MQ-RNN model¶
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#!pip install deepts_forecasting
#!pip install deepts_forecasting
Import libraries¶
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import numpy as np
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
from deepts_forecasting.utils.data import TimeSeriesDataSet
from deepts_forecasting.utils.data.encoders import TorchNormalizer
from deepts_forecasting.datasets import AirPassengersDataset
from deepts_forecasting.models.mqrnn import MQRNN
import numpy as np
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
from deepts_forecasting.utils.data import TimeSeriesDataSet
from deepts_forecasting.utils.data.encoders import TorchNormalizer
from deepts_forecasting.datasets import AirPassengersDataset
from deepts_forecasting.models.mqrnn import MQRNN
Dataset¶
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data = AirPassengersDataset().load()
data['year'] = data['Month'].dt.year
data['month'] = data['Month'].dt.month
data['group'] = '0'
data['time_idx'] = np.arange(len(data))
data['Passengers'] = data['Passengers'].astype(float)
data['month'] = data['month'].astype('str')
data.head()
data = AirPassengersDataset().load()
data['year'] = data['Month'].dt.year
data['month'] = data['Month'].dt.month
data['group'] = '0'
data['time_idx'] = np.arange(len(data))
data['Passengers'] = data['Passengers'].astype(float)
data['month'] = data['month'].astype('str')
data.head()
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| Month | Passengers | year | month | group | time_idx | |
|---|---|---|---|---|---|---|
| 0 | 1949-01-01 | 112.0 | 1949 | 1 | 0 | 0 |
| 1 | 1949-02-01 | 118.0 | 1949 | 2 | 0 | 1 |
| 2 | 1949-03-01 | 132.0 | 1949 | 3 | 0 | 2 |
| 3 | 1949-04-01 | 129.0 | 1949 | 4 | 0 | 3 |
| 4 | 1949-05-01 | 121.0 | 1949 | 5 | 0 | 4 |
Split train/test sets¶
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max_encoder_length = 18
max_prediction_length = 12
training_cutoff = data["time_idx"].max() - max_encoder_length - max_prediction_length
training = TimeSeriesDataSet(
data[lambda x: x.time_idx <= training_cutoff],
max_encoder_length= max_encoder_length,
min_encoder_length=max_encoder_length,
max_prediction_length=max_prediction_length,
min_prediction_length=max_prediction_length,
time_idx="time_idx",
target="Passengers",
group_ids=["group"],
static_categoricals=[],
static_reals=[],
time_varying_known_categoricals=['month'],
time_varying_known_reals=[],
time_varying_unknown_reals=["Passengers"],
time_varying_unknown_categoricals=[],
target_normalizer=TorchNormalizer(method="standard",
transformation=None),
)
training.get_parameters()
validation = TimeSeriesDataSet.from_dataset(training,
data[lambda x: x.time_idx > training_cutoff])
batch_size = 16
train_dataloader = DataLoader(training, batch_size=batch_size, shuffle=False, drop_last=False)
val_dataloader = DataLoader(validation, batch_size=batch_size, shuffle=False, drop_last=False)
max_encoder_length = 18
max_prediction_length = 12
training_cutoff = data["time_idx"].max() - max_encoder_length - max_prediction_length
training = TimeSeriesDataSet(
data[lambda x: x.time_idx <= training_cutoff],
max_encoder_length= max_encoder_length,
min_encoder_length=max_encoder_length,
max_prediction_length=max_prediction_length,
min_prediction_length=max_prediction_length,
time_idx="time_idx",
target="Passengers",
group_ids=["group"],
static_categoricals=[],
static_reals=[],
time_varying_known_categoricals=['month'],
time_varying_known_reals=[],
time_varying_unknown_reals=["Passengers"],
time_varying_unknown_categoricals=[],
target_normalizer=TorchNormalizer(method="standard",
transformation=None),
)
training.get_parameters()
validation = TimeSeriesDataSet.from_dataset(training,
data[lambda x: x.time_idx > training_cutoff])
batch_size = 16
train_dataloader = DataLoader(training, batch_size=batch_size, shuffle=False, drop_last=False)
val_dataloader = DataLoader(validation, batch_size=batch_size, shuffle=False, drop_last=False)
Define model¶
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pl.seed_everything(1234)
# create PyTorch Lighning Trainer with early stopping
early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4,
patience=60, verbose=False, mode="min")
lr_logger = LearningRateMonitor()
trainer = pl.Trainer(
max_epochs=300,
gpus=0, # run on CPU, if on multiple GPUs, use accelerator="ddp"
gradient_clip_val=0.1,
limit_train_batches=30, # 30 batches per epoch
callbacks=[lr_logger, early_stop_callback],
logger=TensorBoardLogger("lightning_logs")
)
model = MQRNN.from_dataset(
training,
quantiles=[0.25, 0.5, 0.75],
hidden_size=16,
rnn_layers=2,
)
model.summarize
pl.seed_everything(1234)
# create PyTorch Lighning Trainer with early stopping
early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4,
patience=60, verbose=False, mode="min")
lr_logger = LearningRateMonitor()
trainer = pl.Trainer(
max_epochs=300,
gpus=0, # run on CPU, if on multiple GPUs, use accelerator="ddp"
gradient_clip_val=0.1,
limit_train_batches=30, # 30 batches per epoch
callbacks=[lr_logger, early_stop_callback],
logger=TensorBoardLogger("lightning_logs")
)
model = MQRNN.from_dataset(
training,
quantiles=[0.25, 0.5, 0.75],
hidden_size=16,
rnn_layers=2,
)
model.summarize
Global seed set to 1234 GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs
Out[26]:
<bound method LightningModule.summarize of MQRNN(
(loss): QuantileLoss()
(logging_metrics): ModuleList()
(global_mlp): Linear(in_features=22, out_features=208, bias=True)
(local_mlp): Linear(in_features=390, out_features=3, bias=True)
(encode_rnn): LSTM(7, 16, num_layers=2, batch_first=True)
(embeddings): ModuleDict(
(month): Embedding(12, 6)
)
)>
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model.hparams
model.hparams
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"categorical_groups": {}
"embedding_labels": {'month': array(['1', '10', '11', '12', '2', '3', '4', '5', '6', '7', '8', '9'],
dtype=object)}
"embedding_paddings": []
"embedding_sizes": {'month': [12, 6]}
"hidden_size": 16
"learning_rate": 0.001
"log_interval": -1
"log_val_interval": None
"logging_metrics": ModuleList()
"loss": QuantileLoss()
"max_encoder_length": 18
"max_prediction_length": 12
"monotone_constaints": {}
"output_size": 1
"output_transformer": TorchNormalizer()
"quantiles": [0.25, 0.5, 0.75]
"rnn_layers": 2
"static_categoricals": []
"static_reals": []
"time_varying_categoricals_decoder": ['month']
"time_varying_categoricals_encoder": ['month']
"time_varying_reals_decoder": []
"time_varying_reals_encoder": ['Passengers']
"x_categoricals": ['month']
"x_reals": ['Passengers']
Train model with early stopping¶
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trainer.fit(
model, train_dataloader=train_dataloader, val_dataloaders=val_dataloader,
)
# (given that we use early stopping, this is not necessarily the last epoch)
best_model_path = trainer.checkpoint_callback.best_model_path
best_model = MQRNN.load_from_checkpoint(best_model_path)
# calcualte mean absolute error on validation set
actuals = torch.cat([model.transform_output(prediction=y, target_scale=x['target_scale'])
for x, y in iter(val_dataloader)])
predictions,x_index = best_model.predict(val_dataloader)
mae = (actuals - predictions[:, :, 1:2]).abs().mean()
# print('predictions shape is:', predictions.shape)
# print('actuals shape is:', actuals.shape)
print(torch.cat([actuals, predictions[:, :, 1:2]]))
print('MAE is:', mae)
trainer.fit(
model, train_dataloader=train_dataloader, val_dataloaders=val_dataloader,
)
# (given that we use early stopping, this is not necessarily the last epoch)
best_model_path = trainer.checkpoint_callback.best_model_path
best_model = MQRNN.load_from_checkpoint(best_model_path)
# calcualte mean absolute error on validation set
actuals = torch.cat([model.transform_output(prediction=y, target_scale=x['target_scale'])
for x, y in iter(val_dataloader)])
predictions,x_index = best_model.predict(val_dataloader)
mae = (actuals - predictions[:, :, 1:2]).abs().mean()
# print('predictions shape is:', predictions.shape)
# print('actuals shape is:', actuals.shape)
print(torch.cat([actuals, predictions[:, :, 1:2]]))
print('MAE is:', mae)
| Name | Type | Params ------------------------------------------------- 0 | loss | QuantileLoss | 0 1 | logging_metrics | ModuleList | 0 2 | global_mlp | Linear | 4.8 K 3 | local_mlp | Linear | 1.2 K 4 | encode_rnn | LSTM | 3.8 K 5 | embeddings | ModuleDict | 72 ------------------------------------------------- 9.8 K Trainable params 0 Non-trainable params 9.8 K Total params 0.039 Total estimated model params size (MB)
Global seed set to 1234
Epoch 0: 86%|██████████▎ | 6/7 [00:00<00:00, 46.50it/s, loss=1.25, v_num=16]
Validating: 0it [00:00, ?it/s]
Epoch 0: 100%|█| 7/7 [00:00<00:00, 49.64it/s, loss=1.25, v_num=16, val_loss=4
Epoch 1: 86%|▊| 6/7 [00:00<00:00, 40.53it/s, loss=1.19, v_num=16, val_loss=4
Validating: 0it [00:00, ?it/s]
Epoch 1: 100%|█| 7/7 [00:00<00:00, 43.61it/s, loss=1.19, v_num=16, val_loss=4
Epoch 2: 86%|▊| 6/7 [00:00<00:00, 37.15it/s, loss=1.15, v_num=16, val_loss=4
Validating: 0it [00:00, ?it/s]
Epoch 2: 100%|█| 7/7 [00:00<00:00, 39.88it/s, loss=1.15, v_num=16, val_loss=4
Epoch 3: 86%|▊| 6/7 [00:00<00:00, 35.29it/s, loss=1.19, v_num=16, val_loss=4
Validating: 0it [00:00, ?it/s]
Epoch 3: 100%|█| 7/7 [00:00<00:00, 37.83it/s, loss=1.19, v_num=16, val_loss=4
Epoch 4: 86%|▊| 6/7 [00:00<00:00, 28.77it/s, loss=1.13, v_num=16, val_loss=4
Validating: 0it [00:00, ?it/s]
Epoch 4: 100%|█| 7/7 [00:00<00:00, 31.24it/s, loss=1.13, v_num=16, val_loss=3
Epoch 5: 86%|▊| 6/7 [00:00<00:00, 36.03it/s, loss=1.1, v_num=16, val_loss=3.
