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Regression: Comparing Two Tabular Models Trained on Simulated Data๏
๐ Welcome to this tutorial on training and comparing two fusion models on a regression task using simulated multimodal tabular data! ๐
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 halfโs 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.
๐ Key Features:
๐ฅ Importing models based on name.
๐งช Training and testing models with train/test protocol.
๐พ Saving trained models to a dictionary for later analysis.
๐ Plotting the results of a single model.
๐ Plotting the results of multiple models as a bar chart.
๐พ 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 ๐๏
Letโs 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.
Weโre 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 ๐ฏ๏
Now, letโs 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 eitherregression
,binary
, orclassification
.
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 canโt bek=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 unlessprediction_task
ismulticlass
.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 ๐ฎ๏
Weโre 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 ๐๏
Here we train the first fusion model. Weโre 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 weโll 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:
Choose the model: Weโre using the first model in the
fusion_models
list we made earlier.Print the attributes of the model: To check itโs been initialised correctly.
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.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.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
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%|โโ | 1/8 [00:00<00:00, 78.91it/s]
Epoch 0: 12%|โโ | 1/8 [00:00<00:00, 76.85it/s, v_num=odal]
Epoch 0: 25%|โโโ | 2/8 [00:00<00:00, 92.70it/s, v_num=odal]
Epoch 0: 25%|โโโ | 2/8 [00:00<00:00, 91.54it/s, v_num=odal]
Epoch 0: 38%|โโโโ | 3/8 [00:00<00:00, 99.35it/s, v_num=odal]
Epoch 0: 38%|โโโโ | 3/8 [00:00<00:00, 98.49it/s, v_num=odal]
Epoch 0: 50%|โโโโโ | 4/8 [00:00<00:00, 102.80it/s, v_num=odal]
Epoch 0: 50%|โโโโโ | 4/8 [00:00<00:00, 102.11it/s, v_num=odal]
Epoch 0: 62%|โโโโโโโ | 5/8 [00:00<00:00, 104.37it/s, v_num=odal]
Epoch 0: 62%|โโโโโโโ | 5/8 [00:00<00:00, 103.74it/s, v_num=odal]
Epoch 0: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 105.79it/s, v_num=odal]
Epoch 0: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 105.26it/s, v_num=odal]
Epoch 0: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 105.45it/s, v_num=odal]
Epoch 0: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 104.93it/s, v_num=odal]
Epoch 0: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 106.98it/s, v_num=odal]
Epoch 0: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 106.55it/s, v_num=odal]
Epoch 0: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 89.83it/s, v_num=odal, val_loss=10.10]
Epoch 0: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 89.06it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 0: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 12%|โโ | 1/8 [00:00<00:00, 112.90it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 12%|โโ | 1/8 [00:00<00:00, 109.35it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 25%|โโโ | 2/8 [00:00<00:00, 112.77it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 25%|โโโ | 2/8 [00:00<00:00, 111.01it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 38%|โโโโ | 3/8 [00:00<00:00, 114.62it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 38%|โโโโ | 3/8 [00:00<00:00, 113.39it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 50%|โโโโโ | 4/8 [00:00<00:00, 115.56it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 50%|โโโโโ | 4/8 [00:00<00:00, 114.62it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.32it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 62%|โโโโโโโ | 5/8 [00:00<00:00, 114.47it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.44it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 113.68it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 114.21it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 113.64it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 114.47it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 113.99it/s, v_num=odal, val_loss=10.10, train_loss=15.40]
Epoch 1: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 97.90it/s, v_num=odal, val_loss=7.280, train_loss=15.40]
Epoch 1: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 97.02it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 1: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 12%|โโ | 1/8 [00:00<00:00, 116.57it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 12%|โโ | 1/8 [00:00<00:00, 112.85it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 25%|โโโ | 2/8 [00:00<00:00, 117.11it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 25%|โโโ | 2/8 [00:00<00:00, 115.21it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 38%|โโโโ | 3/8 [00:00<00:00, 117.32it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 38%|โโโโ | 3/8 [00:00<00:00, 116.04it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 50%|โโโโโ | 4/8 [00:00<00:00, 117.50it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 50%|โโโโโ | 4/8 [00:00<00:00, 116.54it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.77it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.00it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.98it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.34it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.30it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.75it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.54it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.04it/s, v_num=odal, val_loss=7.280, train_loss=8.730]
Epoch 2: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.09it/s, v_num=odal, val_loss=5.780, train_loss=8.730]
Epoch 2: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.17it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 2: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 12%|โโ | 1/8 [00:00<00:00, 117.23it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 12%|โโ | 1/8 [00:00<00:00, 113.41it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 25%|โโโ | 2/8 [00:00<00:00, 117.58it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 25%|โโโ | 2/8 [00:00<00:00, 115.67it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 38%|โโโโ | 3/8 [00:00<00:00, 114.24it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 38%|โโโโ | 3/8 [00:00<00:00, 113.06it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 50%|โโโโโ | 4/8 [00:00<00:00, 115.27it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 50%|โโโโโ | 4/8 [00:00<00:00, 114.34it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.94it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.20it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.51it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.88it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.90it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.36it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.31it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.77it/s, v_num=odal, val_loss=5.780, train_loss=6.230]
Epoch 3: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.65it/s, v_num=odal, val_loss=5.210, train_loss=6.230]
Epoch 3: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.69it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 3: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 12%|โโ | 1/8 [00:00<00:00, 117.63it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 12%|โโ | 1/8 [00:00<00:00, 113.81it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 25%|โโโ | 2/8 [00:00<00:00, 115.70it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 25%|โโโ | 2/8 [00:00<00:00, 113.81it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 38%|โโโโ | 3/8 [00:00<00:00, 115.06it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 38%|โโโโ | 3/8 [00:00<00:00, 113.81it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 50%|โโโโโ | 4/8 [00:00<00:00, 115.96it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 50%|โโโโโ | 4/8 [00:00<00:00, 115.02it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.63it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.88it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.30it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.67it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.81it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.22it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.09it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.60it/s, v_num=odal, val_loss=5.210, train_loss=4.710]
Epoch 4: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.74it/s, v_num=odal, val_loss=5.310, train_loss=4.710]
Epoch 4: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.81it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 4: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 12%|โโ | 1/8 [00:00<00:00, 114.71it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 12%|โโ | 1/8 [00:00<00:00, 110.90it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 25%|โโโ | 2/8 [00:00<00:00, 116.11it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 25%|โโโ | 2/8 [00:00<00:00, 114.25it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 38%|โโโโ | 3/8 [00:00<00:00, 114.35it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 38%|โโโโ | 3/8 [00:00<00:00, 113.14it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 50%|โโโโโ | 4/8 [00:00<00:00, 114.94it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 50%|โโโโโ | 4/8 [00:00<00:00, 114.03it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.32it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 62%|โโโโโโโ | 5/8 [00:00<00:00, 114.59it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.10it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.48it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.63it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.09it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.07it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.58it/s, v_num=odal, val_loss=5.310, train_loss=4.640]
Epoch 5: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.06it/s, v_num=odal, val_loss=4.720, train_loss=4.640]
Epoch 5: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.15it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 5: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 12%|โโ | 1/8 [00:00<00:00, 117.57it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 12%|โโ | 1/8 [00:00<00:00, 113.76it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 25%|โโโ | 2/8 [00:00<00:00, 118.46it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 25%|โโโ | 2/8 [00:00<00:00, 116.52it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 38%|โโโโ | 3/8 [00:00<00:00, 118.24it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 38%|โโโโ | 3/8 [00:00<00:00, 116.94it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 50%|โโโโโ | 4/8 [00:00<00:00, 118.37it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 50%|โโโโโ | 4/8 [00:00<00:00, 117.39it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.51it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.72it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.80it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.15it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.09it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.53it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.35it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.82it/s, v_num=odal, val_loss=4.720, train_loss=4.040]
Epoch 6: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.48it/s, v_num=odal, val_loss=4.290, train_loss=4.040]
Epoch 6: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.49it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 6: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 12%|โโ | 1/8 [00:00<00:00, 118.93it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 12%|โโ | 1/8 [00:00<00:00, 115.05it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 25%|โโโ | 2/8 [00:00<00:00, 119.42it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 25%|โโโ | 2/8 [00:00<00:00, 117.48it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 38%|โโโโ | 3/8 [00:00<00:00, 117.74it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 38%|โโโโ | 3/8 [00:00<00:00, 116.45it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 50%|โโโโโ | 4/8 [00:00<00:00, 118.28it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 50%|โโโโโ | 4/8 [00:00<00:00, 117.30it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.48it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.64it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.62it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.93it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.37it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.75it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.88it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.34it/s, v_num=odal, val_loss=4.290, train_loss=3.900]
Epoch 7: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.38it/s, v_num=odal, val_loss=3.870, train_loss=3.900]
