Note

Go to the end to download the full example code.

Regression: Comparing Two Tabular Models Trained on Simulated Data๏ƒ

๐Ÿš€ Welcome to this tutorial on training and comparing two fusion models on a regression task using simulated multimodal tabular data! ๐ŸŽ‰

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.

๐ŸŒŸ Key Features:

  • ๐Ÿ“ฅ Importing models based on name.

  • ๐Ÿงช Training and testing models with train/test protocol.

  • ๐Ÿ’พ Saving trained models to a dictionary for later analysis.

  • ๐Ÿ“Š Plotting the results of a single model.

  • ๐Ÿ“ˆ Plotting the results of multiple models as a bar chart.

  • ๐Ÿ’พ Saving the results of multiple models as a CSV file.

import importlib

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

from fusilli.data import prepare_fusion_data
from fusilli.eval import RealsVsPreds, ModelComparison
from fusilli.train import train_and_save_models
from fusilli.utils.model_chooser import import_chosen_fusion_models

# sphinx_gallery_thumbnail_number = -1

1. Import fusion models ๐Ÿ”๏ƒ

Letโ€™s kick things off by importing our fusion models. The models are imported using the import_chosen_fusion_models() function, which takes a dictionary of conditions as an input. The conditions are the attributes of the models, e.g. the class name, the modality type, etc.

The function returns list of class objects that match the conditions. If no conditions are specified, then all the models are returned.

Weโ€™re importing Tabular1Unimodal and ConcatTabularFeatureMaps `models for this example, so we have one unimodal benchmark and one multimodal model.

model_conditions = {
    "class_name": ["Tabular1Unimodal", "ConcatTabularFeatureMaps"],
}

fusion_models = import_chosen_fusion_models(model_conditions)

Imported methods:
['Tabular1 uni-modal' 'Concatenating tabular feature maps']

2. Set the training parameters ๐ŸŽฏ๏ƒ

Now, letโ€™s configure our training parameters. The parameters are stored in a dictionary and passed to most of the methods in this library.

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 (predicting a binary variable - 0 or 1)
prediction_task = "regression"

# Set the batch size
batch_size = 48

# Set the test_size
test_size = 0.3

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

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 MNIST data 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 first fusion model ๐Ÿ๏ƒ

Here we train the first fusion model. Weโ€™re using the train_and_save_models function to train and test the models. This function takes the following inputs:

  • 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.

First weโ€™ll create a dictionary to store both the trained models so we can compare them later.

all_trained_models = {}  # create dictionary to store trained models

To train the first model we need to:

  1. Choose the model: Weโ€™re using the first model in the fusion_models list we made earlier.

  2. Print the attributes of the model: To check itโ€™s been initialised correctly.

  3. Create the datamodule: This is done with the prepare_fusion_data() function. This function takes the initialised model and some parameters as inputs. It returns the datamodule.

  4. Train and test the model: This is done with the train_and_save_models() function. This function takes the datamodule and the fusion model as inputs, as well as optional training modifications. It returns the trained model.

  5. Add the trained model to the ``all_trained_models`` dictionary: This is so we can compare the results of the two models later.

fusion_model = fusion_models[0]

print("Method name:", fusion_model.method_name)
print("Modality type:", fusion_model.modality_type)
print("Fusion type:", fusion_model.fusion_type)

# Create the data module
dm = prepare_fusion_data(prediction_task=prediction_task,
                         fusion_model=fusion_model,
                         data_paths=data_paths,
                         output_paths=output_paths,
                         batch_size=batch_size,
                         test_size=test_size)

# train and test
model_1_list = train_and_save_models(
    data_module=dm,
    fusion_model=fusion_model,
    enable_checkpointing=False,  # False for the example notebooks
    show_loss_plot=True,
)

# Add trained model to dictionary
all_trained_models[fusion_model.__name__] = model_1_list
Loss Curves for Tabular1Unimodal
Method name: Tabular1 uni-modal
Modality type: tabular1
Fusion type: unimodal

