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:   8%|โ–Š         | 1/13 [00:00<00:00, 66.58it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 68.23it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.54it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.50it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.01it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.67it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.33it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.11it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.84it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.61it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.39it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.96it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.77it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.48it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.31it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.57it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.35it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.38it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.23it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.22it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.09it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.93it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.82it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.23it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.12it/s, v_num=tion, val_loss=2.270, train_loss=0.0478]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.29it/s, v_num=tion, val_loss=2.400, train_loss=0.0478]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.03it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 23:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:   8%|โ–Š         | 1/13 [00:00<00:00, 66.37it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:   8%|โ–Š         | 1/13 [00:00<00:00, 65.06it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.36it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.72it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.24it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.79it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.36it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.00it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 65.26it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.97it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.53it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.30it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.98it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 64.79it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.54it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.38it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.84it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.70it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.93it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.80it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.90it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.78it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.23it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.12it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.67it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.57it/s, v_num=tion, val_loss=2.400, train_loss=0.0612]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.98it/s, v_num=tion, val_loss=2.350, train_loss=0.0612]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.74it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 24:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:   8%|โ–Š         | 1/13 [00:00<00:00, 65.05it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:   8%|โ–Š         | 1/13 [00:00<00:00, 63.82it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.01it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 66.34it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.60it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.14it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.47it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.12it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.89it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.60it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.00it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.77it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.03it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.83it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.82it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.64it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.61it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.43it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.10it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.95it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.31it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.18it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.41it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.29it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.55it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.43it/s, v_num=tion, val_loss=2.350, train_loss=0.0607]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.42it/s, v_num=tion, val_loss=2.430, train_loss=0.0607]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.16it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 25:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:   8%|โ–Š         | 1/13 [00:00<00:00, 62.60it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:   8%|โ–Š         | 1/13 [00:00<00:00, 61.30it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 62.96it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 62.31it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 63.63it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 63.20it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 63.98it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 63.66it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.82it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 64.55it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.23it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.02it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.71it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.52it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.06it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 65.88it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.80it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 65.64it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.72it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.57it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.60it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.47it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.48it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 65.36it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.38it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 65.27it/s, v_num=tion, val_loss=2.430, train_loss=0.0538]
Epoch 26: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.77it/s, v_num=tion, val_loss=2.300, train_loss=0.0538]
Epoch 26: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 59.53it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 26:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:   8%|โ–Š         | 1/13 [00:00<00:00, 67.12it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:   8%|โ–Š         | 1/13 [00:00<00:00, 65.79it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 68.02it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.35it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.50it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.05it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 68.08it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.73it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.38it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 68.10it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.51it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 68.27it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.64it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 68.40it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 68.14it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.95it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 68.16it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.99it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.22it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.08it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.29it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 68.16it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.14it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.02it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.37it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.24it/s, v_num=tion, val_loss=2.300, train_loss=0.0492]
Epoch 27: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.41it/s, v_num=tion, val_loss=2.390, train_loss=0.0492]
Epoch 27: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.15it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 27:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:   8%|โ–Š         | 1/13 [00:00<00:00, 66.42it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:   8%|โ–Š         | 1/13 [00:00<00:00, 65.06it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.35it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.68it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.46it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.02it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.15it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.83it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.90it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.64it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.45it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.24it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.46it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.24it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.78it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.61it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.90it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.74it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 68.06it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.92it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.86it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.73it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.99it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.87it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.05it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.94it/s, v_num=tion, val_loss=2.390, train_loss=0.0378]
Epoch 28: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.13it/s, v_num=tion, val_loss=2.430, train_loss=0.0378]