Epoch 7: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.45it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 7: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 12%|โโ | 1/8 [00:00<00:00, 116.21it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 12%|โโ | 1/8 [00:00<00:00, 112.00it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 25%|โโโ | 2/8 [00:00<00:00, 116.43it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 25%|โโโ | 2/8 [00:00<00:00, 114.54it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 38%|โโโโ | 3/8 [00:00<00:00, 116.97it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 38%|โโโโ | 3/8 [00:00<00:00, 115.68it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 50%|โโโโโ | 4/8 [00:00<00:00, 117.41it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 50%|โโโโโ | 4/8 [00:00<00:00, 116.45it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.72it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.85it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.31it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.64it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.51it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 114.90it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.84it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.33it/s, v_num=odal, val_loss=3.870, train_loss=2.800]
Epoch 8: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.65it/s, v_num=odal, val_loss=3.600, train_loss=2.800]
Epoch 8: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.67it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 8: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 12%|โโ | 1/8 [00:00<00:00, 115.35it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 12%|โโ | 1/8 [00:00<00:00, 111.46it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 25%|โโโ | 2/8 [00:00<00:00, 109.11it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 25%|โโโ | 2/8 [00:00<00:00, 107.23it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 38%|โโโโ | 3/8 [00:00<00:00, 108.73it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 38%|โโโโ | 3/8 [00:00<00:00, 106.95it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 50%|โโโโโ | 4/8 [00:00<00:00, 110.07it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 50%|โโโโโ | 4/8 [00:00<00:00, 109.18it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 62%|โโโโโโโ | 5/8 [00:00<00:00, 110.83it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 62%|โโโโโโโ | 5/8 [00:00<00:00, 110.09it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 111.38it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 110.76it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 111.81it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 111.25it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 113.10it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 112.64it/s, v_num=odal, val_loss=3.600, train_loss=2.240]
Epoch 9: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 96.40it/s, v_num=odal, val_loss=3.290, train_loss=2.240]
Epoch 9: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 95.53it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 9: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 12%|โโ | 1/8 [00:00<00:00, 116.66it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 12%|โโ | 1/8 [00:00<00:00, 112.93it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 25%|โโโ | 2/8 [00:00<00:00, 117.40it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 25%|โโโ | 2/8 [00:00<00:00, 115.32it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 38%|โโโโ | 3/8 [00:00<00:00, 117.50it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 38%|โโโโ | 3/8 [00:00<00:00, 116.08it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 50%|โโโโโ | 4/8 [00:00<00:00, 115.76it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 50%|โโโโโ | 4/8 [00:00<00:00, 114.77it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.39it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 62%|โโโโโโโ | 5/8 [00:00<00:00, 114.62it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.43it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 113.77it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 114.57it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 113.97it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 115.67it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 115.15it/s, v_num=odal, val_loss=3.290, train_loss=2.270]
Epoch 10: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.69it/s, v_num=odal, val_loss=3.700, train_loss=2.270]
Epoch 10: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 97.72it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 10: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 12%|โโ | 1/8 [00:00<00:00, 116.30it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 12%|โโ | 1/8 [00:00<00:00, 112.56it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 25%|โโโ | 2/8 [00:00<00:00, 117.31it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 25%|โโโ | 2/8 [00:00<00:00, 115.42it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 38%|โโโโ | 3/8 [00:00<00:00, 116.04it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 38%|โโโโ | 3/8 [00:00<00:00, 114.78it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 50%|โโโโโ | 4/8 [00:00<00:00, 117.05it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 50%|โโโโโ | 4/8 [00:00<00:00, 116.06it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.63it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.84it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.63it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.98it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.66it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.11it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.58it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.09it/s, v_num=odal, val_loss=3.700, train_loss=1.880]
Epoch 11: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.46it/s, v_num=odal, val_loss=3.540, train_loss=1.880]
Epoch 11: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.53it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 11: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 12%|โโ | 1/8 [00:00<00:00, 111.01it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 12%|โโ | 1/8 [00:00<00:00, 107.57it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 25%|โโโ | 2/8 [00:00<00:00, 114.43it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 25%|โโโ | 2/8 [00:00<00:00, 112.62it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 38%|โโโโ | 3/8 [00:00<00:00, 115.91it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 38%|โโโโ | 3/8 [00:00<00:00, 114.65it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 50%|โโโโโ | 4/8 [00:00<00:00, 116.83it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 50%|โโโโโ | 4/8 [00:00<00:00, 115.87it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.85it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.03it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.37it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.65it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.33it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 114.76it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.56it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.06it/s, v_num=odal, val_loss=3.540, train_loss=1.890]
Epoch 12: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.80it/s, v_num=odal, val_loss=3.440, train_loss=1.890]
Epoch 12: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 97.87it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 12: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 12%|โโ | 1/8 [00:00<00:00, 113.90it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 12%|โโ | 1/8 [00:00<00:00, 110.03it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 25%|โโโ | 2/8 [00:00<00:00, 114.73it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 25%|โโโ | 2/8 [00:00<00:00, 112.83it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 38%|โโโโ | 3/8 [00:00<00:00, 113.55it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 38%|โโโโ | 3/8 [00:00<00:00, 112.33it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 50%|โโโโโ | 4/8 [00:00<00:00, 114.51it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 50%|โโโโโ | 4/8 [00:00<00:00, 113.57it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.20it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 62%|โโโโโโโ | 5/8 [00:00<00:00, 114.43it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.68it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.03it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.16it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.61it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.56it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.06it/s, v_num=odal, val_loss=3.440, train_loss=1.820]
Epoch 13: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.78it/s, v_num=odal, val_loss=3.750, train_loss=1.820]
Epoch 13: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.88it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 13: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 12%|โโ | 1/8 [00:00<00:00, 116.75it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 12%|โโ | 1/8 [00:00<00:00, 113.03it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 25%|โโโ | 2/8 [00:00<00:00, 117.66it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 25%|โโโ | 2/8 [00:00<00:00, 115.74it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 38%|โโโโ | 3/8 [00:00<00:00, 118.15it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 38%|โโโโ | 3/8 [00:00<00:00, 116.83it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 50%|โโโโโ | 4/8 [00:00<00:00, 118.38it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 50%|โโโโโ | 4/8 [00:00<00:00, 117.38it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.48it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.69it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.71it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.05it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.05it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.45it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.39it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.89it/s, v_num=odal, val_loss=3.750, train_loss=1.410]
Epoch 14: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.86it/s, v_num=odal, val_loss=3.440, train_loss=1.410]
Epoch 14: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.89it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 14: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 12%|โโ | 1/8 [00:00<00:00, 119.25it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 12%|โโ | 1/8 [00:00<00:00, 115.36it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 25%|โโโ | 2/8 [00:00<00:00, 119.32it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 25%|โโโ | 2/8 [00:00<00:00, 117.37it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 38%|โโโโ | 3/8 [00:00<00:00, 117.48it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 38%|โโโโ | 3/8 [00:00<00:00, 116.18it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 50%|โโโโโ | 4/8 [00:00<00:00, 118.21it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 50%|โโโโโ | 4/8 [00:00<00:00, 117.24it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.57it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.78it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.90it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.08it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.43it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.87it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.78it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.23it/s, v_num=odal, val_loss=3.440, train_loss=1.510]
Epoch 15: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.57it/s, v_num=odal, val_loss=3.480, train_loss=1.510]
Epoch 15: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.63it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 15: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 12%|โโ | 1/8 [00:00<00:00, 115.18it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 12%|โโ | 1/8 [00:00<00:00, 111.50it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 25%|โโโ | 2/8 [00:00<00:00, 115.64it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 25%|โโโ | 2/8 [00:00<00:00, 113.77it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 38%|โโโโ | 3/8 [00:00<00:00, 115.70it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 38%|โโโโ | 3/8 [00:00<00:00, 114.37it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 50%|โโโโโ | 4/8 [00:00<00:00, 115.59it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 50%|โโโโโ | 4/8 [00:00<00:00, 114.61it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.59it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 62%|โโโโโโโ | 5/8 [00:00<00:00, 114.80it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.84it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.20it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.52it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 114.98it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.72it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.24it/s, v_num=odal, val_loss=3.480, train_loss=1.500]