Training: |          | 0/? [00:00<?, ?it/s]
Training:   0%|          | 0/8 [00:00<?, ?it/s]
Epoch 0:   0%|          | 0/8 [00:00<?, ?it/s]
Epoch 0:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 79.15it/s]
Epoch 0:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 77.35it/s, v_num=odal]
Epoch 0:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 91.36it/s, v_num=odal]
Epoch 0:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 90.31it/s, v_num=odal]
Epoch 0:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 99.51it/s, v_num=odal]
Epoch 0:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 98.67it/s, v_num=odal]
Epoch 0:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 103.64it/s, v_num=odal]
Epoch 0:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 102.96it/s, v_num=odal]
Epoch 0:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 105.50it/s, v_num=odal]
Epoch 0:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 104.94it/s, v_num=odal]
Epoch 0:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 107.89it/s, v_num=odal]
Epoch 0:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 107.40it/s, v_num=odal]
Epoch 0:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 109.16it/s, v_num=odal]
Epoch 0:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 108.73it/s, v_num=odal]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 111.47it/s, v_num=odal]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 111.09it/s, v_num=odal]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 93.65it/s, v_num=odal, val_loss=9.710]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 92.90it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 0:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 120.54it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 116.76it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.93it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 117.03it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.19it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.91it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.83it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.84it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.99it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.14it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.03it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.32it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.20it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.64it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.65it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.15it/s, v_num=odal, val_loss=9.710, train_loss=18.40]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.72it/s, v_num=odal, val_loss=7.140, train_loss=18.40]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.75it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 1:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.87it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.00it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.85it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.88it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 122.02it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.71it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.41it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.39it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.38it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.58it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.24it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.57it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.24it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.40it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 120.81it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 120.33it/s, v_num=odal, val_loss=7.140, train_loss=6.340]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.82it/s, v_num=odal, val_loss=6.370, train_loss=6.340]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.89it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 2:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.81it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 115.09it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 116.66it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 114.84it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 114.74it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 113.54it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 115.83it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 114.92it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 115.65it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 114.92it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 115.99it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 115.29it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 116.96it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 116.44it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 118.28it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 117.81it/s, v_num=odal, val_loss=6.370, train_loss=5.130]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.21it/s, v_num=odal, val_loss=5.900, train_loss=5.130]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 99.41it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 3:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 119.15it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 115.47it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.13it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 117.22it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.02it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.54it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 118.79it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 117.85it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.40it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 118.64it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 118.90it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 118.26it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 119.48it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 118.93it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 120.94it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 120.46it/s, v_num=odal, val_loss=5.900, train_loss=4.560]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.42it/s, v_num=odal, val_loss=5.270, train_loss=4.560]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.45it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 4:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.66it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.71it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.60it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.60it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.39it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.08it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.99it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.01it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.39it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.59it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.70it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.03it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.82it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.25it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.94it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.43it/s, v_num=odal, val_loss=5.270, train_loss=3.740]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.10it/s, v_num=odal, val_loss=5.790, train_loss=3.740]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.21it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 5:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.84it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 114.22it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.04it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.13it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.96it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.66it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.44it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.45it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.72it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.92it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.82it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.17it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.10it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.54it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.44it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.95it/s, v_num=odal, val_loss=5.790, train_loss=3.330]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.18it/s, v_num=odal, val_loss=5.000, train_loss=3.330]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.26it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 6:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 123.46it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.74it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.87it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.68it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.63it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.32it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.06it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.07it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.50it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.70it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.88it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.21it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.16it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.50it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.93it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.44it/s, v_num=odal, val_loss=5.000, train_loss=2.910]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.47it/s, v_num=odal, val_loss=4.830, train_loss=2.910]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.62it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 7:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 120.86it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 116.91it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.02it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 117.99it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.43it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.91it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.13it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 118.16it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 118.48it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 117.71it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 118.22it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 117.60it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 118.86it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 118.32it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 120.36it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 119.87it/s, v_num=odal, val_loss=4.830, train_loss=2.300]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.78it/s, v_num=odal, val_loss=4.480, train_loss=2.300]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.87it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 8:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.80it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.89it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.25it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.25it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.38it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.09it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.38it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.35it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.87it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.03it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.20it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.48it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.49it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.87it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.82it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.31it/s, v_num=odal, val_loss=4.480, train_loss=2.000]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.16it/s, v_num=odal, val_loss=5.180, train_loss=2.000]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.21it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 9:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.09it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.24it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.91it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.93it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 122.21it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.88it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.52it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.52it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.78it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.98it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 122.04it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.38it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 122.24it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.66it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.22it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.71it/s, v_num=odal, val_loss=5.180, train_loss=1.750]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.84it/s, v_num=odal, val_loss=3.940, train_loss=1.750]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.90it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 10:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 123.14it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 119.18it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 123.15it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.15it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.18it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.70it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.63it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.54it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.89it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.09it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 122.11it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.45it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 122.25it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.68it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.56it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.98it/s, v_num=odal, val_loss=3.940, train_loss=1.730]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.65it/s, v_num=odal, val_loss=4.940, train_loss=1.730]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.67it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 11:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.52it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.63it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.62it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.60it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 122.77it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.43it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 123.08it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.07it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 123.17it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.36it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.92it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.26it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 122.02it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.44it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.24it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.74it/s, v_num=odal, val_loss=4.940, train_loss=1.450]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 105.01it/s, v_num=odal, val_loss=4.240, train_loss=1.450]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.07it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 12:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.85it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.94it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.87it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.85it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.83it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.42it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.34it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.36it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.71it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.91it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 122.03it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.36it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 122.20it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.63it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.45it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.90it/s, v_num=odal, val_loss=4.240, train_loss=1.570]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.52it/s, v_num=odal, val_loss=4.100, train_loss=1.570]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.52it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 13:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.35it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.45it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.49it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.48it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 122.53it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.17it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.17it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.17it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.14it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.33it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.42it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.76it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.66it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.10it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.94it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.43it/s, v_num=odal, val_loss=4.100, train_loss=1.210]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.76it/s, v_num=odal, val_loss=3.800, train_loss=1.210]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.84it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 14:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.60it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.95it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.52it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.35it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.38it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.08it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.03it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.05it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.40it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.61it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.65it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.98it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.78it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.21it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.03it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.53it/s, v_num=odal, val_loss=3.800, train_loss=0.935]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.18it/s, v_num=odal, val_loss=4.010, train_loss=0.935]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.26it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 15:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.98it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.10it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.06it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.09it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 122.30it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.97it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.58it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.57it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.62it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.81it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.66it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.00it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.87it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.30it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.02it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.52it/s, v_num=odal, val_loss=4.010, train_loss=0.845]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.80it/s, v_num=odal, val_loss=4.650, train_loss=0.845]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.84it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 16:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 120.67it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 116.88it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.59it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.65it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.85it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.60it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.99it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.02it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.65it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.87it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.29it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 119.64it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.15it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 119.59it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.07it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 120.58it/s, v_num=odal, val_loss=4.650, train_loss=1.160]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.37it/s, v_num=odal, val_loss=3.730, train_loss=1.160]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.47it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 17:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.78it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.89it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.99it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.02it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 122.08it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.73it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.26it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.26it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.34it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.54it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.75it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.09it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.79it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.11it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.89it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.40it/s, v_num=odal, val_loss=3.730, train_loss=1.040]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.82it/s, v_num=odal, val_loss=4.530, train_loss=1.040]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.88it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 18:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.72it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.82it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.99it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.02it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.99it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.73it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.66it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 118.70it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.24it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.45it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.72it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.07it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.04it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.48it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.33it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.83it/s, v_num=odal, val_loss=4.530, train_loss=0.938]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.67it/s, v_num=odal, val_loss=3.690, train_loss=0.938]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.74it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 19:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.53it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.67it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.62it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.65it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.94it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.62it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.23it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.23it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.43it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.63it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.56it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.91it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.71it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.14it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.03it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.52it/s, v_num=odal, val_loss=3.690, train_loss=0.831]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.85it/s, v_num=odal, val_loss=4.580, train_loss=0.831]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.90it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 20:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.42it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.55it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.42it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.47it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.38it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.11it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.06it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.09it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.41it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 118.64it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 119.22it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 118.58it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 118.97it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 118.41it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 119.94it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 119.46it/s, v_num=odal, val_loss=4.580, train_loss=0.837]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.20it/s, v_num=odal, val_loss=3.950, train_loss=0.837]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.30it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 21:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 116.56it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 112.86it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 116.59it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 114.73it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.01it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 115.77it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 115.71it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 114.81it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.92it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.13it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 115.35it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 114.74it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 115.03it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 114.33it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 115.92it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 115.40it/s, v_num=odal, val_loss=3.950, train_loss=0.770]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 98.48it/s, v_num=odal, val_loss=3.860, train_loss=0.770]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 97.62it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 22:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 110.77it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 107.50it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 113.90it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 112.16it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 113.06it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 111.90it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 114.90it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 114.01it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.30it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 115.57it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 117.32it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 116.70it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 118.09it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 117.56it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 119.80it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 119.32it/s, v_num=odal, val_loss=3.860, train_loss=0.675]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.70it/s, v_num=odal, val_loss=4.360, train_loss=0.675]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.81it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 23:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 120.04it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 116.27it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.10it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.15it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.64it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.33it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.03it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.04it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.14it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.33it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.36it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.69it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.65it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.07it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.93it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.43it/s, v_num=odal, val_loss=4.360, train_loss=0.537]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.81it/s, v_num=odal, val_loss=3.760, train_loss=0.537]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.91it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 24:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.71it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.71it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.38it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.44it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.24it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 115.98it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 117.43it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 116.50it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 117.80it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 117.04it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 118.07it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 117.44it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 118.05it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 117.43it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 119.19it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 118.69it/s, v_num=odal, val_loss=3.760, train_loss=0.532]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.34it/s, v_num=odal, val_loss=4.400, train_loss=0.532]
Epoch 25: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.41it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 25:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.51it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.59it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.87it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.86it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 123.11it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.74it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 123.28it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.26it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 123.10it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.28it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.84it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.17it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 122.03it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.45it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.27it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.76it/s, v_num=odal, val_loss=4.400, train_loss=0.634]
Epoch 26: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.75it/s, v_num=odal, val_loss=4.240, train_loss=0.634]
Epoch 26: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.80it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 26:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.50it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.40it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.27it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.15it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.49it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.19it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.21it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.20it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.23it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.41it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.27it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 119.59it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.48it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 119.91it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.78it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.28it/s, v_num=odal, val_loss=4.240, train_loss=0.776]
Epoch 27: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.91it/s, v_num=odal, val_loss=3.910, train_loss=0.776]
Epoch 27: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.99it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 27:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 112.30it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 108.95it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 114.01it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 112.23it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 115.33it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 114.13it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 116.08it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 115.17it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.53it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 115.80it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 115.73it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 115.13it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 116.15it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 115.62it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 117.91it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 117.44it/s, v_num=odal, val_loss=3.910, train_loss=0.891]
Epoch 28: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.21it/s, v_num=odal, val_loss=3.550, train_loss=0.891]
Epoch 28: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 99.34it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 28:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.38it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.48it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.83it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 116.92it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 116.64it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 115.39it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 117.20it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 116.27it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 117.00it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.16it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 117.30it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 116.68it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 117.50it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 116.96it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 119.25it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 118.78it/s, v_num=odal, val_loss=3.550, train_loss=0.591]
