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, 65.96it/s]
Epoch 0:   8%|โ–Š         | 1/13 [00:00<00:00, 64.83it/s, v_num=tion]
Epoch 0:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 70.37it/s, v_num=tion]
Epoch 0:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 69.87it/s, v_num=tion]
Epoch 0:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 72.71it/s, v_num=tion]
Epoch 0:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 72.38it/s, v_num=tion]
Epoch 0:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 73.72it/s, v_num=tion]
Epoch 0:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 73.46it/s, v_num=tion]
Epoch 0:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 74.98it/s, v_num=tion]
Epoch 0:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 74.77it/s, v_num=tion]
Epoch 0:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 75.71it/s, v_num=tion]
Epoch 0:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 75.52it/s, v_num=tion]
Epoch 0:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 76.32it/s, v_num=tion]
Epoch 0:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 76.14it/s, v_num=tion]
Epoch 0:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 76.18it/s, v_num=tion]
Epoch 0:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 76.03it/s, v_num=tion]
Epoch 0:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 76.52it/s, v_num=tion]
Epoch 0:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 76.39it/s, v_num=tion]
Epoch 0:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 76.95it/s, v_num=tion]
Epoch 0:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 76.83it/s, v_num=tion]
Epoch 0:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 77.11it/s, v_num=tion]
Epoch 0:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 77.01it/s, v_num=tion]
Epoch 0:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 77.00it/s, v_num=tion]
Epoch 0:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 76.91it/s, v_num=tion]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 77.51it/s, v_num=tion]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 77.42it/s, v_num=tion]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 70.50it/s, v_num=tion, val_loss=5.610]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 70.27it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 0:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:   8%|โ–Š         | 1/13 [00:00<00:00, 79.68it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:   8%|โ–Š         | 1/13 [00:00<00:00, 78.41it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.67it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.01it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 81.07it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.55it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 81.32it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.95it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.60it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.31it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.70it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.48it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.98it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.79it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.97it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.80it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.68it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.53it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.75it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.62it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.87it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.74it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.89it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.78it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.00it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.90it/s, v_num=tion, val_loss=5.610, train_loss=9.650]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.67it/s, v_num=tion, val_loss=3.720, train_loss=9.650]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.41it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 1:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:   8%|โ–Š         | 1/13 [00:00<00:00, 79.64it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:   8%|โ–Š         | 1/13 [00:00<00:00, 78.37it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.32it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.64it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.21it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 78.76it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.71it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.38it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.05it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.76it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 79.97it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 79.93it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 79.74it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.09it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 79.93it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.22it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.07it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.36it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.21it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.07it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.31it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.62it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.52it/s, v_num=tion, val_loss=3.720, train_loss=5.470]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.39it/s, v_num=tion, val_loss=3.130, train_loss=5.470]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.13it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 2:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:   8%|โ–Š         | 1/13 [00:00<00:00, 79.25it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:   8%|โ–Š         | 1/13 [00:00<00:00, 78.01it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.38it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.61it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.75it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.31it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.92it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.57it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.44it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.17it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.55it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.33it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.70it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.51it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.81it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.63it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.57it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.42it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.73it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.58it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.51it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.39it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.40it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.29it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.45it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.35it/s, v_num=tion, val_loss=3.130, train_loss=3.460]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.21it/s, v_num=tion, val_loss=2.530, train_loss=3.460]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.95it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 3:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:   8%|โ–Š         | 1/13 [00:00<00:00, 79.63it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:   8%|โ–Š         | 1/13 [00:00<00:00, 78.36it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.36it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.70it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.75it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.32it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.55it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.21it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.02it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.76it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 79.90it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 79.66it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 79.63it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 79.45it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 79.91it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 79.73it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.03it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.88it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.18it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.05it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.06it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.21it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.07it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.21it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.09it/s, v_num=tion, val_loss=2.530, train_loss=2.770]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.91it/s, v_num=tion, val_loss=2.330, train_loss=2.770]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.65it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 4:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:   8%|โ–Š         | 1/13 [00:00<00:00, 77.36it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:   8%|โ–Š         | 1/13 [00:00<00:00, 76.17it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.16it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 78.52it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.91it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.44it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.29it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.96it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.96it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.67it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.21it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 79.99it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.48it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.30it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.61it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.44it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.66it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.52it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 79.80it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 79.67it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 79.94it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 79.82it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.03it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 79.92it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.13it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.03it/s, v_num=tion, val_loss=2.330, train_loss=2.160]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.84it/s, v_num=tion, val_loss=2.480, train_loss=2.160]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.58it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 5:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:   8%|โ–Š         | 1/13 [00:00<00:00, 81.52it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:   8%|โ–Š         | 1/13 [00:00<00:00, 80.20it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.78it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.10it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.86it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.42it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 81.09it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.75it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 81.12it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.83it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.25it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.02it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.98it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.79it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 81.06it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.87it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 