Epoch 16: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.24it/s, v_num=odal, val_loss=3.040, train_loss=1.500]
Epoch 16: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.34it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 16: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 12%|โโ | 1/8 [00:00<00:00, 116.64it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 12%|โโ | 1/8 [00:00<00:00, 112.91it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 25%|โโโ | 2/8 [00:00<00:00, 118.31it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 25%|โโโ | 2/8 [00:00<00:00, 116.36it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 38%|โโโโ | 3/8 [00:00<00:00, 117.12it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 38%|โโโโ | 3/8 [00:00<00:00, 115.84it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 50%|โโโโโ | 4/8 [00:00<00:00, 117.70it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 50%|โโโโโ | 4/8 [00:00<00:00, 116.72it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.24it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.46it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.94it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.28it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.41it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.82it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.73it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.23it/s, v_num=odal, val_loss=3.040, train_loss=1.390]
Epoch 17: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.23it/s, v_num=odal, val_loss=2.950, train_loss=1.390]
Epoch 17: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.21it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 17: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 12%|โโ | 1/8 [00:00<00:00, 116.23it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 12%|โโ | 1/8 [00:00<00:00, 112.51it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 25%|โโโ | 2/8 [00:00<00:00, 116.69it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 25%|โโโ | 2/8 [00:00<00:00, 114.76it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 38%|โโโโ | 3/8 [00:00<00:00, 117.21it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 38%|โโโโ | 3/8 [00:00<00:00, 115.90it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 50%|โโโโโ | 4/8 [00:00<00:00, 115.97it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 50%|โโโโโ | 4/8 [00:00<00:00, 114.87it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.95it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.17it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.28it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.63it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.45it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 114.87it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.60it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.09it/s, v_num=odal, val_loss=2.950, train_loss=1.150]
Epoch 18: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.34it/s, v_num=odal, val_loss=2.870, train_loss=1.150]
Epoch 18: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.40it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 18: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 12%|โโ | 1/8 [00:00<00:00, 113.53it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 12%|โโ | 1/8 [00:00<00:00, 109.95it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 25%|โโโ | 2/8 [00:00<00:00, 115.17it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 25%|โโโ | 2/8 [00:00<00:00, 113.29it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 38%|โโโโ | 3/8 [00:00<00:00, 114.00it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 38%|โโโโ | 3/8 [00:00<00:00, 112.78it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 50%|โโโโโ | 4/8 [00:00<00:00, 115.42it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 50%|โโโโโ | 4/8 [00:00<00:00, 114.49it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.30it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.54it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.88it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.23it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.30it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.71it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.73it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.23it/s, v_num=odal, val_loss=2.870, train_loss=0.973]
Epoch 19: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.75it/s, v_num=odal, val_loss=2.900, train_loss=0.973]
Epoch 19: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.76it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 19: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 12%|โโ | 1/8 [00:00<00:00, 119.76it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 12%|โโ | 1/8 [00:00<00:00, 115.84it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 25%|โโโ | 2/8 [00:00<00:00, 120.12it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 25%|โโโ | 2/8 [00:00<00:00, 118.13it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 38%|โโโโ | 3/8 [00:00<00:00, 120.22it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 38%|โโโโ | 3/8 [00:00<00:00, 118.87it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 50%|โโโโโ | 4/8 [00:00<00:00, 120.40it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 50%|โโโโโ | 4/8 [00:00<00:00, 119.39it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 62%|โโโโโโโ | 5/8 [00:00<00:00, 120.36it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 62%|โโโโโโโ | 5/8 [00:00<00:00, 119.55it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 119.40it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.69it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 119.51it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.86it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 120.68it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 120.12it/s, v_num=odal, val_loss=2.900, train_loss=0.691]
Epoch 20: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 102.51it/s, v_num=odal, val_loss=2.770, train_loss=0.691]
Epoch 20: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.47it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 20: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 12%|โโ | 1/8 [00:00<00:00, 115.77it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 12%|โโ | 1/8 [00:00<00:00, 112.01it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 25%|โโโ | 2/8 [00:00<00:00, 117.12it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 25%|โโโ | 2/8 [00:00<00:00, 115.20it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 38%|โโโโ | 3/8 [00:00<00:00, 115.57it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 38%|โโโโ | 3/8 [00:00<00:00, 114.27it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 50%|โโโโโ | 4/8 [00:00<00:00, 116.13it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 50%|โโโโโ | 4/8 [00:00<00:00, 115.05it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.24it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.43it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.57it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.90it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.64it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.05it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.83it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.33it/s, v_num=odal, val_loss=2.770, train_loss=0.663]
Epoch 21: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.58it/s, v_num=odal, val_loss=2.760, train_loss=0.663]
Epoch 21: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.65it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 21: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 12%|โโ | 1/8 [00:00<00:00, 116.16it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 12%|โโ | 1/8 [00:00<00:00, 112.42it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 25%|โโโ | 2/8 [00:00<00:00, 117.13it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 25%|โโโ | 2/8 [00:00<00:00, 115.21it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 38%|โโโโ | 3/8 [00:00<00:00, 117.85it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 38%|โโโโ | 3/8 [00:00<00:00, 116.53it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 50%|โโโโโ | 4/8 [00:00<00:00, 118.39it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 50%|โโโโโ | 4/8 [00:00<00:00, 117.42it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.88it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.09it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.17it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.51it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.32it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.75it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.61it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.11it/s, v_num=odal, val_loss=2.760, train_loss=0.714]
Epoch 22: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.49it/s, v_num=odal, val_loss=2.940, train_loss=0.714]
Epoch 22: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.49it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 22: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 12%|โโ | 1/8 [00:00<00:00, 117.98it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 12%|โโ | 1/8 [00:00<00:00, 114.16it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 25%|โโโ | 2/8 [00:00<00:00, 118.70it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 25%|โโโ | 2/8 [00:00<00:00, 116.77it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 38%|โโโโ | 3/8 [00:00<00:00, 117.26it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 38%|โโโโ | 3/8 [00:00<00:00, 115.96it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 50%|โโโโโ | 4/8 [00:00<00:00, 118.07it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 50%|โโโโโ | 4/8 [00:00<00:00, 117.08it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.60it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.80it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 119.08it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.41it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 119.41it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.83it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 120.71it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 120.19it/s, v_num=odal, val_loss=2.940, train_loss=0.883]
Epoch 23: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 102.01it/s, v_num=odal, val_loss=2.860, train_loss=0.883]
Epoch 23: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.06it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 23: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 12%|โโ | 1/8 [00:00<00:00, 117.63it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 12%|โโ | 1/8 [00:00<00:00, 113.84it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 25%|โโโ | 2/8 [00:00<00:00, 118.10it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 25%|โโโ | 2/8 [00:00<00:00, 116.17it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 38%|โโโโ | 3/8 [00:00<00:00, 118.47it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 38%|โโโโ | 3/8 [00:00<00:00, 117.17it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 50%|โโโโโ | 4/8 [00:00<00:00, 118.97it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 50%|โโโโโ | 4/8 [00:00<00:00, 117.98it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.13it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.33it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.26it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.55it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.60it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.04it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.34it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.84it/s, v_num=odal, val_loss=2.860, train_loss=0.679]
Epoch 24: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.18it/s, v_num=odal, val_loss=2.910, train_loss=0.679]
Epoch 24: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.28it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 24: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 12%|โโ | 1/8 [00:00<00:00, 120.19it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 12%|โโ | 1/8 [00:00<00:00, 116.03it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 25%|โโโ | 2/8 [00:00<00:00, 120.29it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 25%|โโโ | 2/8 [00:00<00:00, 117.99it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 38%|โโโโ | 3/8 [00:00<00:00, 118.26it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 38%|โโโโ | 3/8 [00:00<00:00, 116.95it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 50%|โโโโโ | 4/8 [00:00<00:00, 118.86it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 50%|โโโโโ | 4/8 [00:00<00:00, 117.86it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 62%|โโโโโโโ | 5/8 [00:00<00:00, 119.29it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.49it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 119.00it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.34it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 119.37it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.80it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 120.66it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 120.15it/s, v_num=odal, val_loss=2.910, train_loss=0.648]