Validating: 0it [00:00, ?it/s]
Epoch 5: 100%|█| 7/7 [00:00<00:00, 38.56it/s, loss=1.1, v_num=16, val_loss=3.
Epoch 6: 86%|▊| 6/7 [00:00<00:00, 28.91it/s, loss=1.06, v_num=16, val_loss=3
Validating: 0it [00:00, ?it/s]
Epoch 6: 100%|█| 7/7 [00:00<00:00, 31.04it/s, loss=1.06, v_num=16, val_loss=3
Epoch 7: 86%|▊| 6/7 [00:00<00:00, 31.00it/s, loss=1.03, v_num=16, val_loss=3
Validating: 0it [00:00, ?it/s]
Epoch 7: 100%|█| 7/7 [00:00<00:00, 33.09it/s, loss=1.03, v_num=16, val_loss=3
Epoch 8: 86%|▊| 6/7 [00:00<00:00, 34.08it/s, loss=0.992, v_num=16, val_loss=
Validating: 0it [00:00, ?it/s]
Epoch 8: 100%|█| 7/7 [00:00<00:00, 36.45it/s, loss=0.992, v_num=16, val_loss=
Epoch 9: 86%|▊| 6/7 [00:00<00:00, 26.66it/s, loss=0.934, v_num=16, val_loss=
Validating: 0it [00:00, ?it/s]
Epoch 9: 100%|█| 7/7 [00:00<00:00, 28.39it/s, loss=0.934, v_num=16, val_loss=
Epoch 10: 86%|▊| 6/7 [00:00<00:00, 34.48it/s, loss=0.847, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 10: 100%|█| 7/7 [00:00<00:00, 37.13it/s, loss=0.847, v_num=16, val_loss
Epoch 11: 86%|▊| 6/7 [00:00<00:00, 29.77it/s, loss=0.73, v_num=16, val_loss=
Validating: 0it [00:00, ?it/s]
Epoch 11: 100%|█| 7/7 [00:00<00:00, 31.96it/s, loss=0.73, v_num=16, val_loss=
Epoch 12: 86%|▊| 6/7 [00:00<00:00, 29.19it/s, loss=0.613, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 12: 100%|█| 7/7 [00:00<00:00, 31.17it/s, loss=0.613, v_num=16, val_loss
Epoch 13: 86%|▊| 6/7 [00:00<00:00, 31.74it/s, loss=0.517, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 13: 100%|█| 7/7 [00:00<00:00, 34.39it/s, loss=0.517, v_num=16, val_loss
Epoch 14: 86%|▊| 6/7 [00:00<00:00, 34.68it/s, loss=0.456, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 14: 100%|█| 7/7 [00:00<00:00, 37.33it/s, loss=0.456, v_num=16, val_loss
Epoch 15: 86%|▊| 6/7 [00:00<00:00, 33.70it/s, loss=0.429, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 15: 100%|█| 7/7 [00:00<00:00, 36.36it/s, loss=0.429, v_num=16, val_loss
Epoch 16: 86%|▊| 6/7 [00:00<00:00, 30.76it/s, loss=0.4, v_num=16, val_loss=1
Validating: 0it [00:00, ?it/s]
Epoch 16: 100%|█| 7/7 [00:00<00:00, 33.01it/s, loss=0.4, v_num=16, val_loss=1
Epoch 17: 86%|▊| 6/7 [00:00<00:00, 32.96it/s, loss=0.379, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 17: 100%|█| 7/7 [00:00<00:00, 35.17it/s, loss=0.379, v_num=16, val_loss
Epoch 18: 86%|▊| 6/7 [00:00<00:00, 27.46it/s, loss=0.361, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 18: 100%|█| 7/7 [00:00<00:00, 29.53it/s, loss=0.361, v_num=16, val_loss
Epoch 19: 86%|▊| 6/7 [00:00<00:00, 31.41it/s, loss=0.343, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 19: 100%|█| 7/7 [00:00<00:00, 33.57it/s, loss=0.343, v_num=16, val_loss
Epoch 20: 86%|▊| 6/7 [00:00<00:00, 31.08it/s, loss=0.325, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 20: 100%|█| 7/7 [00:00<00:00, 33.49it/s, loss=0.325, v_num=16, val_loss
Epoch 21: 86%|▊| 6/7 [00:00<00:00, 27.58it/s, loss=0.312, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 21: 100%|█| 7/7 [00:00<00:00, 29.97it/s, loss=0.312, v_num=16, val_loss
Epoch 22: 86%|▊| 6/7 [00:00<00:00, 31.24it/s, loss=0.302, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 22: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.302, v_num=16, val_loss
Epoch 23: 86%|▊| 6/7 [00:00<00:00, 29.19it/s, loss=0.292, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 23: 100%|█| 7/7 [00:00<00:00, 31.38it/s, loss=0.292, v_num=16, val_loss
Epoch 24: 86%|▊| 6/7 [00:00<00:00, 30.61it/s, loss=0.281, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 24: 100%|█| 7/7 [00:00<00:00, 31.96it/s, loss=0.281, v_num=16, val_loss
Epoch 25: 86%|▊| 6/7 [00:00<00:00, 30.15it/s, loss=0.272, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 25: 100%|█| 7/7 [00:00<00:00, 32.48it/s, loss=0.272, v_num=16, val_loss
Epoch 26: 86%|▊| 6/7 [00:00<00:00, 26.60it/s, loss=0.262, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 26: 100%|█| 7/7 [00:00<00:00, 28.62it/s, loss=0.262, v_num=16, val_loss
Epoch 27: 86%|▊| 6/7 [00:00<00:00, 28.30it/s, loss=0.255, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 27: 100%|█| 7/7 [00:00<00:00, 29.91it/s, loss=0.255, v_num=16, val_loss
Epoch 28: 86%|▊| 6/7 [00:00<00:00, 25.15it/s, loss=0.248, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 28: 100%|█| 7/7 [00:00<00:00, 26.61it/s, loss=0.248, v_num=16, val_loss
Epoch 29: 86%|▊| 6/7 [00:00<00:00, 24.04it/s, loss=0.245, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 29: 100%|█| 7/7 [00:00<00:00, 25.40it/s, loss=0.245, v_num=16, val_loss
Epoch 30: 86%|▊| 6/7 [00:00<00:00, 25.31it/s, loss=0.248, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 30: 100%|█| 7/7 [00:00<00:00, 27.13it/s, loss=0.248, v_num=16, val_loss
Epoch 31: 86%|▊| 6/7 [00:00<00:00, 27.33it/s, loss=0.244, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 31: 100%|█| 7/7 [00:00<00:00, 28.98it/s, loss=0.244, v_num=16, val_loss
Epoch 32: 86%|▊| 6/7 [00:00<00:00, 28.43it/s, loss=0.24, v_num=16, val_loss=
Validating: 0it [00:00, ?it/s]
Epoch 32: 100%|█| 7/7 [00:00<00:00, 30.63it/s, loss=0.24, v_num=16, val_loss=
Epoch 33: 86%|▊| 6/7 [00:00<00:00, 28.43it/s, loss=0.234, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 33: 100%|█| 7/7 [00:00<00:00, 30.23it/s, loss=0.234, v_num=16, val_loss
Epoch 34: 86%|▊| 6/7 [00:00<00:00, 27.14it/s, loss=0.227, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 34: 100%|█| 7/7 [00:00<00:00, 29.04it/s, loss=0.227, v_num=16, val_loss
Epoch 35: 86%|▊| 6/7 [00:00<00:00, 27.21it/s, loss=0.225, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 35: 100%|█| 7/7 [00:00<00:00, 29.22it/s, loss=0.225, v_num=16, val_loss
Epoch 36: 86%|▊| 6/7 [00:00<00:00, 27.90it/s, loss=0.223, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 36: 100%|█| 7/7 [00:00<00:00, 29.97it/s, loss=0.223, v_num=16, val_loss
Epoch 37: 86%|▊| 6/7 [00:00<00:00, 30.53it/s, loss=0.225, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 37: 100%|█| 7/7 [00:00<00:00, 32.48it/s, loss=0.225, v_num=16, val_loss
Epoch 38: 86%|▊| 6/7 [00:00<00:00, 26.03it/s, loss=0.227, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 38: 100%|█| 7/7 [00:00<00:00, 28.00it/s, loss=0.227, v_num=16, val_loss
Epoch 39: 86%|▊| 6/7 [00:00<00:00, 27.27it/s, loss=0.226, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 39: 100%|█| 7/7 [00:00<00:00, 28.80it/s, loss=0.226, v_num=16, val_loss
Epoch 40: 86%|▊| 6/7 [00:00<00:00, 25.69it/s, loss=0.223, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 40: 100%|█| 7/7 [00:00<00:00, 27.72it/s, loss=0.223, v_num=16, val_loss
Epoch 41: 86%|▊| 6/7 [00:00<00:00, 27.14it/s, loss=0.218, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 41: 100%|█| 7/7 [00:00<00:00, 29.22it/s, loss=0.218, v_num=16, val_loss
Epoch 42: 86%|▊| 6/7 [00:00<00:00, 30.84it/s, loss=0.215, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 42: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.215, v_num=16, val_loss
Epoch 43: 86%|▊| 6/7 [00:00<00:00, 26.49it/s, loss=0.218, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 43: 100%|█| 7/7 [00:00<00:00, 28.57it/s, loss=0.218, v_num=16, val_loss
Epoch 44: 86%|▊| 6/7 [00:00<00:00, 28.98it/s, loss=0.219, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 44: 100%|█| 7/7 [00:00<00:00, 31.31it/s, loss=0.219, v_num=16, val_loss
Epoch 45: 86%|▊| 6/7 [00:00<00:00, 28.30it/s, loss=0.219, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 45: 100%|█| 7/7 [00:00<00:00, 30.10it/s, loss=0.219, v_num=16, val_loss
Epoch 46: 86%|▊| 6/7 [00:00<00:00, 31.33it/s, loss=0.216, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 46: 100%|█| 7/7 [00:00<00:00, 33.73it/s, loss=0.216, v_num=16, val_loss
Epoch 47: 86%|▊| 6/7 [00:00<00:00, 27.90it/s, loss=0.214, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 47: 100%|█| 7/7 [00:00<00:00, 29.47it/s, loss=0.214, v_num=16, val_loss