Epoch 29: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.46it/s, v_num=odal, val_loss=3.700, train_loss=0.591]
Epoch 29: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.52it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 29:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.19it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.29it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.71it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.56it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 122.50it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.13it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.66it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.65it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.58it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.77it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.62it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.95it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.85it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.28it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.17it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.67it/s, v_num=odal, val_loss=3.700, train_loss=0.660]
Epoch 30: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.79it/s, v_num=odal, val_loss=3.570, train_loss=0.660]
Epoch 30: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.84it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 30:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 120.40it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 115.08it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.44it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.49it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.53it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.16it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.13it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 118.17it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.86it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.08it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.30it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 119.65it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.69it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.12it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.99it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.49it/s, v_num=odal, val_loss=3.570, train_loss=0.606]
Epoch 31: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.27it/s, v_num=odal, val_loss=4.610, train_loss=0.606]
Epoch 31: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.34it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 31:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 122.26it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.36it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 122.51it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.50it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 122.42it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.08it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 122.56it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.49it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 122.72it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.76it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.72it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.05it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.88it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.31it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 123.27it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.46it/s, v_num=odal, val_loss=4.610, train_loss=0.548]
Epoch 32: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.50it/s, v_num=odal, val_loss=4.550, train_loss=0.548]
Epoch 32: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.14it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 32:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 120.95it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.07it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.50it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.54it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 119.74it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.46it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.53it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.56it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.07it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.27it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 121.42it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.76it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.57it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.99it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.15it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.65it/s, v_num=odal, val_loss=4.550, train_loss=0.512]
Epoch 33: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.70it/s, v_num=odal, val_loss=3.910, train_loss=0.512]
Epoch 33: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 99.80it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 33:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 120.64it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 116.83it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.21it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.25it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 121.51it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 120.10it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 121.76it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 120.64it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.92it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 121.04it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.88it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.23it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 121.15it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.58it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 122.49it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.99it/s, v_num=odal, val_loss=3.910, train_loss=0.448]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 104.24it/s, v_num=odal, val_loss=3.900, train_loss=0.448]
Epoch 34: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 103.23it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 34:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 120.19it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 116.37it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 120.66it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.73it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.97it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.71it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.90it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 118.93it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.45it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.65it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.78it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.11it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.78it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.20it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.63it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.10it/s, v_num=odal, val_loss=3.900, train_loss=0.434]
Epoch 35: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.95it/s, v_num=odal, val_loss=4.060, train_loss=0.434]
Epoch 35: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.95it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 35:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 114.05it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 110.46it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 114.86it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 113.04it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 115.42it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 113.93it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 115.76it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 114.80it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.01it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 115.24it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 115.47it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 114.84it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 116.09it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 115.54it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 117.83it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 117.36it/s, v_num=odal, val_loss=4.060, train_loss=0.466]
Epoch 36: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.66it/s, v_num=odal, val_loss=4.920, train_loss=0.466]
Epoch 36: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 99.83it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 36:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 121.30it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.37it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 121.42it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 119.43it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.98it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.68it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 119.49it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 118.51it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 120.01it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 119.21it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 120.31it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 119.64it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.57it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 120.00it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.64it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 121.14it/s, v_num=odal, val_loss=4.920, train_loss=0.528]
Epoch 37: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 102.89it/s, v_num=odal, val_loss=4.230, train_loss=0.528]
Epoch 37: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.97it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 37:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.15it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 114.25it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 117.89it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 115.91it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 118.44it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 116.99it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 118.67it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 117.65it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 118.94it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 118.15it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 117.79it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 117.13it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 118.03it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 117.46it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 119.46it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 118.96it/s, v_num=odal, val_loss=4.230, train_loss=0.863]
Epoch 38: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 101.81it/s, v_num=odal, val_loss=4.460, train_loss=0.863]
Epoch 38: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.90it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 38:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 118.29it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 114.37it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.20it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 115.94it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 116.11it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 114.84it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 117.02it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 116.05it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.91it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.04it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 116.81it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 116.13it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 117.04it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 116.48it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 118.54it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 118.03it/s, v_num=odal, val_loss=4.460, train_loss=0.615]
Epoch 39: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.23it/s, v_num=odal, val_loss=3.980, train_loss=0.615]
Epoch 39: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 99.31it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 39:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 115.23it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 111.59it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 116.47it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 114.56it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 117.20it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 115.85it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 117.54it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 116.56it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 117.54it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 116.74it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 116.40it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 115.73it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 116.69it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 116.13it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 118.12it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 117.62it/s, v_num=odal, val_loss=3.980, train_loss=0.662]
Epoch 40: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 100.62it/s, v_num=odal, val_loss=4.540, train_loss=0.662]
Epoch 40: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 99.70it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 40:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 117.85it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 114.09it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 118.73it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 116.80it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 116.98it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 115.68it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 108.39it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 107.07it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 107.83it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 107.12it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 109.25it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 108.64it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 110.18it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 109.65it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 111.71it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 111.23it/s, v_num=odal, val_loss=4.540, train_loss=0.571]
Epoch 41: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 95.00it/s, v_num=odal, val_loss=4.110, train_loss=0.571]
Epoch 41: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 94.13it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 41:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 113.07it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 109.33it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 114.51it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 112.61it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 115.67it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 114.34it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 115.59it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 114.57it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 114.12it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 113.14it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 111.46it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 110.74it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 110.84it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 110.26it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 111.44it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 110.86it/s, v_num=odal, val_loss=4.110, train_loss=0.597]
Epoch 42: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 95.12it/s, v_num=odal, val_loss=3.980, train_loss=0.597]
Epoch 42: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 94.25it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 42:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:   0%|          | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 110.51it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 106.60it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 109.14it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 106.98it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 107.30it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 106.02it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 108.16it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 107.19it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 108.88it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 107.96it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 109.09it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 108.41it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 108.89it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 108.29it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 109.81it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 109.29it/s, v_num=odal, val_loss=3.980, train_loss=0.885]
Epoch 43: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 93.30it/s, v_num=odal, val_loss=3.790, train_loss=0.885]
Epoch 43: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 92.39it/s, v_num=odal, val_loss=3.790, train_loss=0.986]
Epoch 43: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 91.14it/s, v_num=odal, val_loss=3.790, train_loss=0.986]
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
     Validate metric           DataLoader 0
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
         MAE_val            1.4235321283340454
         R2_val             0.5514935255050659
        val_loss             3.789829730987549
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€