Epoch 25: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 102.34it/s, v_num=odal, val_loss=2.890, train_loss=0.648]
Epoch 25: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.38it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 25: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 12%|โโ | 1/8 [00:00<00:00, 119.63it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 12%|โโ | 1/8 [00:00<00:00, 115.56it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 25%|โโโ | 2/8 [00:00<00:00, 118.40it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 25%|โโโ | 2/8 [00:00<00:00, 116.47it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 38%|โโโโ | 3/8 [00:00<00:00, 119.05it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 38%|โโโโ | 3/8 [00:00<00:00, 117.73it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 50%|โโโโโ | 4/8 [00:00<00:00, 118.66it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 50%|โโโโโ | 4/8 [00:00<00:00, 117.67it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.89it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.08it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.14it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.47it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.41it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.84it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.40it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.90it/s, v_num=odal, val_loss=2.890, train_loss=0.597]
Epoch 26: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.70it/s, v_num=odal, val_loss=2.830, train_loss=0.597]
Epoch 26: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.76it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 26: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 12%|โโ | 1/8 [00:00<00:00, 117.39it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 12%|โโ | 1/8 [00:00<00:00, 113.61it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 25%|โโโ | 2/8 [00:00<00:00, 117.96it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 25%|โโโ | 2/8 [00:00<00:00, 116.02it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 38%|โโโโ | 3/8 [00:00<00:00, 116.88it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 38%|โโโโ | 3/8 [00:00<00:00, 115.61it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 50%|โโโโโ | 4/8 [00:00<00:00, 117.80it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 50%|โโโโโ | 4/8 [00:00<00:00, 116.82it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.17it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.39it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 118.55it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.89it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.90it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 118.34it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 120.25it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.74it/s, v_num=odal, val_loss=2.830, train_loss=0.445]
Epoch 27: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.51it/s, v_num=odal, val_loss=3.010, train_loss=0.445]
Epoch 27: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.55it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 27: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 12%|โโ | 1/8 [00:00<00:00, 114.48it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 12%|โโ | 1/8 [00:00<00:00, 110.85it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 25%|โโโ | 2/8 [00:00<00:00, 116.25it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 25%|โโโ | 2/8 [00:00<00:00, 114.36it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 38%|โโโโ | 3/8 [00:00<00:00, 117.04it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 38%|โโโโ | 3/8 [00:00<00:00, 115.75it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 50%|โโโโโ | 4/8 [00:00<00:00, 117.26it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 50%|โโโโโ | 4/8 [00:00<00:00, 116.23it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.15it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.31it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.44it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.76it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 114.93it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 114.10it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 115.94it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 115.47it/s, v_num=odal, val_loss=3.010, train_loss=0.485]
Epoch 28: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.93it/s, v_num=odal, val_loss=2.970, train_loss=0.485]
Epoch 28: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.03it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 28: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 12%|โโ | 1/8 [00:00<00:00, 116.68it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 12%|โโ | 1/8 [00:00<00:00, 112.42it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 25%|โโโ | 2/8 [00:00<00:00, 117.43it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 25%|โโโ | 2/8 [00:00<00:00, 115.50it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 38%|โโโโ | 3/8 [00:00<00:00, 116.00it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 38%|โโโโ | 3/8 [00:00<00:00, 114.75it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 50%|โโโโโ | 4/8 [00:00<00:00, 116.96it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 50%|โโโโโ | 4/8 [00:00<00:00, 115.99it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.81it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.03it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.73it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.08it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.77it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.22it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.49it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.99it/s, v_num=odal, val_loss=2.970, train_loss=0.583]
Epoch 29: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.80it/s, v_num=odal, val_loss=3.010, train_loss=0.583]
Epoch 29: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.81it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 29: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 12%|โโ | 1/8 [00:00<00:00, 115.82it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 12%|โโ | 1/8 [00:00<00:00, 112.08it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 25%|โโโ | 2/8 [00:00<00:00, 117.14it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 25%|โโโ | 2/8 [00:00<00:00, 115.21it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 38%|โโโโ | 3/8 [00:00<00:00, 118.01it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 38%|โโโโ | 3/8 [00:00<00:00, 116.69it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 50%|โโโโโ | 4/8 [00:00<00:00, 118.10it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 50%|โโโโโ | 4/8 [00:00<00:00, 117.10it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 62%|โโโโโโโ | 5/8 [00:00<00:00, 118.19it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.40it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 117.43it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.78it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.76it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.21it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 119.01it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.45it/s, v_num=odal, val_loss=3.010, train_loss=0.388]
Epoch 30: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 101.39it/s, v_num=odal, val_loss=3.030, train_loss=0.388]
Epoch 30: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.51it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 30: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 12%|โโ | 1/8 [00:00<00:00, 118.97it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 12%|โโ | 1/8 [00:00<00:00, 115.10it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 25%|โโโ | 2/8 [00:00<00:00, 119.65it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 25%|โโโ | 2/8 [00:00<00:00, 117.68it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 38%|โโโโ | 3/8 [00:00<00:00, 118.27it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 38%|โโโโ | 3/8 [00:00<00:00, 116.35it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 50%|โโโโโ | 4/8 [00:00<00:00, 108.81it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 50%|โโโโโ | 4/8 [00:00<00:00, 107.97it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 62%|โโโโโโโ | 5/8 [00:00<00:00, 109.89it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 62%|โโโโโโโ | 5/8 [00:00<00:00, 109.16it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 109.53it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 108.95it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 110.84it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 110.34it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 112.27it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 111.82it/s, v_num=odal, val_loss=3.030, train_loss=0.519]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 95.80it/s, v_num=odal, val_loss=2.860, train_loss=0.519]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 94.95it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 31: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 12%|โโ | 1/8 [00:00<00:00, 114.68it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 12%|โโ | 1/8 [00:00<00:00, 110.91it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 25%|โโโ | 2/8 [00:00<00:00, 114.75it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 25%|โโโ | 2/8 [00:00<00:00, 112.85it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 38%|โโโโ | 3/8 [00:00<00:00, 115.35it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 38%|โโโโ | 3/8 [00:00<00:00, 114.02it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 50%|โโโโโ | 4/8 [00:00<00:00, 115.84it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 50%|โโโโโ | 4/8 [00:00<00:00, 114.85it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.33it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.55it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.87it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.23it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.32it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.75it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.60it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.09it/s, v_num=odal, val_loss=2.860, train_loss=0.559]
Epoch 32: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.17it/s, v_num=odal, val_loss=3.100, train_loss=0.559]
Epoch 32: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.16it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 32: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 12%|โโ | 1/8 [00:00<00:00, 115.41it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 12%|โโ | 1/8 [00:00<00:00, 111.73it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 25%|โโโ | 2/8 [00:00<00:00, 116.89it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 25%|โโโ | 2/8 [00:00<00:00, 114.99it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 38%|โโโโ | 3/8 [00:00<00:00, 115.87it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 38%|โโโโ | 3/8 [00:00<00:00, 114.62it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 50%|โโโโโ | 4/8 [00:00<00:00, 117.14it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 50%|โโโโโ | 4/8 [00:00<00:00, 116.13it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 62%|โโโโโโโ | 5/8 [00:00<00:00, 117.76it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.91it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.71it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.15it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.59it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.07it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.89it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 116.40it/s, v_num=odal, val_loss=3.100, train_loss=0.457]
Epoch 33: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.06it/s, v_num=odal, val_loss=2.770, train_loss=0.457]
Epoch 33: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.13it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 33: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 12%|โโ | 1/8 [00:00<00:00, 111.49it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 12%|โโ | 1/8 [00:00<00:00, 107.95it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 25%|โโโ | 2/8 [00:00<00:00, 112.17it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 25%|โโโ | 2/8 [00:00<00:00, 110.26it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 38%|โโโโ | 3/8 [00:00<00:00, 112.30it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 38%|โโโโ | 3/8 [00:00<00:00, 110.99it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 50%|โโโโโ | 4/8 [00:00<00:00, 113.00it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 50%|โโโโโ | 4/8 [00:00<00:00, 111.96it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 62%|โโโโโโโ | 5/8 [00:00<00:00, 113.63it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 62%|โโโโโโโ | 5/8 [00:00<00:00, 112.86it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 113.20it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 112.56it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 113.87it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 113.32it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 115.51it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 115.02it/s, v_num=odal, val_loss=2.770, train_loss=0.516]