Epoch 28: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.88it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 28:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:   8%|โ–Š         | 1/13 [00:00<00:00, 64.50it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:   8%|โ–Š         | 1/13 [00:00<00:00, 63.26it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 65.53it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.84it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.96it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.49it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.54it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.18it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.03it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.75it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.82it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.59it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.15it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.95it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.43it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.26it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.71it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.56it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.67it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.54it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.92it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.80it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 68.06it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.94it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.27it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 68.16it/s, v_num=tion, val_loss=2.430, train_loss=0.028]
Epoch 29: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 62.06it/s, v_num=tion, val_loss=2.390, train_loss=0.028]
Epoch 29: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.81it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 29:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:   8%|โ–Š         | 1/13 [00:00<00:00, 67.72it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:   8%|โ–Š         | 1/13 [00:00<00:00, 66.35it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.86it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.16it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.22it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.75it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.62it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.26it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.58it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.29it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.70it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.45it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.65it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.43it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.74it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.55it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.30it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.14it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.44it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.29it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.40it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.27it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.47it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.35it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.45it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.34it/s, v_num=tion, val_loss=2.390, train_loss=0.0305]
Epoch 30: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.60it/s, v_num=tion, val_loss=2.380, train_loss=0.0305]
Epoch 30: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.35it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 30:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:   8%|โ–Š         | 1/13 [00:00<00:00, 67.93it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:   8%|โ–Š         | 1/13 [00:00<00:00, 66.58it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 68.11it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 67.42it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 68.37it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.89it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.37it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 67.00it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 67.28it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.97it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.15it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.90it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.21it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.96it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.81it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.63it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.87it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.71it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.82it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.67it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.82it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.68it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.27it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.14it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.39it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.27it/s, v_num=tion, val_loss=2.380, train_loss=0.0207]
Epoch 31: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.63it/s, v_num=tion, val_loss=2.310, train_loss=0.0207]
Epoch 31: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.39it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 31:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:   8%|โ–Š         | 1/13 [00:00<00:00, 65.21it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:   8%|โ–Š         | 1/13 [00:00<00:00, 63.95it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.81it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 64.18it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.63it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.17it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.08it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.73it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.59it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.31it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.75it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.50it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.37it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.17it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.51it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.33it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.76it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.61it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.96it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 66.82it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.75it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.60it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.71it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.58it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.65it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.53it/s, v_num=tion, val_loss=2.310, train_loss=0.0151]
Epoch 32: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.63it/s, v_num=tion, val_loss=2.390, train_loss=0.0151]
Epoch 32: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.36it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 32:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:   8%|โ–Š         | 1/13 [00:00<00:00, 61.15it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:   8%|โ–Š         | 1/13 [00:00<00:00, 59.94it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 63.65it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 62.99it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 65.26it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 64.82it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.07it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 65.74it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.77it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.47it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.98it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 65.73it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 66.00it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 65.78it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.26it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 66.09it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.25it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 66.10it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.86it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 65.72it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 66.06it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 65.94it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.32it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 66.21it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.73it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 66.62it/s, v_num=tion, val_loss=2.390, train_loss=0.0118]
Epoch 33: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.80it/s, v_num=tion, val_loss=2.350, train_loss=0.0118]
Epoch 33: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.56it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 33:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:   8%|โ–Š         | 1/13 [00:00<00:00, 66.92it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:   8%|โ–Š         | 1/13 [00:00<00:00, 65.59it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 66.58it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 65.89it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 67.02it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 66.54it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.44it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 66.09it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.97it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 66.69it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 67.22it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 66.99it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.49it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 67.29it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.58it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 67.40it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.35it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 67.19it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.53it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 67.38it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.43it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 67.28it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.44it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 67.27it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.15it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 67.04it/s, v_num=tion, val_loss=2.350, train_loss=0.0136]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.28it/s, v_num=tion, val_loss=2.340, train_loss=0.0136]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 61.04it/s, v_num=tion, val_loss=2.340, train_loss=0.0134]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 60.70it/s, v_num=tion, val_loss=2.340, train_loss=0.0134]
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
     Validate metric           DataLoader 0
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
         mae_val            1.1683200597763062
         mse_val            2.3420941829681396
         r2_val             0.6529946327209473
        val_loss            2.3420941829681396
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€

6. Plotting the results ๐Ÿ“Š๏ƒ

Now weโ€™re ready to plot the results of our model. Weโ€™re using the RealsVsPreds class to plot the confusion matrix.

reals_preds_fig = RealsVsPreds.from_final_val_data(
    single_model_list
)
plt.show()
Evaluation: Validation Data, Tabular Crossmodal multi-head attention - Validation r2: 0.653

Total running time of the script: (0 minutes 8.233 seconds)

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