Epoch 48: 86%|▊| 6/7 [00:00<00:00, 29.62it/s, loss=0.212, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 48: 100%|█| 7/7 [00:00<00:00, 31.74it/s, loss=0.212, v_num=16, val_loss
Epoch 49: 86%|▊| 6/7 [00:00<00:00, 29.12it/s, loss=0.211, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 49: 100%|█| 7/7 [00:00<00:00, 31.31it/s, loss=0.211, v_num=16, val_loss
Epoch 50: 86%|▊| 6/7 [00:00<00:00, 25.86it/s, loss=0.21, v_num=16, val_loss=
Validating: 0it [00:00, ?it/s]
Epoch 50: 100%|█| 7/7 [00:00<00:00, 27.77it/s, loss=0.21, v_num=16, val_loss=
Epoch 51: 86%|▊| 6/7 [00:00<00:00, 27.77it/s, loss=0.209, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 51: 100%|█| 7/7 [00:00<00:00, 30.23it/s, loss=0.209, v_num=16, val_loss
Epoch 52: 86%|▊| 6/7 [00:00<00:00, 31.08it/s, loss=0.208, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 52: 100%|█| 7/7 [00:00<00:00, 33.49it/s, loss=0.208, v_num=16, val_loss
Epoch 53: 86%|▊| 6/7 [00:00<00:00, 31.08it/s, loss=0.208, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 53: 100%|█| 7/7 [00:00<00:00, 33.57it/s, loss=0.208, v_num=16, val_loss
Epoch 54: 86%|▊| 6/7 [00:00<00:00, 28.57it/s, loss=0.213, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 54: 100%|█| 7/7 [00:00<00:00, 30.76it/s, loss=0.213, v_num=16, val_loss
Epoch 55: 86%|▊| 6/7 [00:00<00:00, 31.99it/s, loss=0.212, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 55: 100%|█| 7/7 [00:00<00:00, 34.39it/s, loss=0.212, v_num=16, val_loss
Epoch 56: 86%|▊| 6/7 [00:00<00:00, 30.61it/s, loss=0.21, v_num=16, val_loss=
Validating: 0it [00:00, ?it/s]
Epoch 56: 100%|█| 7/7 [00:00<00:00, 32.63it/s, loss=0.21, v_num=16, val_loss=
Epoch 57: 86%|▊| 6/7 [00:00<00:00, 30.76it/s, loss=0.208, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 57: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.208, v_num=16, val_loss
Epoch 58: 86%|▊| 6/7 [00:00<00:00, 30.76it/s, loss=0.205, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 58: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.205, v_num=16, val_loss
Epoch 59: 86%|▊| 6/7 [00:00<00:00, 30.45it/s, loss=0.208, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 59: 100%|█| 7/7 [00:00<00:00, 33.01it/s, loss=0.208, v_num=16, val_loss
Epoch 60: 86%|▊| 6/7 [00:00<00:00, 27.33it/s, loss=0.211, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 60: 100%|█| 7/7 [00:00<00:00, 29.66it/s, loss=0.211, v_num=16, val_loss
Epoch 61: 86%|▊| 6/7 [00:00<00:00, 28.98it/s, loss=0.211, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 61: 100%|█| 7/7 [00:00<00:00, 31.53it/s, loss=0.211, v_num=16, val_loss
Epoch 62: 86%|▊| 6/7 [00:00<00:00, 32.34it/s, loss=0.209, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 62: 100%|█| 7/7 [00:00<00:00, 34.99it/s, loss=0.209, v_num=16, val_loss
Epoch 63: 86%|▊| 6/7 [00:00<00:00, 28.36it/s, loss=0.207, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 63: 100%|█| 7/7 [00:00<00:00, 30.76it/s, loss=0.207, v_num=16, val_loss
Epoch 64: 86%|▊| 6/7 [00:00<00:00, 29.33it/s, loss=0.205, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 64: 100%|█| 7/7 [00:00<00:00, 31.89it/s, loss=0.205, v_num=16, val_loss
Epoch 65: 86%|▊| 6/7 [00:00<00:00, 30.69it/s, loss=0.207, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 65: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.207, v_num=16, val_loss
Epoch 66: 86%|▊| 6/7 [00:00<00:00, 31.74it/s, loss=0.207, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 66: 100%|█| 7/7 [00:00<00:00, 34.48it/s, loss=0.207, v_num=16, val_loss
Epoch 67: 86%|▊| 6/7 [00:00<00:00, 31.57it/s, loss=0.208, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 67: 100%|█| 7/7 [00:00<00:00, 33.89it/s, loss=0.208, v_num=16, val_loss
Epoch 68: 86%|▊| 6/7 [00:00<00:00, 28.36it/s, loss=0.208, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 68: 100%|█| 7/7 [00:00<00:00, 30.10it/s, loss=0.208, v_num=16, val_loss
Epoch 69: 86%|▊| 6/7 [00:00<00:00, 28.10it/s, loss=0.204, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 69: 100%|█| 7/7 [00:00<00:00, 30.50it/s, loss=0.204, v_num=16, val_loss
Epoch 70: 86%|▊| 6/7 [00:00<00:00, 32.17it/s, loss=0.202, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 70: 100%|█| 7/7 [00:00<00:00, 34.82it/s, loss=0.202, v_num=16, val_loss
Epoch 71: 86%|▊| 6/7 [00:00<00:00, 31.24it/s, loss=0.198, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 71: 100%|█| 7/7 [00:00<00:00, 33.41it/s, loss=0.198, v_num=16, val_loss
Epoch 72: 86%|▊| 6/7 [00:00<00:00, 31.49it/s, loss=0.2, v_num=16, val_loss=1
Validating: 0it [00:00, ?it/s]
Epoch 72: 100%|█| 7/7 [00:00<00:00, 33.97it/s, loss=0.2, v_num=16, val_loss=1
Epoch 73: 86%|▊| 6/7 [00:00<00:00, 29.99it/s, loss=0.203, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 73: 100%|█| 7/7 [00:00<00:00, 32.03it/s, loss=0.203, v_num=16, val_loss
Epoch 74: 86%|▊| 6/7 [00:00<00:00, 27.27it/s, loss=0.206, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 74: 100%|█| 7/7 [00:00<00:00, 28.98it/s, loss=0.206, v_num=16, val_loss
Epoch 75: 86%|▊| 6/7 [00:00<00:00, 28.03it/s, loss=0.208, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 75: 100%|█| 7/7 [00:00<00:00, 30.50it/s, loss=0.208, v_num=16, val_loss
Epoch 76: 86%|▊| 6/7 [00:00<00:00, 32.08it/s, loss=0.204, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 76: 100%|█| 7/7 [00:00<00:00, 34.65it/s, loss=0.204, v_num=16, val_loss
Epoch 77: 86%|▊| 6/7 [00:00<00:00, 30.92it/s, loss=0.204, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 77: 100%|█| 7/7 [00:00<00:00, 33.33it/s, loss=0.204, v_num=16, val_loss
Epoch 78: 86%|▊| 6/7 [00:00<00:00, 29.85it/s, loss=0.204, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 78: 100%|█| 7/7 [00:00<00:00, 32.40it/s, loss=0.204, v_num=16, val_loss
Epoch 79: 86%|▊| 6/7 [00:00<00:00, 32.60it/s, loss=0.204, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 79: 100%|█| 7/7 [00:00<00:00, 35.26it/s, loss=0.204, v_num=16, val_loss
Epoch 80: 86%|▊| 6/7 [00:00<00:00, 33.42it/s, loss=0.204, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 80: 100%|█| 7/7 [00:00<00:00, 34.91it/s, loss=0.204, v_num=16, val_loss
Epoch 81: 86%|▊| 6/7 [00:00<00:00, 28.30it/s, loss=0.202, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 81: 100%|█| 7/7 [00:00<00:00, 30.90it/s, loss=0.202, v_num=16, val_loss
Epoch 82: 86%|▊| 6/7 [00:00<00:00, 30.45it/s, loss=0.2, v_num=16, val_loss=1
Validating: 0it [00:00, ?it/s]
Epoch 82: 100%|█| 7/7 [00:00<00:00, 32.86it/s, loss=0.2, v_num=16, val_loss=1
Epoch 83: 86%|▊| 6/7 [00:00<00:00, 29.33it/s, loss=0.2, v_num=16, val_loss=1
Validating: 0it [00:00, ?it/s]
Epoch 83: 100%|█| 7/7 [00:00<00:00, 31.96it/s, loss=0.2, v_num=16, val_loss=1
Epoch 84: 86%|▊| 6/7 [00:00<00:00, 31.82it/s, loss=0.2, v_num=16, val_loss=1
Validating: 0it [00:00, ?it/s]
Epoch 84: 100%|█| 7/7 [00:00<00:00, 34.14it/s, loss=0.2, v_num=16, val_loss=1
Epoch 85: 86%|▊| 6/7 [00:00<00:00, 31.33it/s, loss=0.203, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 85: 100%|█| 7/7 [00:00<00:00, 33.73it/s, loss=0.203, v_num=16, val_loss
Epoch 86: 86%|▊| 6/7 [00:00<00:00, 29.26it/s, loss=0.203, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 86: 100%|█| 7/7 [00:00<00:00, 31.89it/s, loss=0.203, v_num=16, val_loss
Epoch 87: 86%|▊| 6/7 [00:00<00:00, 28.84it/s, loss=0.205, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 87: 100%|█| 7/7 [00:00<00:00, 31.24it/s, loss=0.205, v_num=16, val_loss
Epoch 88: 86%|▊| 6/7 [00:00<00:00, 31.24it/s, loss=0.203, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 88: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.203, v_num=16, val_loss
Epoch 89: 86%|▊| 6/7 [00:00<00:00, 29.19it/s, loss=0.201, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 89: 100%|█| 7/7 [00:00<00:00, 31.38it/s, loss=0.201, v_num=16, val_loss
Epoch 90: 86%|▊| 6/7 [00:00<00:00, 28.03it/s, loss=0.2, v_num=16, val_loss=1