5. Plotting the results of the first model ๐Ÿ“Š๏ƒ

Letโ€™s unveil the results of our first modelโ€™s hard work. Weโ€™re using the RealsVsPreds class to plot the results of the first model. This class takes the trained model as an input and returns a plot of the real values vs the predicted values from the final validation data (when using from_final_val_data). If you want to plot the results from the test data, you can use from_new_data instead. See the example notebook on plotting with new data for more detail.

reals_preds_model_1 = RealsVsPreds.from_final_val_data(model_1_list)

plt.show()
Evaluation: Validation Data, Tabular1 uni-modal - Validation R2: 0.551

6. Training the second fusion model ๐Ÿ๏ƒ

Itโ€™s time for our second fusion model to shine! Here we train the second fusion model: ConcatTabularFeatureMaps. Weโ€™re using the same steps as before, but this time weโ€™re using the second model in the fusion_models list.

Choose the model

fusion_model = fusion_models[1]

print("Method name:", fusion_model.method_name)
print("Modality type:", fusion_model.modality_type)
print("Fusion type:", fusion_model.fusion_type)

# Create the data module
dm = prepare_fusion_data(prediction_task=prediction_task,
                         fusion_model=fusion_model,
                         data_paths=data_paths,
                         output_paths=output_paths,
                         batch_size=batch_size,
                         test_size=test_size)

# train and test
model_2_list = train_and_save_models(
    data_module=dm,
    fusion_model=fusion_model,
    enable_checkpointing=False,  # False for the example notebooks
    show_loss_plot=True,
)

# Add trained model to dictionary
all_trained_models[fusion_model.__name__] = model_2_list
Loss Curves for ConcatTabularFeatureMaps
Method name: Concatenating tabular feature maps
Modality type: tabular_tabular
Fusion type: operation

