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#代码1部分

epochs = 3

print_every = 40 steps = 0 for e in range(epochs):     running_loss = 0     for images, labels in iter(trainloader):         steps += 1         # Flatten MNIST images into a 784 long vector         print("images.size()[0]=",images.size()[0])         images.resize_(images.size()[0], 784)                  optimizer.zero_grad()                  # Forward and backward passes         output = model.forward(images)         print("output.size()=",output.size())         loss = criterion(output, labels)         loss.backward()         optimizer.step()                  running_loss += loss.item()                  if steps % print_every == 0:             print("Epoch: {}/{}... ".format(e+1, epochs),                   "Loss: {:.4f}".format(running_loss/print_every))                          running_loss = 0

#result

output.size()= torch.Size([64, 10])images.size()[0]= 64output.size()= torch.Size([64, 10])images.size()[0]= 64output.size()= torch.Size([64, 10])images.size()[0]= 64output.size()= torch.Size([64, 10])images.size()[0]= 64output.size()= torch.Size([64, 10])images.size()[0]= 64output.size()= torch.Size([64, 10])images.size()[0]= 32output.size()= torch.Size([32, 10])

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