Epoch 34: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 98.02it/s, v_num=odal, val_loss=3.060, train_loss=0.516]
Epoch 34: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 97.12it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 34: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 12%|โโ | 1/8 [00:00<00:00, 115.82it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 12%|โโ | 1/8 [00:00<00:00, 112.11it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 25%|โโโ | 2/8 [00:00<00:00, 116.72it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 25%|โโโ | 2/8 [00:00<00:00, 114.79it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 38%|โโโโ | 3/8 [00:00<00:00, 114.75it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 38%|โโโโ | 3/8 [00:00<00:00, 113.47it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 50%|โโโโโ | 4/8 [00:00<00:00, 115.78it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 50%|โโโโโ | 4/8 [00:00<00:00, 114.82it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 62%|โโโโโโโ | 5/8 [00:00<00:00, 116.43it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.66it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.99it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 116.34it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 117.44it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.88it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.86it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 118.37it/s, v_num=odal, val_loss=3.060, train_loss=0.434]
Epoch 35: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.74it/s, v_num=odal, val_loss=2.970, train_loss=0.434]
Epoch 35: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.77it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 35: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 0%| | 0/8 [00:00<?, ?it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 12%|โโ | 1/8 [00:00<00:00, 113.42it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 12%|โโ | 1/8 [00:00<00:00, 109.87it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 25%|โโโ | 2/8 [00:00<00:00, 114.86it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 25%|โโโ | 2/8 [00:00<00:00, 113.00it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 38%|โโโโ | 3/8 [00:00<00:00, 115.39it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 38%|โโโโ | 3/8 [00:00<00:00, 114.14it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 50%|โโโโโ | 4/8 [00:00<00:00, 116.43it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 50%|โโโโโ | 4/8 [00:00<00:00, 115.42it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 62%|โโโโโโโ | 5/8 [00:00<00:00, 115.68it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 62%|โโโโโโโ | 5/8 [00:00<00:00, 114.83it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 115.25it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 114.62it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 116.03it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 115.48it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.57it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 117.08it/s, v_num=odal, val_loss=2.970, train_loss=0.429]
Epoch 36: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 100.00it/s, v_num=odal, val_loss=2.940, train_loss=0.429]
Epoch 36: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 99.03it/s, v_num=odal, val_loss=2.940, train_loss=0.536]
Epoch 36: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 97.53it/s, v_num=odal, val_loss=2.940, train_loss=0.536]
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Validate metric DataLoader 0
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
MAE_val 1.3372687101364136
R2_val 0.5574553608894348
val_loss 2.9392478466033936
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
5. Plotting the results of the first model ๐๏
Letโs unveil the results of our first modelโs hard work. Weโre 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()
6. Training the second fusion model ๐๏
Itโs time for our second fusion model to shine! Here we train the second fusion model: ConcatTabularFeatureMaps. Weโre using the same steps as before, but this time weโre 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
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%|โโ | 1/8 [00:00<00:00, 61.55it/s]
Epoch 0: 12%|โโ | 1/8 [00:00<00:00, 60.27it/s, v_num=Maps]
Epoch 0: 25%|โโโ | 2/8 [00:00<00:00, 68.64it/s, v_num=Maps]
Epoch 0: 25%|โโโ | 2/8 [00:00<00:00, 67.90it/s, v_num=Maps]
Epoch 0: 38%|โโโโ | 3/8 [00:00<00:00, 72.46it/s, v_num=Maps]
Epoch 0: 38%|โโโโ | 3/8 [00:00<00:00, 71.91it/s, v_num=Maps]
Epoch 0: 50%|โโโโโ | 4/8 [00:00<00:00, 74.67it/s, v_num=Maps]
Epoch 0: 50%|โโโโโ | 4/8 [00:00<00:00, 74.23it/s, v_num=Maps]
Epoch 0: 62%|โโโโโโโ | 5/8 [00:00<00:00, 76.24it/s, v_num=Maps]
Epoch 0: 62%|โโโโโโโ | 5/8 [00:00<00:00, 75.88it/s, v_num=Maps]
Epoch 0: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 77.46it/s, v_num=Maps]
Epoch 0: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 77.17it/s, v_num=Maps]
Epoch 0: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 77.86it/s, v_num=Maps]
Epoch 0: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 77.60it/s, v_num=Maps]
Epoch 0: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 79.40it/s, v_num=Maps]
Epoch 0: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 79.17it/s, v_num=Maps]
Epoch 0: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 68.20it/s, v_num=Maps, val_loss=8.070]
Epoch 0: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 67.71it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 0: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 12%|โโ | 1/8 [00:00<00:00, 82.86it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 12%|โโ | 1/8 [00:00<00:00, 80.90it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 25%|โโโ | 2/8 [00:00<00:00, 82.60it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 25%|โโโ | 2/8 [00:00<00:00, 81.63it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 38%|โโโโ | 3/8 [00:00<00:00, 83.46it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 38%|โโโโ | 3/8 [00:00<00:00, 82.78it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 50%|โโโโโ | 4/8 [00:00<00:00, 84.05it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 50%|โโโโโ | 4/8 [00:00<00:00, 83.54it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.45it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.03it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.34it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 83.99it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 83.67it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 83.37it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 84.87it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 84.60it/s, v_num=Maps, val_loss=8.070, train_loss=12.60]
Epoch 1: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.71it/s, v_num=Maps, val_loss=6.060, train_loss=12.60]
Epoch 1: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.15it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 1: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 12%|โโ | 1/8 [00:00<00:00, 84.33it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 12%|โโ | 1/8 [00:00<00:00, 82.16it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 25%|โโโ | 2/8 [00:00<00:00, 84.69it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 25%|โโโ | 2/8 [00:00<00:00, 83.60it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 38%|โโโโ | 3/8 [00:00<00:00, 84.85it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 38%|โโโโ | 3/8 [00:00<00:00, 84.14it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 50%|โโโโโ | 4/8 [00:00<00:00, 83.99it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 50%|โโโโโ | 4/8 [00:00<00:00, 83.45it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.43it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.01it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.36it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 83.99it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.31it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.00it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.05it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 84.77it/s, v_num=Maps, val_loss=6.060, train_loss=7.870]
Epoch 2: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.80it/s, v_num=Maps, val_loss=9.620, train_loss=7.870]
Epoch 2: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.25it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 2: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 12%|โโ | 1/8 [00:00<00:00, 78.24it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 12%|โโ | 1/8 [00:00<00:00, 76.52it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 25%|โโโ | 2/8 [00:00<00:00, 81.93it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 25%|โโโ | 2/8 [00:00<00:00, 80.98it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 38%|โโโโ | 3/8 [00:00<00:00, 83.51it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 38%|โโโโ | 3/8 [00:00<00:00, 82.86it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 50%|โโโโโ | 4/8 [00:00<00:00, 84.43it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 50%|โโโโโ | 4/8 [00:00<00:00, 83.93it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.60it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.17it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 83.79it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 83.45it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.24it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 83.95it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.55it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.27it/s, v_num=Maps, val_loss=9.620, train_loss=6.220]
Epoch 3: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.51it/s, v_num=Maps, val_loss=5.120, train_loss=6.220]
Epoch 3: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.96it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 3: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 12%|โโ | 1/8 [00:00<00:00, 87.30it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 12%|โโ | 1/8 [00:00<00:00, 85.20it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 25%|โโโ | 2/8 [00:00<00:00, 87.00it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 25%|โโโ | 2/8 [00:00<00:00, 85.92it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 38%|โโโโ | 3/8 [00:00<00:00, 85.45it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 38%|โโโโ | 3/8 [00:00<00:00, 84.72it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 50%|โโโโโ | 4/8 [00:00<00:00, 85.24it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 50%|โโโโโ | 4/8 [00:00<00:00, 84.70it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.29it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.86it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.39it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.03it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.50it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.19it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.58it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.31it/s, v_num=Maps, val_loss=5.120, train_loss=6.910]
Epoch 4: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.76it/s, v_num=Maps, val_loss=4.450, train_loss=6.910]
Epoch 4: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.19it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 4: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 12%|โโ | 1/8 [00:00<00:00, 73.61it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 12%|โโ | 1/8 [00:00<00:00, 71.60it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 25%|โโโ | 2/8 [00:00<00:00, 75.17it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 25%|โโโ | 2/8 [00:00<00:00, 74.15it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 38%|โโโโ | 3/8 [00:00<00:00, 75.98it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 38%|โโโโ | 3/8 [00:00<00:00, 75.29it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 50%|โโโโโ | 4/8 [00:00<00:00, 75.55it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 50%|โโโโโ | 4/8 [00:00<00:00, 74.96it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 62%|โโโโโโโ | 5/8 [00:00<00:00, 74.55it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 62%|โโโโโโโ | 5/8 [00:00<00:00, 74.15it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 75.71it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 75.40it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 76.81it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 76.54it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 77.34it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 77.05it/s, v_num=Maps, val_loss=4.450, train_loss=4.610]
Epoch 5: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 67.00it/s, v_num=Maps, val_loss=3.950, train_loss=4.610]