Training: |          | 0/? [00:00<?, ?it/s]
Training:   0%|          | 0/8 [00:00<?, ?it/s]
Epoch 0:   0%|          | 0/8 [00:00<?, ?it/s]
Epoch 0:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 60.86it/s]
Epoch 0:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 59.48it/s, v_num=Maps]
Epoch 0:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 62.31it/s, v_num=Maps]
Epoch 0:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 61.58it/s, v_num=Maps]
Epoch 0:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 65.29it/s, v_num=Maps]
Epoch 0:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 64.78it/s, v_num=Maps]
Epoch 0:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 66.93it/s, v_num=Maps]
Epoch 0:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 66.54it/s, v_num=Maps]
Epoch 0:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 68.18it/s, v_num=Maps]
Epoch 0:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 67.82it/s, v_num=Maps]
Epoch 0:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 68.98it/s, v_num=Maps]
Epoch 0:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 68.69it/s, v_num=Maps]
Epoch 0:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 69.05it/s, v_num=Maps]
Epoch 0:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 68.79it/s, v_num=Maps]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 70.69it/s, v_num=Maps]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 70.45it/s, v_num=Maps]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 60.86it/s, v_num=Maps, val_loss=6.560]
Epoch 0: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 60.45it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 0:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 75.15it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 73.13it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 74.36it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 73.38it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 75.76it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 75.01it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 76.18it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 75.64it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 76.50it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 76.07it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 76.43it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 76.06it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 75.57it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 75.24it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.15it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.87it/s, v_num=Maps, val_loss=6.560, train_loss=15.20]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 65.94it/s, v_num=Maps, val_loss=7.170, train_loss=15.20]
Epoch 1: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 65.45it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 1:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 71.15it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 69.20it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 72.19it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 71.13it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 72.42it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 71.73it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 71.62it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 71.04it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 71.72it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 71.31it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 71.88it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 71.54it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 72.19it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 71.89it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 73.12it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 72.86it/s, v_num=Maps, val_loss=7.170, train_loss=8.550]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 63.72it/s, v_num=Maps, val_loss=6.560, train_loss=8.550]
Epoch 2: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 63.27it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 2:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 69.98it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 68.02it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 69.73it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 68.76it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 69.59it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 68.95it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 70.27it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 69.78it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 70.59it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 70.20it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 70.79it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 70.48it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 71.32it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 71.05it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 72.81it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 72.58it/s, v_num=Maps, val_loss=6.560, train_loss=5.710]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 64.10it/s, v_num=Maps, val_loss=5.270, train_loss=5.710]
Epoch 3: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 63.65it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 3:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 81.14it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 79.08it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 81.49it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 80.43it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 80.27it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 79.50it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 79.79it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 79.22it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 79.57it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 79.11it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 79.42it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 79.04it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 79.54it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 79.23it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 80.67it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 80.40it/s, v_num=Maps, val_loss=5.270, train_loss=5.110]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 69.70it/s, v_num=Maps, val_loss=4.250, train_loss=5.110]
Epoch 4: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 69.20it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 4:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 82.24it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 80.07it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 80.92it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 79.38it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 79.57it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 78.78it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 78.46it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 77.88it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 77.29it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 76.78it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 76.93it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 76.54it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 76.68it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 76.36it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.69it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.41it/s, v_num=Maps, val_loss=4.250, train_loss=4.440]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 67.68it/s, v_num=Maps, val_loss=3.730, train_loss=4.440]
Epoch 5: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 67.19it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 5:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 80.15it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 77.58it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 76.82it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 75.76it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 76.66it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 75.94it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 76.57it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 76.00it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 76.25it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 75.80it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 76.23it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 75.85it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 75.36it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 75.05it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.10it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.82it/s, v_num=Maps, val_loss=3.730, train_loss=3.310]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 66.11it/s, v_num=Maps, val_loss=3.190, train_loss=3.310]
Epoch 6: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 65.63it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 6:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 76.88it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 74.72it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 75.82it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 74.83it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 76.06it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 75.34it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 75.21it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 74.63it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 75.20it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 74.78it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 75.83it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 75.50it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 76.66it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 76.37it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 78.34it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 78.10it/s, v_num=Maps, val_loss=3.190, train_loss=2.490]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 68.75it/s, v_num=Maps, val_loss=3.210, train_loss=2.490]
Epoch 7: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 68.25it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 7:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 80.93it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 79.13it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 83.95it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 83.00it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 85.81it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 85.04it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 86.75it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 86.23it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 87.35it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 86.94it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 87.00it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 86.64it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 87.20it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 86.89it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 88.29it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 88.01it/s, v_num=Maps, val_loss=3.210, train_loss=2.180]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.64it/s, v_num=Maps, val_loss=3.300, train_loss=2.180]
