pytorch 6 batch_train 批训练操作

import torch
import torch.utils.data as Data
torch.manual_seed(1) # reproducible
# BATCH_SIZE = 5  
BATCH_SIZE = 8# 每次使用8个数据同时传入网路
x = torch.linspace(1, 10, 10) # this is x data (torch tensor)
y = torch.linspace(10, 1, 10) # this is y data (torch tensor)
torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
 dataset=torch_dataset,# torch TensorDataset format
 batch_size=BATCH_SIZE,# mini batch size
 shuffle=False,  # 设置不随机打乱数据 random shuffle for training
 num_workers=2,  # 使用两个进程提取数据，subprocesses for loading data
)
def show_batch():
 for epoch in range(3):# 全部的数据使用3遍，train entire dataset 3 times
  for step, (batch_x, batch_y) in enumerate(loader):  # for each training step
# train your data...
print('Epoch: ', epoch, '| Step: ', step, '| batch x: ',
batch_x.numpy(), '| batch y: ', batch_y.numpy())
if __name__ == '__main__':
 show_batch()

Epoch:  0 | Step:  0 | batch x:  [1. 2. 3. 4. 5. 6. 7. 8.] | batch y:  [10.  9.  8.  7.  6.  5.  4.  3.]
Epoch:  0 | Step:  1 | batch x:  [ 9. 10.] | batch y:  [2. 1.]
Epoch:  1 | Step:  0 | batch x:  [1. 2. 3. 4. 5. 6. 7. 8.] | batch y:  [10.  9.  8.  7.  6.  5.  4.  3.]
Epoch:  1 | Step:  1 | batch x:  [ 9. 10.] | batch y:  [2. 1.]
Epoch:  2 | Step:  0 | batch x:  [1. 2. 3. 4. 5. 6. 7. 8.] | batch y:  [10.  9.  8.  7.  6.  5.  4.  3.]
Epoch:  2 | Step:  1 | batch x:  [ 9. 10.] | batch y:  [2. 1.]

train_dataset = Data.TensorDataset(train_x, train_y)

train_x = torch.FloatTensor(train_x)

train_loader = Data.DataLoader(
  dataset=train_dataset,
  batch_size=batch_size,
  shuffle=True
 )

 for epoch in range(epochs):
  for step, (batch_x, batch_y) in enumerate(train_loader):
batch_x, batch_y = Variable(batch_x), Variable(batch_y)