81.19it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 81.04it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.28it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.15it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 81.07it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.94it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 81.10it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.99it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.36it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.25it/s, v_num=tion, val_loss=2.480, train_loss=1.990]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.98it/s, v_num=tion, val_loss=1.970, train_loss=1.990]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.72it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 6:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:   8%|โ–Š         | 1/13 [00:00<00:00, 79.71it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:   8%|โ–Š         | 1/13 [00:00<00:00, 78.44it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.97it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.23it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 81.18it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.74it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.94it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.61it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.24it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.97it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.42it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.20it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.52it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.33it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.70it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.53it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.49it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.32it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.51it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.37it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.60it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.47it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.64it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.52it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.66it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.56it/s, v_num=tion, val_loss=1.970, train_loss=1.150]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.36it/s, v_num=tion, val_loss=2.070, train_loss=1.150]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.11it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 7:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:   8%|โ–Š         | 1/13 [00:00<00:00, 79.24it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:   8%|โ–Š         | 1/13 [00:00<00:00, 77.98it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.46it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.80it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.90it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.46it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.22it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.89it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.63it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.36it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.04it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.82it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.68it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.49it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.80it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.62it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.49it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.34it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.61it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.48it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.51it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.39it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.68it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.57it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.74it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.64it/s, v_num=tion, val_loss=2.070, train_loss=0.944]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.02it/s, v_num=tion, val_loss=1.930, train_loss=0.944]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.76it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 8:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:   8%|โ–Š         | 1/13 [00:00<00:00, 72.33it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:   8%|โ–Š         | 1/13 [00:00<00:00, 71.16it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 76.14it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 75.55it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 77.43it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 77.02it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 78.20it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 77.89it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 78.05it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 77.68it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 78.44it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 78.21it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 78.87it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 78.69it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 79.05it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 78.89it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 78.88it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 78.71it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 78.96it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 78.83it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 79.05it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 78.93it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 79.12it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 79.01it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 79.20it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 79.10it/s, v_num=tion, val_loss=1.930, train_loss=0.723]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.15it/s, v_num=tion, val_loss=2.020, train_loss=0.723]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 71.91it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 9:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:   8%|โ–Š         | 1/13 [00:00<00:00, 81.58it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:   8%|โ–Š         | 1/13 [00:00<00:00, 80.24it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.63it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.88it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 81.07it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.62it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 81.35it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 81.02it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 81.46it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 81.19it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.55it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.30it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 81.27it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 81.08it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 81.40it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 81.22it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 81.42it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 81.26it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.50it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.36it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 81.28it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 81.16it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 81.08it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.97it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.23it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.12it/s, v_num=tion, val_loss=2.020, train_loss=0.448]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.59it/s, v_num=tion, val_loss=1.720, train_loss=0.448]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.34it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 10:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:   8%|โ–Š         | 1/13 [00:00<00:00, 77.13it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:   8%|โ–Š         | 1/13 [00:00<00:00, 75.76it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 78.16it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 77.37it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 78.65it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 78.23it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.05it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 78.53it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 78.27it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 78.01it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 78.34it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 78.11it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 78.33it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 78.14it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 78.84it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 78.66it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 78.68it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 78.54it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 78.90it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 78.77it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 78.97it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 78.84it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 79.16it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 79.03it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 79.08it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 78.97it/s, v_num=tion, val_loss=1.720, train_loss=0.393]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 71.92it/s, v_num=tion, val_loss=2.070, train_loss=0.393]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 71.61it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 11:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:   8%|โ–Š         | 1/13 [00:00<00:00, 80.68it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:   8%|โ–Š         | 1/13 [00:00<00:00, 79.38it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.23it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.56it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.11it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.67it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.42it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.08it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.69it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.42it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.85it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.63it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 79.94it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 79.75it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.12it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 79.95it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.26it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.10it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.36it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.23it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.13it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.01it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.25it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.14it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.57it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.47it/s, v_num=tion, val_loss=2.070, train_loss=0.349]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.32it/s, v_num=tion, val_loss=2.090, train_loss=0.349]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.07it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 12:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:   8%|โ–Š         | 1/13 [00:00<00:00, 78.65it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:   8%|โ–Š         | 1/13 [00:00<00:00, 