import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


data_train = torch.utils.data.DataLoader('dataset/train',
                                          batch_size=20,
                                          shuffle=True)
data_valid = torch.utils.data.DataLoader('dataset/valid',
                                          batch_size=1,
                                          shuffle=True)

print(data_valid)
print(data_train)
# for i, (images, labels) in enumerate(data_valid):
#     print(type(images))
#
#
# print(data_valid)
# print(data_valid.size())
#
4df28f89c25ea55d83106303edb14832a9854595
<mask token>

<mask token>
print(data_valid)
print(data_train)

<mask token>
data_train = torch.utils.data.DataLoader('dataset/train', batch_size=20,
    shuffle=True)
data_valid = torch.utils.data.DataLoader('dataset/valid', batch_size=1,
    shuffle=True)
print(data_valid)
print(data_train)

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_train = torch.utils.data.DataLoader('dataset/train', batch_size=20,
    shuffle=True)
data_valid = torch.utils.data.DataLoader('dataset/valid', batch_size=1,
    shuffle=True)
print(data_valid)
print(data_train)

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


data_train = torch.utils.data.DataLoader('dataset/train',
                                          batch_size=20,
                                          shuffle=True)
data_valid = torch.utils.data.DataLoader('dataset/valid',
                                          batch_size=1,
                                          shuffle=True)

print(data_valid)
print(data_train)
# for i, (images, labels) in enumerate(data_valid):
#     print(type(images))
#
#
# print(data_valid)
# print(data_valid.size())
#