Epoch 5: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 66.47it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 5: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 12%|โโ | 1/8 [00:00<00:00, 75.94it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 12%|โโ | 1/8 [00:00<00:00, 73.89it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 25%|โโโ | 2/8 [00:00<00:00, 76.21it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 25%|โโโ | 2/8 [00:00<00:00, 75.28it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 38%|โโโโ | 3/8 [00:00<00:00, 78.11it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 38%|โโโโ | 3/8 [00:00<00:00, 77.50it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 50%|โโโโโ | 4/8 [00:00<00:00, 79.07it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 50%|โโโโโ | 4/8 [00:00<00:00, 78.60it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 62%|โโโโโโโ | 5/8 [00:00<00:00, 80.18it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 62%|โโโโโโโ | 5/8 [00:00<00:00, 79.80it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 80.55it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 80.09it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 80.57it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 80.30it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 82.13it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 81.89it/s, v_num=Maps, val_loss=3.950, train_loss=3.850]
Epoch 6: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 71.84it/s, v_num=Maps, val_loss=3.270, train_loss=3.850]
Epoch 6: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 71.31it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 6: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 12%|โโ | 1/8 [00:00<00:00, 84.48it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 12%|โโ | 1/8 [00:00<00:00, 82.23it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 25%|โโโ | 2/8 [00:00<00:00, 84.88it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 25%|โโโ | 2/8 [00:00<00:00, 83.85it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 38%|โโโโ | 3/8 [00:00<00:00, 85.55it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 38%|โโโโ | 3/8 [00:00<00:00, 84.86it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 50%|โโโโโ | 4/8 [00:00<00:00, 84.82it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 50%|โโโโโ | 4/8 [00:00<00:00, 84.30it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.14it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.72it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.46it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.11it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.49it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.20it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.21it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.94it/s, v_num=Maps, val_loss=3.270, train_loss=3.620]
Epoch 7: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.94it/s, v_num=Maps, val_loss=3.180, train_loss=3.620]
Epoch 7: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.36it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 7: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 12%|โโ | 1/8 [00:00<00:00, 81.34it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 12%|โโ | 1/8 [00:00<00:00, 79.46it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 25%|โโโ | 2/8 [00:00<00:00, 83.89it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 25%|โโโ | 2/8 [00:00<00:00, 82.90it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 38%|โโโโ | 3/8 [00:00<00:00, 84.63it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 38%|โโโโ | 3/8 [00:00<00:00, 84.03it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 50%|โโโโโ | 4/8 [00:00<00:00, 85.45it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 50%|โโโโโ | 4/8 [00:00<00:00, 84.89it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.73it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.28it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.60it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.20it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.49it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.14it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.41it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.13it/s, v_num=Maps, val_loss=3.180, train_loss=2.790]
Epoch 8: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.11it/s, v_num=Maps, val_loss=3.580, train_loss=2.790]
Epoch 8: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.54it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 8: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 12%|โโ | 1/8 [00:00<00:00, 83.49it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 12%|โโ | 1/8 [00:00<00:00, 81.53it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 25%|โโโ | 2/8 [00:00<00:00, 84.63it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 25%|โโโ | 2/8 [00:00<00:00, 83.62it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 38%|โโโโ | 3/8 [00:00<00:00, 83.79it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 38%|โโโโ | 3/8 [00:00<00:00, 83.09it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 50%|โโโโโ | 4/8 [00:00<00:00, 84.11it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 50%|โโโโโ | 4/8 [00:00<00:00, 83.59it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 62%|โโโโโโโ | 5/8 [00:00<00:00, 83.05it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 62%|โโโโโโโ | 5/8 [00:00<00:00, 82.45it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 83.26it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 82.92it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 83.53it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 83.19it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 84.21it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 83.92it/s, v_num=Maps, val_loss=3.580, train_loss=2.540]
Epoch 9: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 72.55it/s, v_num=Maps, val_loss=2.690, train_loss=2.540]
Epoch 9: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 72.01it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 9: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 12%|โโ | 1/8 [00:00<00:00, 83.86it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 12%|โโ | 1/8 [00:00<00:00, 81.90it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 25%|โโโ | 2/8 [00:00<00:00, 84.97it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 25%|โโโ | 2/8 [00:00<00:00, 83.95it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 38%|โโโโ | 3/8 [00:00<00:00, 85.68it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 38%|โโโโ | 3/8 [00:00<00:00, 84.99it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 50%|โโโโโ | 4/8 [00:00<00:00, 85.62it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 50%|โโโโโ | 4/8 [00:00<00:00, 84.94it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.22it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.80it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.85it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.51it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.04it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.74it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.26it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.97it/s, v_num=Maps, val_loss=2.690, train_loss=2.310]
Epoch 10: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.98it/s, v_num=Maps, val_loss=2.570, train_loss=2.310]
Epoch 10: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.41it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 10: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 12%|โโ | 1/8 [00:00<00:00, 87.19it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 12%|โโ | 1/8 [00:00<00:00, 85.06it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 25%|โโโ | 2/8 [00:00<00:00, 87.56it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 25%|โโโ | 2/8 [00:00<00:00, 86.48it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 38%|โโโโ | 3/8 [00:00<00:00, 84.63it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 38%|โโโโ | 3/8 [00:00<00:00, 83.90it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 50%|โโโโโ | 4/8 [00:00<00:00, 80.97it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 50%|โโโโโ | 4/8 [00:00<00:00, 80.36it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 62%|โโโโโโโ | 5/8 [00:00<00:00, 80.46it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 62%|โโโโโโโ | 5/8 [00:00<00:00, 80.02it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 80.48it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 80.11it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 80.17it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 79.82it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 80.78it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 80.50it/s, v_num=Maps, val_loss=2.570, train_loss=1.730]
Epoch 11: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 69.59it/s, v_num=Maps, val_loss=2.500, train_loss=1.730]
Epoch 11: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 69.03it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 11: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 12%|โโ | 1/8 [00:00<00:00, 81.12it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 12%|โโ | 1/8 [00:00<00:00, 79.26it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 25%|โโโ | 2/8 [00:00<00:00, 81.61it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 25%|โโโ | 2/8 [00:00<00:00, 80.67it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 38%|โโโโ | 3/8 [00:00<00:00, 83.08it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 38%|โโโโ | 3/8 [00:00<00:00, 82.43it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 50%|โโโโโ | 4/8 [00:00<00:00, 83.80it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 50%|โโโโโ | 4/8 [00:00<00:00, 83.29it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 62%|โโโโโโโ | 5/8 [00:00<00:00, 83.71it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 62%|โโโโโโโ | 5/8 [00:00<00:00, 83.30it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.21it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 83.87it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.76it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.45it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.02it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.76it/s, v_num=Maps, val_loss=2.500, train_loss=1.470]
Epoch 12: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.70it/s, v_num=Maps, val_loss=2.420, train_loss=1.470]
Epoch 12: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.13it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 12: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 12%|โโ | 1/8 [00:00<00:00, 85.78it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 12%|โโ | 1/8 [00:00<00:00, 83.74it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 25%|โโโ | 2/8 [00:00<00:00, 84.32it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 25%|โโโ | 2/8 [00:00<00:00, 83.32it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 38%|โโโโ | 3/8 [00:00<00:00, 85.41it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 38%|โโโโ | 3/8 [00:00<00:00, 84.72it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 50%|โโโโโ | 4/8 [00:00<00:00, 85.98it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 50%|โโโโโ | 4/8 [00:00<00:00, 85.47it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 62%|โโโโโโโ | 5/8 [00:00<00:00, 86.50it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 62%|โโโโโโโ | 5/8 [00:00<00:00, 86.04it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.90it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.54it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.63it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.32it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.72it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.45it/s, v_num=Maps, val_loss=2.420, train_loss=1.020]
Epoch 13: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 76.16it/s, v_num=Maps, val_loss=2.280, train_loss=1.020]
Epoch 13: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.57it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 13: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 12%|โโ | 1/8 [00:00<00:00, 86.31it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 12%|โโ | 1/8 [00:00<00:00, 84.16it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 25%|โโโ | 2/8 [00:00<00:00, 86.06it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 25%|โโโ | 2/8 [00:00<00:00, 84.98it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 38%|โโโโ | 3/8 [00:00<00:00, 85.46it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 38%|โโโโ | 3/8 [00:00<00:00, 84.76it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 50%|โโโโโ | 4/8 [00:00<00:00, 84.93it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 50%|โโโโโ | 4/8 [00:00<00:00, 84.41it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.42it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.95it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.02it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.63it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.34it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.03it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.07it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 84.80it/s, v_num=Maps, val_loss=2.280, train_loss=0.986]