Epoch 8: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.08it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 8:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 88.62it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 86.54it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 89.11it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 88.04it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.98it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.27it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.28it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 87.71it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.41it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 87.97it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.47it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.10it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.61it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.30it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.71it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.43it/s, v_num=Maps, val_loss=3.300, train_loss=1.750]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.93it/s, v_num=Maps, val_loss=2.900, train_loss=1.750]
Epoch 9: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.36it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 9:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 83.18it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 81.28it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 85.27it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 84.25it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 86.70it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 86.01it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 87.45it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 86.92it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 87.87it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 87.43it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 87.18it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 86.81it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 87.19it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 86.88it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 88.52it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 88.25it/s, v_num=Maps, val_loss=2.900, train_loss=1.430]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.05it/s, v_num=Maps, val_loss=3.580, train_loss=1.430]
Epoch 10: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.49it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 10:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 89.74it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 87.60it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 89.31it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 88.23it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.98it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.27it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.34it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 87.80it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.53it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.09it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.49it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.11it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.73it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.41it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.82it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.54it/s, v_num=Maps, val_loss=3.580, train_loss=1.060]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.29it/s, v_num=Maps, val_loss=3.510, train_loss=1.060]
Epoch 11: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.68it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 11:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 57.97it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 57.02it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 69.69it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 69.01it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 75.06it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 74.54it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 78.10it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 77.67it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 79.57it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 79.21it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 81.03it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 80.72it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 81.62it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 81.35it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 82.73it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 82.50it/s, v_num=Maps, val_loss=3.510, train_loss=0.945]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 72.07it/s, v_num=Maps, val_loss=3.240, train_loss=0.945]
Epoch 12: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 71.57it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 12:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 87.29it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 85.26it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 85.19it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 83.73it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 85.27it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 84.60it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 85.65it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 85.14it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 85.48it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 85.06it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 85.81it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 85.46it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 84.98it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 84.69it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 86.22it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 85.96it/s, v_num=Maps, val_loss=3.240, train_loss=0.883]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 74.58it/s, v_num=Maps, val_loss=3.570, train_loss=0.883]
Epoch 13: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 74.05it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 13:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 82.30it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 80.32it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 84.20it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 83.18it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 85.48it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 84.80it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 85.16it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 84.64it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 85.52it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 85.09it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 85.87it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 85.51it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 86.12it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 85.81it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 87.38it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 87.11it/s, v_num=Maps, val_loss=3.570, train_loss=0.758]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.84it/s, v_num=Maps, val_loss=3.510, train_loss=0.758]
Epoch 14: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.28it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 14:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 84.20it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 82.30it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 86.19it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 85.19it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 86.52it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 85.84it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 86.59it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 86.07it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 86.38it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 85.95it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 85.23it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 84.88it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 85.18it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 84.88it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 86.69it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 86.38it/s, v_num=Maps, val_loss=3.510, train_loss=0.717]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.00it/s, v_num=Maps, val_loss=3.150, train_loss=0.717]
Epoch 15: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 74.47it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 15:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 89.39it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 87.27it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 89.72it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 88.65it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 88.32it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.62it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.77it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.24it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 89.13it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.70it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 89.38it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 89.01it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 89.46it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 89.14it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 90.53it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 90.25it/s, v_num=Maps, val_loss=3.150, train_loss=0.790]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 78.00it/s, v_num=Maps, val_loss=3.060, train_loss=0.790]
Epoch 16: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.41it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 16:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 88.02it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 85.89it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 88.16it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 87.10it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 88.26it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.53it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.60it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.05it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.16it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 87.73it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 87.16it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 86.70it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 86.70it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 86.40it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 87.80it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 87.53it/s, v_num=Maps, val_loss=3.060, train_loss=0.672]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.96it/s, v_num=Maps, val_loss=3.130, train_loss=0.672]