77.40it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.43it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.77it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.86it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.41it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.73it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.37it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.35it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.09it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.60it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.34it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.66it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.47it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.79it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.63it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.54it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.38it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.64it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.51it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.78it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.66it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.87it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.76it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.89it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.79it/s, v_num=tion, val_loss=2.090, train_loss=0.330]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.63it/s, v_num=tion, val_loss=2.150, train_loss=0.330]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.37it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 13:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:   8%|โ–Š         | 1/13 [00:00<00:00, 81.20it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:   8%|โ–Š         | 1/13 [00:00<00:00, 79.87it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.64it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.91it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.72it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.25it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 81.06it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.72it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 81.23it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.96it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.35it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.10it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 81.03it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.84it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 81.13it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.94it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 81.17it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 81.02it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.17it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.03it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.90it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.78it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.99it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.87it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.94it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.81it/s, v_num=tion, val_loss=2.150, train_loss=0.333]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.41it/s, v_num=tion, val_loss=2.380, train_loss=0.333]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.14it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 14:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:   8%|โ–Š         | 1/13 [00:00<00:00, 78.10it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:   8%|โ–Š         | 1/13 [00:00<00:00, 76.87it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.14it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.48it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.66it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.18it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.83it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.49it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.50it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.22it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.68it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.44it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.69it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.50it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.64it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.40it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.13it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.96it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.26it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.13it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.35it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.23it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.38it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.26it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.22it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.11it/s, v_num=tion, val_loss=2.380, train_loss=0.281]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.00it/s, v_num=tion, val_loss=2.200, train_loss=0.281]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.73it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 15:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:   8%|โ–Š         | 1/13 [00:00<00:00, 81.52it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:   8%|โ–Š         | 1/13 [00:00<00:00, 80.18it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.55it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.88it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.45it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.01it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.72it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.39it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.89it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.60it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.92it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.70it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.57it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.36it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.70it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.53it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.87it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.72it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.00it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.86it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.76it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.64it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.86it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.74it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.09it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.99it/s, v_num=tion, val_loss=2.200, train_loss=0.335]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.70it/s, v_num=tion, val_loss=1.890, train_loss=0.335]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.44it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 16:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:   8%|โ–Š         | 1/13 [00:00<00:00, 78.17it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:   8%|โ–Š         | 1/13 [00:00<00:00, 76.93it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.94it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.24it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.12it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.68it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.32it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.99it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.02it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.76it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.22it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.00it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.42it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.23it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.62it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.44it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.33it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.51it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.36it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.55it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.43it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.65it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.54it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.69it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.57it/s, v_num=tion, val_loss=1.890, train_loss=0.205]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.45it/s, v_num=tion, val_loss=2.080, train_loss=0.205]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.18it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 17:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:   8%|โ–Š         | 1/13 [00:00<00:00, 81.66it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:   8%|โ–Š         | 1/13 [00:00<00:00, 80.32it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.73it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.05it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.79it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.35it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 81.01it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.67it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 81.30it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 81.03it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.36it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 81.13it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 81.03it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.84it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 81.17it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 81.00it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 81.25it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 81.09it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.41it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 81.27it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 81.20it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 81.07it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 81.26it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 81.15it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.49it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.39it/s, v_num=tion, val_loss=2.080, train_loss=0.135]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 74.10it/s, v_num=tion, val_loss=1.900, train_loss=0.135]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.85it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 18:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:   8%|โ–Š         | 1/13 [00:00<00:00, 79.18it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:   8%|โ–Š         | 1/13 [00:00<00:00, 77.92it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.55it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.89it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.82it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.37it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.68it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.35it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.07it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.81it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.30it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.08it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.34it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.15it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.35it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.03it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.88it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.18it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.05it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.17it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.05it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.08it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.26it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.16it/s, v_num=tion, val_loss=1.900, train_loss=0.121]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.08it/s, v_num=tion, val_loss=2.000, train_loss=0.121]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.82it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 19:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:   8%|โ–Š         | 1/13 [00:00<00:00, 78.60it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:   8%|โ–Š         | 1/13 [00:00<00:00, 77.35it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 78.02it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 77.40it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 77.81it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 77.38it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 78.51it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 78.17it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 78.72it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 78.46it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 78.94it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 78.73it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 78.86it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 78.67it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 78.97it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 78.81it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.28it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.13it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 79.38it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 79.25it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 79.31it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 79.20it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 79.40it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 79.27it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 79.66it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 79.55it/s, v_num=tion, val_loss=2.000, train_loss=0.092]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.41it/s, v_num=tion, val_loss=2.050, train_loss=0.092]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.15it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 20:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:   8%|โ–Š         | 1/13 [00:00<00:00, 77.26it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:   8%|โ–Š         | 1/13 [00:00<00:00, 76.06it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.39it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 78.75it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.00it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.56it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.43it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.10it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.34it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.03it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.67it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.43it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.84it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.65it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.99it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.82it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.76it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.60it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.92it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.79it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 81.02it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.89it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 81.10it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.99it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.10it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.99it/s, v_num=tion, val_loss=2.050, train_loss=0.0845]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.56it/s, v_num=tion, val_loss=1.730, train_loss=0.0845]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.30it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 21:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:   8%|โ–Š         | 1/13 [00:00<00:00, 79.76it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:   8%|โ–Š         | 1/13 [00:00<00:00, 78.49it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.88it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.23it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 78.54it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 78.11it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.03it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 78.71it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.13it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 78.87it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 79.37it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 79.16it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 78.99it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 78.80it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 79.20it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 79.03it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.09it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 78.95it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 79.19it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 79.05it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 78.86it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 78.74it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 79.07it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 78.97it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 79.37it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 79.27it/s, v_num=tion, val_loss=1.730, train_loss=0.0588]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.14it/s, v_num=tion, val_loss=1.910, train_loss=0.0588]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 71.89it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 22:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:   8%|โ–Š         | 1/13 [00:00<00:00, 78.27it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:   8%|โ–Š         | 1/13 [00:00<00:00, 76.91it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 78.71it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 78.08it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.17it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 78.69it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.59it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 79.26it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.65it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 79.34it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 79.92it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 79.70it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.18it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.00it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.36it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.05it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 79.90it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.06it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.29it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.17it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.42it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.31it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.52it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.42it/s, v_num=tion, val_loss=1.910, train_loss=0.0419]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.28it/s, v_num=tion, val_loss=1.870, train_loss=0.0419]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.01it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 23:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:   8%|โ–Š         | 1/13 [00:00<00:00, 81.99it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:   8%|โ–Š         | 1/13 [00:00<00:00, 80.50it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 81.22it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.53it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.19it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 79.75it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.60it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.27it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.75it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.49it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.92it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.67it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.36it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.15it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.62it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.43it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.75it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.60it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.90it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.77it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.65it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.53it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.77it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.66it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.05it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.94it/s, v_num=tion, val_loss=1.870, train_loss=0.053]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.69it/s, v_num=tion, val_loss=1.800, train_loss=0.053]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.44it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 24:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:   0%|          | 0/13 [00:00<?, ?it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:   8%|โ–Š         | 1/13 [00:00<00:00, 79.42it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:   8%|โ–Š         | 1/13 [00:00<00:00, 78.02it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 80.43it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  15%|โ–ˆโ–Œ        | 2/13 [00:00<00:00, 79.77it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 81.14it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  23%|โ–ˆโ–ˆโ–Ž       | 3/13 [00:00<00:00, 80.69it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 81.26it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  31%|โ–ˆโ–ˆโ–ˆ       | 4/13 [00:00<00:00, 80.93it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.81it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 5/13 [00:00<00:00, 80.54it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.96it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  46%|โ–ˆโ–ˆโ–ˆโ–ˆโ–Œ     | 6/13 [00:00<00:00, 80.73it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 81.09it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  54%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–    | 7/13 [00:00<00:00, 80.89it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 81.16it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–   | 8/13 [00:00<00:00, 80.98it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.85it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  69%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‰   | 9/13 [00:00<00:00, 80.70it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.98it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  77%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–‹  | 10/13 [00:00<00:00, 80.83it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 81.06it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  85%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ– | 11/13 [00:00<00:00, 80.94it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 81.09it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25:  92%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–| 12/13 [00:00<00:00, 80.98it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 81.01it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 80.89it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.52it/s, v_num=tion, val_loss=1.800, train_loss=0.0877]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 73.27it/s, v_num=tion, val_loss=1.800, train_loss=0.104]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 13/13 [00:00<00:00, 72.98it/s, v_num=tion, val_loss=1.800, train_loss=0.104]
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€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             0.924605131149292
         mse_val            1.8015339374542236
         r2_val             0.7826212048530579
        val_loss             1.801533818244934
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€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: 0.783

Total running time of the script: (0 minutes 5.094 seconds)

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