Validating: 0it [00:00, ?it/s]
Epoch 90: 100%|█| 7/7 [00:00<00:00, 29.97it/s, loss=0.2, v_num=16, val_loss=1
Epoch 91: 86%|▊| 6/7 [00:00<00:00, 30.07it/s, loss=0.199, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 91: 100%|█| 7/7 [00:00<00:00, 32.33it/s, loss=0.199, v_num=16, val_loss
Epoch 92: 86%|▊| 6/7 [00:00<00:00, 27.71it/s, loss=0.202, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 92: 100%|█| 7/7 [00:00<00:00, 30.17it/s, loss=0.202, v_num=16, val_loss
Epoch 93: 86%|▊| 6/7 [00:00<00:00, 31.82it/s, loss=0.201, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 93: 100%|█| 7/7 [00:00<00:00, 34.06it/s, loss=0.201, v_num=16, val_loss
Epoch 94: 86%|▊| 6/7 [00:00<00:00, 29.33it/s, loss=0.201, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 94: 100%|█| 7/7 [00:00<00:00, 31.89it/s, loss=0.201, v_num=16, val_loss
Epoch 95: 86%|▊| 6/7 [00:00<00:00, 30.92it/s, loss=0.2, v_num=16, val_loss=1
Validating: 0it [00:00, ?it/s]
Epoch 95: 100%|█| 7/7 [00:00<00:00, 33.41it/s, loss=0.2, v_num=16, val_loss=1
Epoch 96: 86%|▊| 6/7 [00:00<00:00, 32.60it/s, loss=0.202, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 96: 100%|█| 7/7 [00:00<00:00, 34.82it/s, loss=0.202, v_num=16, val_loss
Epoch 97: 86%|▊| 6/7 [00:00<00:00, 27.97it/s, loss=0.201, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 97: 100%|█| 7/7 [00:00<00:00, 29.97it/s, loss=0.201, v_num=16, val_loss
Epoch 98: 86%|▊| 6/7 [00:00<00:00, 31.00it/s, loss=0.202, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 98: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.202, v_num=16, val_loss
Epoch 99: 86%|▊| 6/7 [00:00<00:00, 30.07it/s, loss=0.198, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 99: 100%|█| 7/7 [00:00<00:00, 31.96it/s, loss=0.198, v_num=16, val_loss
Epoch 100: 86%|▊| 6/7 [00:00<00:00, 32.08it/s, loss=0.195, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 100: 100%|█| 7/7 [00:00<00:00, 33.65it/s, loss=0.195, v_num=16, val_los
Epoch 101: 86%|▊| 6/7 [00:00<00:00, 29.33it/s, loss=0.194, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 101: 100%|█| 7/7 [00:00<00:00, 31.67it/s, loss=0.194, v_num=16, val_los
Epoch 102: 86%|▊| 6/7 [00:00<00:00, 30.30it/s, loss=0.197, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 102: 100%|█| 7/7 [00:00<00:00, 31.60it/s, loss=0.197, v_num=16, val_los
Epoch 103: 86%|▊| 6/7 [00:00<00:00, 29.55it/s, loss=0.199, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 103: 100%|█| 7/7 [00:00<00:00, 31.96it/s, loss=0.199, v_num=16, val_los
Epoch 104: 86%|▊| 6/7 [00:00<00:00, 29.85it/s, loss=0.197, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 104: 100%|█| 7/7 [00:00<00:00, 32.25it/s, loss=0.197, v_num=16, val_los
Epoch 105: 86%|▊| 6/7 [00:00<00:00, 25.91it/s, loss=0.194, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 105: 100%|█| 7/7 [00:00<00:00, 28.22it/s, loss=0.194, v_num=16, val_los
Epoch 106: 86%|▊| 6/7 [00:00<00:00, 29.92it/s, loss=0.194, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 106: 100%|█| 7/7 [00:00<00:00, 31.96it/s, loss=0.194, v_num=16, val_los
Epoch 107: 86%|▊| 6/7 [00:00<00:00, 27.97it/s, loss=0.197, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 107: 100%|█| 7/7 [00:00<00:00, 30.23it/s, loss=0.197, v_num=16, val_los
Epoch 108: 86%|▊| 6/7 [00:00<00:00, 27.64it/s, loss=0.196, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 108: 100%|█| 7/7 [00:00<00:00, 29.66it/s, loss=0.196, v_num=16, val_los
Epoch 109: 86%|▊| 6/7 [00:00<00:00, 28.84it/s, loss=0.193, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 109: 100%|█| 7/7 [00:00<00:00, 30.83it/s, loss=0.193, v_num=16, val_los
Epoch 110: 86%|▊| 6/7 [00:00<00:00, 27.90it/s, loss=0.192, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 110: 100%|█| 7/7 [00:00<00:00, 29.91it/s, loss=0.192, v_num=16, val_los
Epoch 111: 86%|▊| 6/7 [00:00<00:00, 29.62it/s, loss=0.189, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 111: 100%|█| 7/7 [00:00<00:00, 32.03it/s, loss=0.189, v_num=16, val_los
Epoch 112: 86%|▊| 6/7 [00:00<00:00, 29.33it/s, loss=0.192, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 112: 100%|█| 7/7 [00:00<00:00, 32.18it/s, loss=0.192, v_num=16, val_los
Epoch 113: 86%|▊| 6/7 [00:00<00:00, 28.16it/s, loss=0.194, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 113: 100%|█| 7/7 [00:00<00:00, 30.36it/s, loss=0.194, v_num=16, val_los
Epoch 114: 86%|▊| 6/7 [00:00<00:00, 32.51it/s, loss=0.194, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 114: 100%|█| 7/7 [00:00<00:00, 35.17it/s, loss=0.194, v_num=16, val_los
Epoch 115: 86%|▊| 6/7 [00:00<00:00, 31.99it/s, loss=0.192, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 115: 100%|█| 7/7 [00:00<00:00, 34.31it/s, loss=0.192, v_num=16, val_los
Epoch 116: 86%|▊| 6/7 [00:00<00:00, 33.42it/s, loss=0.191, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 116: 100%|█| 7/7 [00:00<00:00, 35.89it/s, loss=0.191, v_num=16, val_los
Epoch 117: 86%|▊| 6/7 [00:00<00:00, 33.42it/s, loss=0.192, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 117: 100%|█| 7/7 [00:00<00:00, 35.71it/s, loss=0.192, v_num=16, val_los
Epoch 118: 86%|▊| 6/7 [00:00<00:00, 33.51it/s, loss=0.191, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 118: 100%|█| 7/7 [00:00<00:00, 35.98it/s, loss=0.191, v_num=16, val_los
Epoch 119: 86%|▊| 6/7 [00:00<00:00, 32.96it/s, loss=0.19, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 119: 100%|█| 7/7 [00:00<00:00, 35.62it/s, loss=0.19, v_num=16, val_loss
Epoch 120: 86%|▊| 6/7 [00:00<00:00, 28.91it/s, loss=0.188, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 120: 100%|█| 7/7 [00:00<00:00, 30.56it/s, loss=0.188, v_num=16, val_los
Epoch 121: 86%|▊| 6/7 [00:00<00:00, 29.77it/s, loss=0.185, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 121: 100%|█| 7/7 [00:00<00:00, 32.33it/s, loss=0.185, v_num=16, val_los
Epoch 122: 86%|▊| 6/7 [00:00<00:00, 31.82it/s, loss=0.186, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 122: 100%|█| 7/7 [00:00<00:00, 34.48it/s, loss=0.186, v_num=16, val_los
Epoch 123: 86%|▊| 6/7 [00:00<00:00, 31.66it/s, loss=0.187, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 123: 100%|█| 7/7 [00:00<00:00, 33.97it/s, loss=0.187, v_num=16, val_los
Epoch 124: 86%|▊| 6/7 [00:00<00:00, 30.92it/s, loss=0.19, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 124: 100%|█| 7/7 [00:00<00:00, 33.25it/s, loss=0.19, v_num=16, val_loss
Epoch 125: 86%|▊| 6/7 [00:00<00:00, 32.87it/s, loss=0.191, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 125: 100%|█| 7/7 [00:00<00:00, 34.99it/s, loss=0.191, v_num=16, val_los
Epoch 126: 86%|▊| 6/7 [00:00<00:00, 31.66it/s, loss=0.187, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 126: 100%|█| 7/7 [00:00<00:00, 34.06it/s, loss=0.187, v_num=16, val_los
Epoch 127: 86%|▊| 6/7 [00:00<00:00, 31.08it/s, loss=0.184, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 127: 100%|█| 7/7 [00:00<00:00, 33.89it/s, loss=0.184, v_num=16, val_los
Epoch 128: 86%|▊| 6/7 [00:00<00:00, 32.17it/s, loss=0.182, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 128: 100%|█| 7/7 [00:00<00:00, 34.56it/s, loss=0.182, v_num=16, val_los
Epoch 129: 86%|▊| 6/7 [00:00<00:00, 30.53it/s, loss=0.181, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 129: 100%|█| 7/7 [00:00<00:00, 32.48it/s, loss=0.181, v_num=16, val_los
Epoch 130: 86%|▊| 6/7 [00:00<00:00, 32.87it/s, loss=0.185, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 130: 100%|█| 7/7 [00:00<00:00, 35.17it/s, loss=0.185, v_num=16, val_los
Epoch 131: 86%|▊| 6/7 [00:00<00:00, 31.82it/s, loss=0.185, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 131: 100%|█| 7/7 [00:00<00:00, 34.31it/s, loss=0.185, v_num=16, val_los
Epoch 132: 86%|▊| 6/7 [00:00<00:00, 32.34it/s, loss=0.182, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 132: 100%|█| 7/7 [00:00<00:00, 34.56it/s, loss=0.182, v_num=16, val_los