Epoch 14: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.56it/s, v_num=Maps, val_loss=2.380, train_loss=0.986]
Epoch 14: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.01it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 14: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 12%|โโ | 1/8 [00:00<00:00, 82.15it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 12%|โโ | 1/8 [00:00<00:00, 80.25it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 25%|โโโ | 2/8 [00:00<00:00, 82.49it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 25%|โโโ | 2/8 [00:00<00:00, 81.50it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 38%|โโโโ | 3/8 [00:00<00:00, 83.31it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 38%|โโโโ | 3/8 [00:00<00:00, 82.57it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 50%|โโโโโ | 4/8 [00:00<00:00, 84.35it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 50%|โโโโโ | 4/8 [00:00<00:00, 83.84it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.04it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.62it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.94it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.60it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.78it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.48it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.08it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.81it/s, v_num=Maps, val_loss=2.380, train_loss=0.936]
Epoch 15: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.65it/s, v_num=Maps, val_loss=2.270, train_loss=0.936]
Epoch 15: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.08it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 15: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 12%|โโ | 1/8 [00:00<00:00, 85.63it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 12%|โโ | 1/8 [00:00<00:00, 83.52it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 25%|โโโ | 2/8 [00:00<00:00, 85.36it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 25%|โโโ | 2/8 [00:00<00:00, 84.32it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 38%|โโโโ | 3/8 [00:00<00:00, 84.50it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 38%|โโโโ | 3/8 [00:00<00:00, 83.82it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 50%|โโโโโ | 4/8 [00:00<00:00, 84.53it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 50%|โโโโโ | 4/8 [00:00<00:00, 84.02it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.11it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.66it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.49it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.14it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.77it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.47it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.99it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.72it/s, v_num=Maps, val_loss=2.270, train_loss=0.701]
Epoch 16: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.14it/s, v_num=Maps, val_loss=2.190, train_loss=0.701]
Epoch 16: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.53it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 16: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 12%|โโ | 1/8 [00:00<00:00, 85.63it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 12%|โโ | 1/8 [00:00<00:00, 83.60it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 25%|โโโ | 2/8 [00:00<00:00, 85.93it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 25%|โโโ | 2/8 [00:00<00:00, 84.90it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 38%|โโโโ | 3/8 [00:00<00:00, 86.37it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 38%|โโโโ | 3/8 [00:00<00:00, 85.67it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 50%|โโโโโ | 4/8 [00:00<00:00, 86.65it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 50%|โโโโโ | 4/8 [00:00<00:00, 86.06it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 62%|โโโโโโโ | 5/8 [00:00<00:00, 86.29it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.83it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.58it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.22it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.64it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.31it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.37it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.04it/s, v_num=Maps, val_loss=2.190, train_loss=0.748]
Epoch 17: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.39it/s, v_num=Maps, val_loss=2.420, train_loss=0.748]
Epoch 17: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.79it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 17: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 12%|โโ | 1/8 [00:00<00:00, 81.22it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 12%|โโ | 1/8 [00:00<00:00, 78.94it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 25%|โโโ | 2/8 [00:00<00:00, 76.94it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 25%|โโโ | 2/8 [00:00<00:00, 75.67it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 38%|โโโโ | 3/8 [00:00<00:00, 77.09it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 38%|โโโโ | 3/8 [00:00<00:00, 76.42it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 50%|โโโโโ | 4/8 [00:00<00:00, 78.11it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 50%|โโโโโ | 4/8 [00:00<00:00, 77.63it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 62%|โโโโโโโ | 5/8 [00:00<00:00, 79.45it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 62%|โโโโโโโ | 5/8 [00:00<00:00, 79.06it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 80.56it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 80.24it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 80.29it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 79.97it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 81.21it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 80.92it/s, v_num=Maps, val_loss=2.420, train_loss=0.869]
Epoch 18: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 70.39it/s, v_num=Maps, val_loss=2.580, train_loss=0.869]
Epoch 18: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 69.84it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 18: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 12%|โโ | 1/8 [00:00<00:00, 82.50it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 12%|โโ | 1/8 [00:00<00:00, 80.59it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 25%|โโโ | 2/8 [00:00<00:00, 83.79it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 25%|โโโ | 2/8 [00:00<00:00, 82.79it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 38%|โโโโ | 3/8 [00:00<00:00, 84.79it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 38%|โโโโ | 3/8 [00:00<00:00, 84.11it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 50%|โโโโโ | 4/8 [00:00<00:00, 84.53it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 50%|โโโโโ | 4/8 [00:00<00:00, 84.02it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.17it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.76it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.39it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.92it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.47it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.17it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.67it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.40it/s, v_num=Maps, val_loss=2.580, train_loss=0.760]
Epoch 19: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.19it/s, v_num=Maps, val_loss=2.630, train_loss=0.760]
Epoch 19: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.62it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 19: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 12%|โโ | 1/8 [00:00<00:00, 82.47it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 12%|โโ | 1/8 [00:00<00:00, 80.45it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 25%|โโโ | 2/8 [00:00<00:00, 84.94it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 25%|โโโ | 2/8 [00:00<00:00, 83.93it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 38%|โโโโ | 3/8 [00:00<00:00, 85.60it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 38%|โโโโ | 3/8 [00:00<00:00, 84.85it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 50%|โโโโโ | 4/8 [00:00<00:00, 85.68it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 50%|โโโโโ | 4/8 [00:00<00:00, 85.13it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.89it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.45it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.89it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.53it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.03it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.71it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.75it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.47it/s, v_num=Maps, val_loss=2.630, train_loss=0.770]
Epoch 20: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.42it/s, v_num=Maps, val_loss=2.500, train_loss=0.770]
Epoch 20: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.85it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 20: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 12%|โโ | 1/8 [00:00<00:00, 83.16it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 12%|โโ | 1/8 [00:00<00:00, 81.17it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 25%|โโโ | 2/8 [00:00<00:00, 84.07it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 25%|โโโ | 2/8 [00:00<00:00, 83.01it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 38%|โโโโ | 3/8 [00:00<00:00, 82.47it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 38%|โโโโ | 3/8 [00:00<00:00, 81.75it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 50%|โโโโโ | 4/8 [00:00<00:00, 82.96it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 50%|โโโโโ | 4/8 [00:00<00:00, 82.43it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 62%|โโโโโโโ | 5/8 [00:00<00:00, 83.33it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 62%|โโโโโโโ | 5/8 [00:00<00:00, 82.91it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 83.70it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 83.36it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.17it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 83.85it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.49it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.22it/s, v_num=Maps, val_loss=2.500, train_loss=0.669]
Epoch 21: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.82it/s, v_num=Maps, val_loss=2.840, train_loss=0.669]
Epoch 21: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.27it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 21: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 12%|โโ | 1/8 [00:00<00:00, 85.52it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 12%|โโ | 1/8 [00:00<00:00, 83.44it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 25%|โโโ | 2/8 [00:00<00:00, 85.76it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 25%|โโโ | 2/8 [00:00<00:00, 84.70it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 38%|โโโโ | 3/8 [00:00<00:00, 85.93it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 38%|โโโโ | 3/8 [00:00<00:00, 85.22it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 50%|โโโโโ | 4/8 [00:00<00:00, 86.32it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 50%|โโโโโ | 4/8 [00:00<00:00, 85.80it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 62%|โโโโโโโ | 5/8 [00:00<00:00, 86.00it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.58it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.37it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.01it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.42it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.10it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.49it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.22it/s, v_num=Maps, val_loss=2.840, train_loss=0.781]
Epoch 22: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.87it/s, v_num=Maps, val_loss=2.790, train_loss=0.781]
Epoch 22: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.29it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 22: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 12%|โโ | 1/8 [00:00<00:00, 86.98it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 12%|โโ | 1/8 [00:00<00:00, 84.88it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 25%|โโโ | 2/8 [00:00<00:00, 85.24it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 25%|โโโ | 2/8 [00:00<00:00, 84.22it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 38%|โโโโ | 3/8 [00:00<00:00, 85.23it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 38%|โโโโ | 3/8 [00:00<00:00, 84.62it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 50%|โโโโโ | 4/8 [00:00<00:00, 85.94it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 50%|โโโโโ | 4/8 [00:00<00:00, 85.41it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 62%|โโโโโโโ | 5/8 [00:00<00:00, 86.35it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.93it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.81it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.45it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.60it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.27it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.63it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.35it/s, v_num=Maps, val_loss=2.790, train_loss=0.869]