Epoch 17: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.42it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 17:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 86.94it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 84.89it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 83.48it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 82.51it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 84.64it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 83.98it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 85.25it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 84.73it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 85.61it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 85.20it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 85.70it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 85.36it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 85.30it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 85.00it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 86.49it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 86.23it/s, v_num=Maps, val_loss=3.130, train_loss=0.648]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 74.94it/s, v_num=Maps, val_loss=3.020, train_loss=0.648]
Epoch 18: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 74.40it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 18:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 89.60it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 87.47it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 89.63it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 88.53it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 89.66it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 88.92it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.74it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.20it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 89.10it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.66it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.99it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.62it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 89.07it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.75it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 90.22it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.92it/s, v_num=Maps, val_loss=3.020, train_loss=0.671]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.98it/s, v_num=Maps, val_loss=3.110, train_loss=0.671]
Epoch 19: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.40it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 19:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 84.78it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 82.78it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 86.66it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 85.63it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.54it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 86.85it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.06it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 87.53it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.42it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 87.99it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 87.92it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 87.53it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.11it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 87.80it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.35it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.07it/s, v_num=Maps, val_loss=3.110, train_loss=0.525]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.52it/s, v_num=Maps, val_loss=3.180, train_loss=0.525]
Epoch 20: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.95it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 20:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 90.45it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 88.07it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 90.52it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 89.42it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 88.22it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.51it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.05it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 87.52it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.54it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.11it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.84it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.48it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 89.05it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.74it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 90.21it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.93it/s, v_num=Maps, val_loss=3.180, train_loss=0.665]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.39it/s, v_num=Maps, val_loss=3.030, train_loss=0.665]
Epoch 21: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.81it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 21:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 87.63it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 85.58it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 88.05it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 86.99it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 88.68it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 87.86it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 89.10it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.57it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.45it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.01it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.62it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.26it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.85it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.54it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 90.11it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 89.83it/s, v_num=Maps, val_loss=3.030, train_loss=0.633]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 78.03it/s, v_num=Maps, val_loss=3.060, train_loss=0.633]
Epoch 22: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.46it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 22:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 88.21it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 85.94it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 84.99it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 83.96it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 85.95it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 85.25it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 86.53it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 85.96it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 87.30it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 86.85it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 87.68it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 87.32it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 87.37it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 87.06it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 88.64it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 88.37it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.96it/s, v_num=Maps, val_loss=3.060, train_loss=0.681]
Epoch 23: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.39it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 23:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:   0%|          | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 89.53it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  12%|โ–ˆโ–Ž        | 1/8 [00:00<00:00, 87.41it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 89.83it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  25%|โ–ˆโ–ˆโ–Œ       | 2/8 [00:00<00:00, 88.75it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 89.36it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  38%|โ–ˆโ–ˆโ–ˆโ–Š      | 3/8 [00:00<00:00, 88.64it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.60it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  50%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ     | 4/8 [00:00<00:00, 88.06it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.83it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  62%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Ž   | 5/8 [00:00<00:00, 88.35it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 89.02it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  75%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Œ  | 6/8 [00:00<00:00, 88.64it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 89.14it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24:  88%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–Š | 7/8 [00:00<00:00, 88.83it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 90.32it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 90.04it/s, v_num=Maps, val_loss=3.060, train_loss=0.524]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 77.46it/s, v_num=Maps, val_loss=3.360, train_loss=0.524]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 76.86it/s, v_num=Maps, val_loss=3.360, train_loss=0.576]
Epoch 24: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 8/8 [00:00<00:00, 75.58it/s, v_num=Maps, val_loss=3.360, train_loss=0.576]
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
     Validate metric           DataLoader 0
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
         MAE_val            1.3299411535263062
         R2_val             0.5474092364311218
        val_loss            3.3598976135253906
โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€

7. Plotting the results of the second model ๐Ÿ“Š๏ƒ

reals_preds_model_2 = RealsVsPreds.from_final_val_data(model_2_list)

plt.show()
Evaluation: Validation Data, Concatenating tabular feature maps - Validation R2: 0.547

8. Comparing the results of the two models ๐Ÿ“ˆ๏ƒ

Let the ultimate showdown begin! Weโ€™re comparing the results of our two models. Weโ€™re using the ModelComparison class to compare the results of the two models. This class takes the trained models as an input and returns a plot of the results of the two models and a Pandas DataFrame of the metrics of the two models.

comparison_plot, metrics_dataframe = ModelComparison.from_final_val_data(
    all_trained_models
)

plt.show()
Model Performance Comparison, R2, MAE

9. Saving the metrics of the two models ๐Ÿ’พ๏ƒ

Time to archive our modelsโ€™ achievements. Weโ€™re using the ModelComparison class to save the metrics of the two models.

metrics_dataframe
R2 MAE
Method
Tabular1 uni-modal 0.551494 1.423532
Concatenating tabular feature maps 0.547409 1.329941


Total running time of the script: (0 minutes 7.844 seconds)

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