Epoch 133: 86%|▊| 6/7 [00:00<00:00, 29.62it/s, loss=0.179, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 133: 100%|█| 7/7 [00:00<00:00, 32.18it/s, loss=0.179, v_num=16, val_los
Epoch 134: 86%|▊| 6/7 [00:00<00:00, 31.74it/s, loss=0.177, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 134: 100%|█| 7/7 [00:00<00:00, 33.97it/s, loss=0.177, v_num=16, val_los
Epoch 135: 86%|▊| 6/7 [00:00<00:00, 31.66it/s, loss=0.179, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 135: 100%|█| 7/7 [00:00<00:00, 34.14it/s, loss=0.179, v_num=16, val_los
Epoch 136: 86%|▊| 6/7 [00:00<00:00, 31.82it/s, loss=0.181, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 136: 100%|█| 7/7 [00:00<00:00, 33.97it/s, loss=0.181, v_num=16, val_los
Epoch 137: 86%|▊| 6/7 [00:00<00:00, 29.33it/s, loss=0.183, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 137: 100%|█| 7/7 [00:00<00:00, 31.96it/s, loss=0.183, v_num=16, val_los
Epoch 138: 86%|▊| 6/7 [00:00<00:00, 32.17it/s, loss=0.183, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 138: 100%|█| 7/7 [00:00<00:00, 34.82it/s, loss=0.183, v_num=16, val_los
Epoch 139: 86%|▊| 6/7 [00:00<00:00, 33.33it/s, loss=0.182, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 139: 100%|█| 7/7 [00:00<00:00, 35.89it/s, loss=0.182, v_num=16, val_los
Epoch 140: 86%|▊| 6/7 [00:00<00:00, 32.60it/s, loss=0.178, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 140: 100%|█| 7/7 [00:00<00:00, 34.73it/s, loss=0.178, v_num=16, val_los
Epoch 141: 86%|▊| 6/7 [00:00<00:00, 31.74it/s, loss=0.175, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 141: 100%|█| 7/7 [00:00<00:00, 34.22it/s, loss=0.175, v_num=16, val_los
Epoch 142: 86%|▊| 6/7 [00:00<00:00, 32.08it/s, loss=0.174, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 142: 100%|█| 7/7 [00:00<00:00, 34.56it/s, loss=0.174, v_num=16, val_los
Epoch 143: 86%|▊| 6/7 [00:00<00:00, 34.28it/s, loss=0.173, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 143: 100%|█| 7/7 [00:00<00:00, 36.45it/s, loss=0.173, v_num=16, val_los
Epoch 144: 86%|▊| 6/7 [00:00<00:00, 30.61it/s, loss=0.175, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 144: 100%|█| 7/7 [00:00<00:00, 32.25it/s, loss=0.175, v_num=16, val_los
Epoch 145: 86%|▊| 6/7 [00:00<00:00, 30.76it/s, loss=0.18, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 145: 100%|█| 7/7 [00:00<00:00, 32.70it/s, loss=0.18, v_num=16, val_loss
Epoch 146: 86%|▊| 6/7 [00:00<00:00, 33.80it/s, loss=0.179, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 146: 100%|█| 7/7 [00:00<00:00, 36.26it/s, loss=0.179, v_num=16, val_los
Epoch 147: 86%|▊| 6/7 [00:00<00:00, 29.41it/s, loss=0.177, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 147: 100%|█| 7/7 [00:00<00:00, 31.46it/s, loss=0.177, v_num=16, val_los
Epoch 148: 86%|▊| 6/7 [00:00<00:00, 34.18it/s, loss=0.173, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 148: 100%|█| 7/7 [00:00<00:00, 36.45it/s, loss=0.173, v_num=16, val_los
Epoch 149: 86%|▊| 6/7 [00:00<00:00, 31.49it/s, loss=0.17, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 149: 100%|█| 7/7 [00:00<00:00, 33.73it/s, loss=0.17, v_num=16, val_loss
Epoch 150: 86%|▊| 6/7 [00:00<00:00, 33.14it/s, loss=0.17, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 150: 100%|█| 7/7 [00:00<00:00, 35.17it/s, loss=0.17, v_num=16, val_loss
Epoch 151: 86%|▊| 6/7 [00:00<00:00, 31.82it/s, loss=0.174, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 151: 100%|█| 7/7 [00:00<00:00, 34.06it/s, loss=0.174, v_num=16, val_los
Epoch 152: 86%|▊| 6/7 [00:00<00:00, 34.88it/s, loss=0.173, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 152: 100%|█| 7/7 [00:00<00:00, 37.63it/s, loss=0.173, v_num=16, val_los
Epoch 153: 86%|▊| 6/7 [00:00<00:00, 35.60it/s, loss=0.172, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 153: 100%|█| 7/7 [00:00<00:00, 38.35it/s, loss=0.172, v_num=16, val_los
Epoch 154: 86%|▊| 6/7 [00:00<00:00, 33.33it/s, loss=0.168, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 154: 100%|█| 7/7 [00:00<00:00, 35.98it/s, loss=0.168, v_num=16, val_los
Epoch 155: 86%|▊| 6/7 [00:00<00:00, 31.33it/s, loss=0.165, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 155: 100%|█| 7/7 [00:00<00:00, 32.78it/s, loss=0.165, v_num=16, val_los
Epoch 156: 86%|▊| 6/7 [00:00<00:00, 26.96it/s, loss=0.167, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 156: 100%|█| 7/7 [00:00<00:00, 28.74it/s, loss=0.167, v_num=16, val_los
Epoch 157: 86%|▊| 6/7 [00:00<00:00, 31.08it/s, loss=0.17, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 157: 100%|█| 7/7 [00:00<00:00, 33.73it/s, loss=0.17, v_num=16, val_loss
Epoch 158: 86%|▊| 6/7 [00:00<00:00, 34.28it/s, loss=0.171, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 158: 100%|█| 7/7 [00:00<00:00, 36.64it/s, loss=0.171, v_num=16, val_los
Epoch 159: 86%|▊| 6/7 [00:00<00:00, 34.48it/s, loss=0.168, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 159: 100%|█| 7/7 [00:00<00:00, 37.03it/s, loss=0.168, v_num=16, val_los
Epoch 160: 86%|▊| 6/7 [00:00<00:00, 32.87it/s, loss=0.165, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 160: 100%|█| 7/7 [00:00<00:00, 35.26it/s, loss=0.165, v_num=16, val_los
Epoch 161: 86%|▊| 6/7 [00:00<00:00, 32.43it/s, loss=0.164, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 161: 100%|█| 7/7 [00:00<00:00, 35.26it/s, loss=0.164, v_num=16, val_los
Epoch 162: 86%|▊| 6/7 [00:00<00:00, 33.99it/s, loss=0.167, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 162: 100%|█| 7/7 [00:00<00:00, 36.36it/s, loss=0.167, v_num=16, val_los
Epoch 163: 86%|▊| 6/7 [00:00<00:00, 33.42it/s, loss=0.168, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 163: 100%|█| 7/7 [00:00<00:00, 35.53it/s, loss=0.168, v_num=16, val_los
Epoch 164: 86%|▊| 6/7 [00:00<00:00, 33.99it/s, loss=0.169, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 164: 100%|█| 7/7 [00:00<00:00, 36.84it/s, loss=0.169, v_num=16, val_los
Epoch 165: 86%|▊| 6/7 [00:00<00:00, 33.14it/s, loss=0.167, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 165: 100%|█| 7/7 [00:00<00:00, 35.44it/s, loss=0.167, v_num=16, val_los
Epoch 166: 86%|▊| 6/7 [00:00<00:00, 31.49it/s, loss=0.161, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 166: 100%|█| 7/7 [00:00<00:00, 34.31it/s, loss=0.161, v_num=16, val_los
Epoch 167: 86%|▊| 6/7 [00:00<00:00, 33.61it/s, loss=0.162, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 167: 100%|█| 7/7 [00:00<00:00, 36.17it/s, loss=0.162, v_num=16, val_los
Epoch 168: 86%|▊| 6/7 [00:00<00:00, 33.61it/s, loss=0.162, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 168: 100%|█| 7/7 [00:00<00:00, 35.62it/s, loss=0.162, v_num=16, val_los
Epoch 169: 86%|▊| 6/7 [00:00<00:00, 29.34it/s, loss=0.164, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 169: 100%|█| 7/7 [00:00<00:00, 31.53it/s, loss=0.164, v_num=16, val_los
Epoch 170: 86%|▊| 6/7 [00:00<00:00, 30.69it/s, loss=0.165, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 170: 100%|█| 7/7 [00:00<00:00, 32.94it/s, loss=0.165, v_num=16, val_los
Epoch 171: 86%|▊| 6/7 [00:00<00:00, 32.17it/s, loss=0.163, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 171: 100%|█| 7/7 [00:00<00:00, 35.08it/s, loss=0.163, v_num=16, val_los
Epoch 172: 86%|▊| 6/7 [00:00<00:00, 36.80it/s, loss=0.161, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 172: 100%|█| 7/7 [00:00<00:00, 39.32it/s, loss=0.161, v_num=16, val_los
Epoch 173: 86%|▊| 6/7 [00:00<00:00, 36.25it/s, loss=0.157, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 173: 100%|█| 7/7 [00:00<00:00, 39.10it/s, loss=0.157, v_num=16, val_los
Epoch 174: 86%|▊| 6/7 [00:00<00:00, 30.76it/s, loss=0.158, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 174: 100%|█| 7/7 [00:00<00:00, 33.41it/s, loss=0.158, v_num=16, val_los