Epoch 23: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.71it/s, v_num=Maps, val_loss=2.540, train_loss=0.869]
Epoch 23: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.11it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 23: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 12%|โโ | 1/8 [00:00<00:00, 86.14it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 12%|โโ | 1/8 [00:00<00:00, 84.08it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 25%|โโโ | 2/8 [00:00<00:00, 86.50it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 25%|โโโ | 2/8 [00:00<00:00, 85.45it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 38%|โโโโ | 3/8 [00:00<00:00, 86.84it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 38%|โโโโ | 3/8 [00:00<00:00, 86.11it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 50%|โโโโโ | 4/8 [00:00<00:00, 86.01it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 50%|โโโโโ | 4/8 [00:00<00:00, 85.47it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 62%|โโโโโโโ | 5/8 [00:00<00:00, 86.18it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.73it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.24it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.86it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.31it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.00it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.98it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.69it/s, v_num=Maps, val_loss=2.540, train_loss=0.754]
Epoch 24: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.90it/s, v_num=Maps, val_loss=2.430, train_loss=0.754]
Epoch 24: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.35it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 24: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 12%|โโ | 1/8 [00:00<00:00, 83.04it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 12%|โโ | 1/8 [00:00<00:00, 81.11it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 25%|โโโ | 2/8 [00:00<00:00, 84.81it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 25%|โโโ | 2/8 [00:00<00:00, 83.79it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 38%|โโโโ | 3/8 [00:00<00:00, 85.66it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 38%|โโโโ | 3/8 [00:00<00:00, 84.96it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 50%|โโโโโ | 4/8 [00:00<00:00, 86.14it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 50%|โโโโโ | 4/8 [00:00<00:00, 85.56it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 62%|โโโโโโโ | 5/8 [00:00<00:00, 86.42it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.99it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.03it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.65it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.20it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.90it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.41it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.13it/s, v_num=Maps, val_loss=2.430, train_loss=0.511]
Epoch 25: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.88it/s, v_num=Maps, val_loss=2.620, train_loss=0.511]
Epoch 25: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.31it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 25: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 12%|โโ | 1/8 [00:00<00:00, 83.89it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 12%|โโ | 1/8 [00:00<00:00, 81.90it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 25%|โโโ | 2/8 [00:00<00:00, 85.09it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 25%|โโโ | 2/8 [00:00<00:00, 84.06it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 38%|โโโโ | 3/8 [00:00<00:00, 84.19it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 38%|โโโโ | 3/8 [00:00<00:00, 83.59it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 50%|โโโโโ | 4/8 [00:00<00:00, 85.39it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 50%|โโโโโ | 4/8 [00:00<00:00, 84.85it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.80it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.37it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.09it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.72it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.17it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.80it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.72it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.42it/s, v_num=Maps, val_loss=2.620, train_loss=0.536]
Epoch 26: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.67it/s, v_num=Maps, val_loss=2.760, train_loss=0.536]
Epoch 26: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 73.08it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 26: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 12%|โโ | 1/8 [00:00<00:00, 78.00it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 12%|โโ | 1/8 [00:00<00:00, 75.78it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 25%|โโโ | 2/8 [00:00<00:00, 75.15it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 25%|โโโ | 2/8 [00:00<00:00, 74.10it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 38%|โโโโ | 3/8 [00:00<00:00, 75.35it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 38%|โโโโ | 3/8 [00:00<00:00, 74.69it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 50%|โโโโโ | 4/8 [00:00<00:00, 77.00it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 50%|โโโโโ | 4/8 [00:00<00:00, 76.49it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 62%|โโโโโโโ | 5/8 [00:00<00:00, 77.36it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 62%|โโโโโโโ | 5/8 [00:00<00:00, 76.92it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 75.99it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 75.62it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 75.78it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 75.48it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 77.38it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 77.13it/s, v_num=Maps, val_loss=2.760, train_loss=0.515]
Epoch 27: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 67.88it/s, v_num=Maps, val_loss=2.620, train_loss=0.515]
Epoch 27: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 67.39it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 27: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 12%|โโ | 1/8 [00:00<00:00, 84.07it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 12%|โโ | 1/8 [00:00<00:00, 82.07it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 25%|โโโ | 2/8 [00:00<00:00, 83.20it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 25%|โโโ | 2/8 [00:00<00:00, 82.22it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 38%|โโโโ | 3/8 [00:00<00:00, 84.55it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 38%|โโโโ | 3/8 [00:00<00:00, 83.88it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 50%|โโโโโ | 4/8 [00:00<00:00, 85.38it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 50%|โโโโโ | 4/8 [00:00<00:00, 84.86it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.64it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.22it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.07it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.72it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.88it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.57it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.07it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.80it/s, v_num=Maps, val_loss=2.620, train_loss=0.464]
Epoch 28: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.68it/s, v_num=Maps, val_loss=2.920, train_loss=0.464]
Epoch 28: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.11it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 28: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 12%|โโ | 1/8 [00:00<00:00, 85.86it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 12%|โโ | 1/8 [00:00<00:00, 83.81it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 25%|โโโ | 2/8 [00:00<00:00, 86.48it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 25%|โโโ | 2/8 [00:00<00:00, 85.37it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 38%|โโโโ | 3/8 [00:00<00:00, 86.89it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 38%|โโโโ | 3/8 [00:00<00:00, 86.18it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 50%|โโโโโ | 4/8 [00:00<00:00, 85.63it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 50%|โโโโโ | 4/8 [00:00<00:00, 85.09it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.34it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 62%|โโโโโโโ | 5/8 [00:00<00:00, 84.92it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.43it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 85.07it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.64it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 85.34it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.64it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 86.36it/s, v_num=Maps, val_loss=2.920, train_loss=0.502]
Epoch 29: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.16it/s, v_num=Maps, val_loss=2.410, train_loss=0.502]
Epoch 29: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.57it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 29: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 12%|โโ | 1/8 [00:00<00:00, 81.06it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 12%|โโ | 1/8 [00:00<00:00, 79.23it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 25%|โโโ | 2/8 [00:00<00:00, 83.32it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 25%|โโโ | 2/8 [00:00<00:00, 82.33it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 38%|โโโโ | 3/8 [00:00<00:00, 84.77it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 38%|โโโโ | 3/8 [00:00<00:00, 84.09it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 50%|โโโโโ | 4/8 [00:00<00:00, 85.05it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 50%|โโโโโ | 4/8 [00:00<00:00, 84.50it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.77it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.34it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.45it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 84.04it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.76it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 84.44it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.98it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 85.71it/s, v_num=Maps, val_loss=2.410, train_loss=0.540]
Epoch 30: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.78it/s, v_num=Maps, val_loss=2.480, train_loss=0.540]
Epoch 30: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.19it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 30: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 0%| | 0/8 [00:00<?, ?it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 12%|โโ | 1/8 [00:00<00:00, 86.79it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 12%|โโ | 1/8 [00:00<00:00, 84.63it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 25%|โโโ | 2/8 [00:00<00:00, 86.87it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 25%|โโโ | 2/8 [00:00<00:00, 85.79it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 38%|โโโโ | 3/8 [00:00<00:00, 85.46it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 38%|โโโโ | 3/8 [00:00<00:00, 84.76it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 50%|โโโโโ | 4/8 [00:00<00:00, 85.93it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 50%|โโโโโ | 4/8 [00:00<00:00, 85.40it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 62%|โโโโโโโ | 5/8 [00:00<00:00, 86.19it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 62%|โโโโโโโ | 5/8 [00:00<00:00, 85.71it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.47it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 75%|โโโโโโโโ | 6/8 [00:00<00:00, 86.10it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.73it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 88%|โโโโโโโโโ | 7/8 [00:00<00:00, 86.41it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.83it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 87.56it/s, v_num=Maps, val_loss=2.480, train_loss=0.666]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.72it/s, v_num=Maps, val_loss=2.690, train_loss=0.666]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 75.11it/s, v_num=Maps, val_loss=2.690, train_loss=0.532]
Epoch 31: 100%|โโโโโโโโโโ| 8/8 [00:00<00:00, 74.27it/s, v_num=Maps, val_loss=2.690, train_loss=0.532]
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Validate metric DataLoader 0
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
MAE_val 1.178006887435913
R2_val 0.6820151209831238
val_loss 2.6859920024871826
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
7. Plotting the results of the second model ๐๏
reals_preds_model_2 = RealsVsPreds.from_final_val_data(model_2_list)
plt.show()
8. Comparing the results of the two models ๐๏
Let the ultimate showdown begin! Weโre comparing the results of our two models.
Weโre 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()
9. Saving the metrics of the two models ๐พ๏
Time to archive our modelsโ achievements. Weโre using the ModelComparison
class to save the metrics of the two models.
metrics_dataframe
Total running time of the script: (0 minutes 8.075 seconds)