Epoch 175: 86%|▊| 6/7 [00:00<00:00, 31.49it/s, loss=0.161, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 175: 100%|█| 7/7 [00:00<00:00, 33.81it/s, loss=0.161, v_num=16, val_los
Epoch 176: 86%|▊| 6/7 [00:00<00:00, 33.70it/s, loss=0.159, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 176: 100%|█| 7/7 [00:00<00:00, 36.17it/s, loss=0.159, v_num=16, val_los
Epoch 177: 86%|▊| 6/7 [00:00<00:00, 34.38it/s, loss=0.156, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 177: 100%|█| 7/7 [00:00<00:00, 37.03it/s, loss=0.156, v_num=16, val_los
Epoch 178: 86%|▊| 6/7 [00:00<00:00, 31.00it/s, loss=0.153, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 178: 100%|█| 7/7 [00:00<00:00, 32.70it/s, loss=0.153, v_num=16, val_los
Epoch 179: 86%|▊| 6/7 [00:00<00:00, 33.99it/s, loss=0.149, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 179: 100%|█| 7/7 [00:00<00:00, 36.93it/s, loss=0.149, v_num=16, val_los
Epoch 180: 86%|▊| 6/7 [00:00<00:00, 33.05it/s, loss=0.152, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 180: 100%|█| 7/7 [00:00<00:00, 35.62it/s, loss=0.152, v_num=16, val_los
Epoch 181: 86%|▊| 6/7 [00:00<00:00, 33.99it/s, loss=0.156, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 181: 100%|█| 7/7 [00:00<00:00, 36.74it/s, loss=0.156, v_num=16, val_los
Epoch 182: 86%|▊| 6/7 [00:00<00:00, 32.08it/s, loss=0.156, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 182: 100%|█| 7/7 [00:00<00:00, 34.73it/s, loss=0.156, v_num=16, val_los
Epoch 183: 86%|▊| 6/7 [00:00<00:00, 31.00it/s, loss=0.154, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 183: 100%|█| 7/7 [00:00<00:00, 33.65it/s, loss=0.154, v_num=16, val_los
Epoch 184: 86%|▊| 6/7 [00:00<00:00, 34.68it/s, loss=0.151, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 184: 100%|█| 7/7 [00:00<00:00, 37.03it/s, loss=0.151, v_num=16, val_los
Epoch 185: 86%|▊| 6/7 [00:00<00:00, 37.03it/s, loss=0.147, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 185: 100%|█| 7/7 [00:00<00:00, 39.32it/s, loss=0.147, v_num=16, val_los
Epoch 186: 86%|▊| 6/7 [00:00<00:00, 34.08it/s, loss=0.15, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 186: 100%|█| 7/7 [00:00<00:00, 36.93it/s, loss=0.15, v_num=16, val_loss
Epoch 187: 86%|▊| 6/7 [00:00<00:00, 33.70it/s, loss=0.151, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 187: 100%|█| 7/7 [00:00<00:00, 36.45it/s, loss=0.151, v_num=16, val_los
Epoch 188: 86%|▊| 6/7 [00:00<00:00, 32.78it/s, loss=0.152, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 188: 100%|█| 7/7 [00:00<00:00, 35.53it/s, loss=0.152, v_num=16, val_los
Epoch 189: 86%|▊| 6/7 [00:00<00:00, 34.78it/s, loss=0.15, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 189: 100%|█| 7/7 [00:00<00:00, 37.63it/s, loss=0.15, v_num=16, val_loss
Epoch 190: 86%|▊| 6/7 [00:00<00:00, 32.43it/s, loss=0.147, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 190: 100%|█| 7/7 [00:00<00:00, 34.99it/s, loss=0.147, v_num=16, val_los
Epoch 191: 86%|▊| 6/7 [00:00<00:00, 32.96it/s, loss=0.146, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 191: 100%|█| 7/7 [00:00<00:00, 35.71it/s, loss=0.146, v_num=16, val_los
Epoch 192: 86%|▊| 6/7 [00:00<00:00, 34.08it/s, loss=0.148, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 192: 100%|█| 7/7 [00:00<00:00, 36.17it/s, loss=0.148, v_num=16, val_los
Epoch 193: 86%|▊| 6/7 [00:00<00:00, 30.84it/s, loss=0.147, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 193: 100%|█| 7/7 [00:00<00:00, 33.09it/s, loss=0.147, v_num=16, val_los
Epoch 194: 86%|▊| 6/7 [00:00<00:00, 34.58it/s, loss=0.144, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 194: 100%|█| 7/7 [00:00<00:00, 37.33it/s, loss=0.144, v_num=16, val_los
Epoch 195: 86%|▊| 6/7 [00:00<00:00, 35.60it/s, loss=0.146, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 195: 100%|█| 7/7 [00:00<00:00, 37.73it/s, loss=0.146, v_num=16, val_los
Epoch 196: 86%|▊| 6/7 [00:00<00:00, 31.24it/s, loss=0.143, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 196: 100%|█| 7/7 [00:00<00:00, 33.73it/s, loss=0.143, v_num=16, val_los
Epoch 197: 86%|▊| 6/7 [00:00<00:00, 34.09it/s, loss=0.143, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 197: 100%|█| 7/7 [00:00<00:00, 35.53it/s, loss=0.143, v_num=16, val_los
Epoch 198: 86%|▊| 6/7 [00:00<00:00, 31.99it/s, loss=0.144, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 198: 100%|█| 7/7 [00:00<00:00, 34.48it/s, loss=0.144, v_num=16, val_los
Epoch 199: 86%|▊| 6/7 [00:00<00:00, 32.51it/s, loss=0.144, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 199: 100%|█| 7/7 [00:00<00:00, 35.35it/s, loss=0.144, v_num=16, val_los
Epoch 200: 86%|▊| 6/7 [00:00<00:00, 33.33it/s, loss=0.144, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 200: 100%|█| 7/7 [00:00<00:00, 36.17it/s, loss=0.144, v_num=16, val_los
Epoch 201: 86%|▊| 6/7 [00:00<00:00, 33.51it/s, loss=0.146, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 201: 100%|█| 7/7 [00:00<00:00, 36.26it/s, loss=0.146, v_num=16, val_los
Epoch 202: 86%|▊| 6/7 [00:00<00:00, 29.70it/s, loss=0.143, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 202: 100%|█| 7/7 [00:00<00:00, 32.25it/s, loss=0.143, v_num=16, val_los
Epoch 203: 86%|▊| 6/7 [00:00<00:00, 29.85it/s, loss=0.14, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 203: 100%|█| 7/7 [00:00<00:00, 32.03it/s, loss=0.14, v_num=16, val_loss
Epoch 204: 86%|▊| 6/7 [00:00<00:00, 32.69it/s, loss=0.139, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 204: 100%|█| 7/7 [00:00<00:00, 35.44it/s, loss=0.139, v_num=16, val_los
Epoch 205: 86%|▊| 6/7 [00:00<00:00, 30.92it/s, loss=0.135, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 205: 100%|█| 7/7 [00:00<00:00, 32.86it/s, loss=0.135, v_num=16, val_los
Epoch 206: 86%|▊| 6/7 [00:00<00:00, 33.42it/s, loss=0.139, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 206: 100%|█| 7/7 [00:00<00:00, 34.99it/s, loss=0.139, v_num=16, val_los
Epoch 207: 86%|▊| 6/7 [00:00<00:00, 28.23it/s, loss=0.138, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 207: 100%|█| 7/7 [00:00<00:00, 30.30it/s, loss=0.138, v_num=16, val_los
Epoch 208: 86%|▊| 6/7 [00:00<00:00, 31.24it/s, loss=0.14, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 208: 100%|█| 7/7 [00:00<00:00, 33.01it/s, loss=0.14, v_num=16, val_loss
Epoch 209: 86%|▊| 6/7 [00:00<00:00, 30.30it/s, loss=0.14, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 209: 100%|█| 7/7 [00:00<00:00, 32.33it/s, loss=0.14, v_num=16, val_loss
Epoch 210: 86%|▊| 6/7 [00:00<00:00, 29.62it/s, loss=0.141, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 210: 100%|█| 7/7 [00:00<00:00, 32.03it/s, loss=0.141, v_num=16, val_los
Epoch 211: 86%|▊| 6/7 [00:00<00:00, 29.55it/s, loss=0.14, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 211: 100%|█| 7/7 [00:00<00:00, 32.03it/s, loss=0.14, v_num=16, val_loss
Epoch 212: 86%|▊| 6/7 [00:00<00:00, 28.36it/s, loss=0.142, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 212: 100%|█| 7/7 [00:00<00:00, 30.23it/s, loss=0.142, v_num=16, val_los
Epoch 213: 86%|▊| 6/7 [00:00<00:00, 30.07it/s, loss=0.141, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 213: 100%|█| 7/7 [00:00<00:00, 32.48it/s, loss=0.141, v_num=16, val_los
Epoch 214: 86%|▊| 6/7 [00:00<00:00, 29.70it/s, loss=0.136, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 214: 100%|█| 7/7 [00:00<00:00, 32.25it/s, loss=0.136, v_num=16, val_los
Epoch 215: 86%|▊| 6/7 [00:00<00:00, 28.91it/s, loss=0.138, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 215: 100%|█| 7/7 [00:00<00:00, 31.11it/s, loss=0.138, v_num=16, val_los
Epoch 216: 86%|▊| 6/7 [00:00<00:00, 34.18it/s, loss=0.139, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 216: 100%|█| 7/7 [00:00<00:00, 36.45it/s, loss=0.139, v_num=16, val_los
Epoch 217: 86%|▊| 6/7 [00:00<00:00, 29.55it/s, loss=0.14, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 217: 100%|█| 7/7 [00:00<00:00, 32.18it/s, loss=0.14, v_num=16, val_loss
Epoch 218: 86%|▊| 6/7 [00:00<00:00, 29.77it/s, loss=0.137, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 218: 100%|█| 7/7 [00:00<00:00, 32.40it/s, loss=0.137, v_num=16, val_los
Epoch 219: 86%|▊| 6/7 [00:00<00:00, 30.61it/s, loss=0.137, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 219: 100%|█| 7/7 [00:00<00:00, 32.94it/s, loss=0.137, v_num=16, val_los
Epoch 220: 86%|▊| 6/7 [00:00<00:00, 31.00it/s, loss=0.137, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 220: 100%|█| 7/7 [00:00<00:00, 33.33it/s, loss=0.137, v_num=16, val_los
Epoch 221: 86%|▊| 6/7 [00:00<00:00, 29.55it/s, loss=0.139, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 221: 100%|█| 7/7 [00:00<00:00, 32.33it/s, loss=0.139, v_num=16, val_los
Epoch 222: 86%|▊| 6/7 [00:00<00:00, 34.48it/s, loss=0.141, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 222: 100%|█| 7/7 [00:00<00:00, 37.43it/s, loss=0.141, v_num=16, val_los
Epoch 223: 86%|▊| 6/7 [00:00<00:00, 30.76it/s, loss=0.141, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 223: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.141, v_num=16, val_los
Epoch 224: 86%|▊| 6/7 [00:00<00:00, 31.49it/s, loss=0.136, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 224: 100%|█| 7/7 [00:00<00:00, 33.49it/s, loss=0.136, v_num=16, val_los
Epoch 225: 86%|▊| 6/7 [00:00<00:00, 36.25it/s, loss=0.135, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 225: 100%|█| 7/7 [00:00<00:00, 39.21it/s, loss=0.135, v_num=16, val_los
Epoch 226: 86%|▊| 6/7 [00:00<00:00, 33.99it/s, loss=0.137, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 226: 100%|█| 7/7 [00:00<00:00, 35.80it/s, loss=0.137, v_num=16, val_los
Epoch 227: 86%|▊| 6/7 [00:00<00:00, 33.24it/s, loss=0.138, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 227: 100%|█| 7/7 [00:00<00:00, 35.62it/s, loss=0.138, v_num=16, val_los
Epoch 228: 86%|▊| 6/7 [00:00<00:00, 34.48it/s, loss=0.138, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 228: 100%|█| 7/7 [00:00<00:00, 36.64it/s, loss=0.138, v_num=16, val_los
Epoch 229: 86%|▊| 6/7 [00:00<00:00, 32.96it/s, loss=0.136, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 229: 100%|█| 7/7 [00:00<00:00, 34.73it/s, loss=0.136, v_num=16, val_los
Epoch 230: 86%|▊| 6/7 [00:00<00:00, 33.99it/s, loss=0.133, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 230: 100%|█| 7/7 [00:00<00:00, 36.93it/s, loss=0.133, v_num=16, val_los
Epoch 231: 86%|▊| 6/7 [00:00<00:00, 36.80it/s, loss=0.13, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 231: 100%|█| 7/7 [00:00<00:00, 39.54it/s, loss=0.13, v_num=16, val_loss
Epoch 232: 86%|▊| 6/7 [00:00<00:00, 34.38it/s, loss=0.128, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 232: 100%|█| 7/7 [00:00<00:00, 37.03it/s, loss=0.128, v_num=16, val_los
Epoch 233: 86%|▊| 6/7 [00:00<00:00, 27.58it/s, loss=0.129, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 233: 100%|█| 7/7 [00:00<00:00, 30.10it/s, loss=0.129, v_num=16, val_los
Epoch 234: 86%|▊| 6/7 [00:00<00:00, 32.17it/s, loss=0.133, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 234: 100%|█| 7/7 [00:00<00:00, 34.91it/s, loss=0.133, v_num=16, val_los
Epoch 235: 86%|▊| 6/7 [00:00<00:00, 33.05it/s, loss=0.139, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 235: 100%|█| 7/7 [00:00<00:00, 35.80it/s, loss=0.139, v_num=16, val_los
Epoch 236: 86%|▊| 6/7 [00:00<00:00, 36.03it/s, loss=0.141, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 236: 100%|█| 7/7 [00:00<00:00, 38.77it/s, loss=0.141, v_num=16, val_los
Epoch 237: 86%|▊| 6/7 [00:00<00:00, 30.45it/s, loss=0.141, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 237: 100%|█| 7/7 [00:00<00:00, 33.17it/s, loss=0.141, v_num=16, val_los
Epoch 238: 86%|▊| 6/7 [00:00<00:00, 35.18it/s, loss=0.138, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 238: 100%|█| 7/7 [00:00<00:00, 37.93it/s, loss=0.138, v_num=16, val_los
Epoch 239: 86%|▊| 6/7 [00:00<00:00, 35.39it/s, loss=0.136, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 239: 100%|█| 7/7 [00:00<00:00, 38.35it/s, loss=0.136, v_num=16, val_los
Epoch 240: 86%|▊| 6/7 [00:00<00:00, 29.70it/s, loss=0.131, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 240: 100%|█| 7/7 [00:00<00:00, 32.18it/s, loss=0.131, v_num=16, val_los
Epoch 241: 86%|▊| 6/7 [00:00<00:00, 33.70it/s, loss=0.132, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 241: 100%|█| 7/7 [00:00<00:00, 35.62it/s, loss=0.132, v_num=16, val_los
Epoch 242: 86%|▊| 6/7 [00:00<00:00, 30.45it/s, loss=0.131, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 242: 100%|█| 7/7 [00:00<00:00, 31.17it/s, loss=0.131, v_num=16, val_los
Epoch 243: 86%|▊| 6/7 [00:00<00:00, 21.93it/s, loss=0.136, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 243: 100%|█| 7/7 [00:00<00:00, 23.41it/s, loss=0.136, v_num=16, val_los
Epoch 244: 86%|▊| 6/7 [00:00<00:00, 30.69it/s, loss=0.143, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 244: 100%|█| 7/7 [00:00<00:00, 33.25it/s, loss=0.143, v_num=16, val_los
Epoch 245: 86%|▊| 6/7 [00:00<00:00, 33.80it/s, loss=0.142, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 245: 100%|█| 7/7 [00:00<00:00, 36.17it/s, loss=0.142, v_num=16, val_los
Epoch 246: 86%|▊| 6/7 [00:00<00:00, 28.84it/s, loss=0.139, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 246: 100%|█| 7/7 [00:00<00:00, 31.46it/s, loss=0.139, v_num=16, val_los
Epoch 247: 86%|▊| 6/7 [00:00<00:00, 23.85it/s, loss=0.139, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 247: 100%|█| 7/7 [00:00<00:00, 25.83it/s, loss=0.139, v_num=16, val_los
Epoch 248: 86%|▊| 6/7 [00:00<00:00, 21.09it/s, loss=0.136, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 248: 100%|█| 7/7 [00:00<00:00, 22.58it/s, loss=0.136, v_num=16, val_los
Epoch 249: 86%|▊| 6/7 [00:00<00:00, 20.54it/s, loss=0.131, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 249: 100%|█| 7/7 [00:00<00:00, 22.36it/s, loss=0.131, v_num=16, val_los
Epoch 250: 86%|▊| 6/7 [00:00<00:00, 23.81it/s, loss=0.132, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 250: 100%|█| 7/7 [00:00<00:00, 25.59it/s, loss=0.132, v_num=16, val_los
Epoch 251: 86%|▊| 6/7 [00:00<00:00, 17.70it/s, loss=0.128, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 251: 100%|█| 7/7 [00:00<00:00, 19.25it/s, loss=0.128, v_num=16, val_los
Epoch 252: 86%|▊| 6/7 [00:00<00:00, 21.54it/s, loss=0.131, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 252: 100%|█| 7/7 [00:00<00:00, 23.14it/s, loss=0.131, v_num=16, val_los
Epoch 253: 86%|▊| 6/7 [00:00<00:00, 16.32it/s, loss=0.134, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 253: 100%|█| 7/7 [00:00<00:00, 17.90it/s, loss=0.134, v_num=16, val_los
Epoch 254: 86%|▊| 6/7 [00:00<00:00, 18.98it/s, loss=0.135, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 254: 100%|█| 7/7 [00:00<00:00, 20.49it/s, loss=0.135, v_num=16, val_los
Epoch 255: 86%|▊| 6/7 [00:00<00:00, 17.85it/s, loss=0.133, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 255: 100%|█| 7/7 [00:00<00:00, 19.20it/s, loss=0.133, v_num=16, val_los
Epoch 256: 86%|▊| 6/7 [00:00<00:00, 11.96it/s, loss=0.131, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 256: 100%|█| 7/7 [00:00<00:00, 13.09it/s, loss=0.131, v_num=16, val_los
Epoch 257: 86%|▊| 6/7 [00:00<00:00, 12.94it/s, loss=0.13, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 257: 100%|█| 7/7 [00:00<00:00, 13.78it/s, loss=0.13, v_num=16, val_loss
Epoch 258: 86%|▊| 6/7 [00:00<00:00, 18.69it/s, loss=0.131, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 258: 100%|█| 7/7 [00:00<00:00, 20.46it/s, loss=0.131, v_num=16, val_los
Epoch 259: 86%|▊| 6/7 [00:00<00:00, 22.05it/s, loss=0.132, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 259: 100%|█| 7/7 [00:00<00:00, 23.97it/s, loss=0.132, v_num=16, val_los
Epoch 260: 86%|▊| 6/7 [00:00<00:00, 20.40it/s, loss=0.131, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 260: 100%|█| 7/7 [00:00<00:00, 22.08it/s, loss=0.131, v_num=16, val_los
Epoch 261: 86%|▊| 6/7 [00:00<00:00, 19.70it/s, loss=0.13, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 261: 100%|█| 7/7 [00:00<00:00, 20.95it/s, loss=0.13, v_num=16, val_loss
Epoch 262: 86%|▊| 6/7 [00:00<00:00, 19.77it/s, loss=0.127, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 262: 100%|█| 7/7 [00:00<00:00, 21.47it/s, loss=0.127, v_num=16, val_los
Epoch 263: 86%|▊| 6/7 [00:00<00:00, 24.29it/s, loss=0.128, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 263: 100%|█| 7/7 [00:00<00:00, 26.21it/s, loss=0.128, v_num=16, val_los
Epoch 264: 86%|▊| 6/7 [00:00<00:00, 23.25it/s, loss=0.13, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 264: 100%|█| 7/7 [00:00<00:00, 25.00it/s, loss=0.13, v_num=16, val_loss
Epoch 265: 86%|▊| 6/7 [00:00<00:00, 20.98it/s, loss=0.132, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 265: 100%|█| 7/7 [00:00<00:00, 22.36it/s, loss=0.132, v_num=16, val_los
Epoch 266: 86%|▊| 6/7 [00:00<00:00, 20.90it/s, loss=0.133, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 266: 100%|█| 7/7 [00:00<00:00, 22.54it/s, loss=0.133, v_num=16, val_los
Epoch 267: 86%|▊| 6/7 [00:00<00:00, 26.37it/s, loss=0.133, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 267: 100%|█| 7/7 [00:00<00:00, 28.11it/s, loss=0.133, v_num=16, val_los
Epoch 268: 86%|▊| 6/7 [00:00<00:00, 24.54it/s, loss=0.134, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 268: 100%|█| 7/7 [00:00<00:00, 26.87it/s, loss=0.134, v_num=16, val_los
Epoch 269: 86%|▊| 6/7 [00:00<00:00, 26.31it/s, loss=0.132, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 269: 100%|█| 7/7 [00:00<00:00, 27.88it/s, loss=0.132, v_num=16, val_los
Epoch 270: 86%|▊| 6/7 [00:00<00:00, 27.33it/s, loss=0.129, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 270: 100%|█| 7/7 [00:00<00:00, 29.85it/s, loss=0.129, v_num=16, val_los
Epoch 271: 86%|▊| 6/7 [00:00<00:00, 31.66it/s, loss=0.129, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 271: 100%|█| 7/7 [00:00<00:00, 33.73it/s, loss=0.129, v_num=16, val_los
Epoch 272: 86%|▊| 6/7 [00:00<00:00, 30.07it/s, loss=0.129, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 272: 100%|█| 7/7 [00:00<00:00, 32.18it/s, loss=0.129, v_num=16, val_los
Epoch 273: 86%|▊| 6/7 [00:00<00:00, 25.26it/s, loss=0.13, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 273: 100%|█| 7/7 [00:00<00:00, 27.29it/s, loss=0.13, v_num=16, val_loss
Epoch 274: 86%|▊| 6/7 [00:00<00:00, 25.31it/s, loss=0.131, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 274: 100%|█| 7/7 [00:00<00:00, 27.07it/s, loss=0.131, v_num=16, val_los
Epoch 275: 86%|▊| 6/7 [00:00<00:00, 25.15it/s, loss=0.129, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 275: 100%|█| 7/7 [00:00<00:00, 27.02it/s, loss=0.129, v_num=16, val_los
Epoch 276: 86%|▊| 6/7 [00:00<00:00, 29.55it/s, loss=0.128, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 276: 100%|█| 7/7 [00:00<00:00, 30.90it/s, loss=0.128, v_num=16, val_los
Epoch 277: 86%|▊| 6/7 [00:00<00:00, 26.84it/s, loss=0.126, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 277: 100%|█| 7/7 [00:00<00:00, 28.74it/s, loss=0.126, v_num=16, val_los
Epoch 278: 86%|▊| 6/7 [00:00<00:00, 26.20it/s, loss=0.125, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 278: 100%|█| 7/7 [00:00<00:00, 28.57it/s, loss=0.125, v_num=16, val_los
Epoch 279: 86%|▊| 6/7 [00:00<00:00, 28.63it/s, loss=0.126, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 279: 100%|█| 7/7 [00:00<00:00, 30.83it/s, loss=0.126, v_num=16, val_los
Epoch 280: 86%|▊| 6/7 [00:00<00:00, 23.85it/s, loss=0.126, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 280: 100%|█| 7/7 [00:00<00:00, 25.54it/s, loss=0.126, v_num=16, val_los
Epoch 281: 86%|▊| 6/7 [00:00<00:00, 24.84it/s, loss=0.13, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 281: 100%|█| 7/7 [00:00<00:00, 26.31it/s, loss=0.13, v_num=16, val_loss
Epoch 282: 86%|▊| 6/7 [00:00<00:00, 30.30it/s, loss=0.132, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 282: 100%|█| 7/7 [00:00<00:00, 32.18it/s, loss=0.132, v_num=16, val_los
Epoch 283: 86%|▊| 6/7 [00:00<00:00, 22.98it/s, loss=0.13, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 283: 100%|█| 7/7 [00:00<00:00, 24.82it/s, loss=0.13, v_num=16, val_loss
Epoch 284: 86%|▊| 6/7 [00:00<00:00, 19.64it/s, loss=0.127, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 284: 100%|█| 7/7 [00:00<00:00, 18.59it/s, loss=0.127, v_num=16, val_los
Epoch 285: 86%|▊| 6/7 [00:00<00:00, 12.02it/s, loss=0.124, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 285: 100%|█| 7/7 [00:00<00:00, 13.09it/s, loss=0.124, v_num=16, val_los
Epoch 286: 86%|▊| 6/7 [00:00<00:00, 29.77it/s, loss=0.123, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 286: 100%|█| 7/7 [00:00<00:00, 32.40it/s, loss=0.123, v_num=16, val_los
Epoch 287: 86%|▊| 6/7 [00:00<00:00, 28.50it/s, loss=0.123, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 287: 100%|█| 7/7 [00:00<00:00, 30.83it/s, loss=0.123, v_num=16, val_los
Epoch 288: 86%|▊| 6/7 [00:00<00:00, 27.77it/s, loss=0.127, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 288: 100%|█| 7/7 [00:00<00:00, 29.59it/s, loss=0.127, v_num=16, val_los
Epoch 289: 86%|▊| 6/7 [00:00<00:00, 28.84it/s, loss=0.133, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 289: 100%|█| 7/7 [00:00<00:00, 31.11it/s, loss=0.133, v_num=16, val_los
Epoch 290: 86%|▊| 6/7 [00:00<00:00, 29.70it/s, loss=0.134, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 290: 100%|█| 7/7 [00:00<00:00, 32.18it/s, loss=0.134, v_num=16, val_los
Epoch 291: 86%|▊| 6/7 [00:00<00:00, 29.85it/s, loss=0.13, v_num=16, val_loss
Validating: 0it [00:00, ?it/s]
Epoch 291: 100%|█| 7/7 [00:00<00:00, 32.25it/s, loss=0.13, v_num=16, val_loss
Epoch 292: 86%|▊| 6/7 [00:00<00:00, 29.26it/s, loss=0.125, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 292: 100%|█| 7/7 [00:00<00:00, 31.67it/s, loss=0.125, v_num=16, val_los
Epoch 293: 86%|▊| 6/7 [00:00<00:00, 32.25it/s, loss=0.123, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 293: 100%|█| 7/7 [00:00<00:00, 34.31it/s, loss=0.123, v_num=16, val_los
Epoch 294: 86%|▊| 6/7 [00:00<00:00, 27.84it/s, loss=0.122, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 294: 100%|█| 7/7 [00:00<00:00, 29.97it/s, loss=0.122, v_num=16, val_los
Epoch 295: 86%|▊| 6/7 [00:00<00:00, 26.96it/s, loss=0.122, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 295: 100%|█| 7/7 [00:00<00:00, 28.74it/s, loss=0.122, v_num=16, val_los
Epoch 296: 86%|▊| 6/7 [00:00<00:00, 27.27it/s, loss=0.123, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 296: 100%|█| 7/7 [00:00<00:00, 29.66it/s, loss=0.123, v_num=16, val_los
Epoch 297: 86%|▊| 6/7 [00:00<00:00, 30.45it/s, loss=0.123, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 297: 100%|█| 7/7 [00:00<00:00, 32.63it/s, loss=0.123, v_num=16, val_los
Epoch 298: 86%|▊| 6/7 [00:00<00:00, 27.71it/s, loss=0.129, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 298: 100%|█| 7/7 [00:00<00:00, 29.28it/s, loss=0.129, v_num=16, val_los
Epoch 299: 86%|▊| 6/7 [00:00<00:00, 29.33it/s, loss=0.128, v_num=16, val_los
Validating: 0it [00:00, ?it/s]
Epoch 299: 100%|█| 7/7 [00:00<00:00, 31.67it/s, loss=0.128, v_num=16, val_los
Epoch 299: 100%|█| 7/7 [00:00<00:00, 29.41it/s, loss=0.128, v_num=16, val_los
tensor([[[417.0000],
[391.0000],
[419.0000],
[461.0000],
[472.0000],
[535.0000],
[622.0000],
[606.0000],
[508.0000],
[461.0000],
[390.0000],
[432.0000]],
[[357.1958],
[366.9867],
[401.5798],
[410.2579],
[436.4939],
[479.5177],
[502.5024],
[512.6150],
[468.7754],
[429.7265],
[382.5964],
[389.3654]]], dtype=torch.float64)
MAE is: tensor(48.0323, dtype=torch.float64)
In [ ]:
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