issue with pytorch-beginner/05-Recurrent Neural Network/recurrent_network.py

[re4388]

1. need to change .data[0] => .item()

2. add model.train() at beginning of the loop

Only need to modify the training loop code, below is the fixed code worked for me :)

for epoch in range(num_epoches):
    model.train()
    print('epoch {}'.format(epoch + 1))
    print('*' * 10)
    running_loss = 0.0
    running_acc = 0.0
    for i, data in enumerate(train_loader, 1):
        img, label = data
        b, c, h, w = img.size()
        assert c == 1, 'channel must be 1'
        img = img.squeeze(1)
        # img = img.view(b*h, w)
        # img = torch.transpose(img, 1, 0)
        # img = img.contiguous().view(w, b, -1)
        if use_gpu:
            img = Variable(img).cuda()
            label = Variable(label).cuda()
        else:
            img = Variable(img)
            label = Variable(label)
            
        
        # 向前传播
        out = model(img)
        loss = criterion(out, label)
        running_loss += loss.item() * label.size(0)
        _, pred = torch.max(out, 1)
        num_correct = (pred == label).sum()
        running_acc += num_correct.item()
        # 向后传播
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if i % 300 == 0:
            print('[{}/{}] Loss: {:.6f}, Acc: {:.6f}'.format(
                epoch + 1, num_epoches, running_loss / (batch_size * i),
                running_acc / (batch_size * i)))
    print('Finish {} epoch, Loss: {:.6f}, Acc: {:.6f}'.format(
        epoch + 1, running_loss / (len(train_dataset)), running_acc / (len(
            train_dataset))))
    model.eval()
    eval_loss = 0.
    eval_acc = 0.
    for data in test_loader:
        img, label = data
        b, c, h, w = img.size()
        assert c == 1, 'channel must be 1'
        img = img.squeeze(1)
        # img = img.view(b*h, w)
        # img = torch.transpose(img, 1, 0)
        # img = img.contiguous().view(w, b, h)
        if use_gpu:
            img = Variable(img, volatile=True).cuda()
            label = Variable(label, volatile=True).cuda()
        else:
            img = Variable(img, volatile=True)
            label = Variable(label, volatile=True)
        out = model(img)
        loss = criterion(out, label)
        eval_loss += loss.item() * label.size(0)
        _, pred = torch.max(out, 1)
        num_correct = (pred == label).sum()
        eval_acc += num_correct.item()
    print('Test Loss: {:.6f}, Acc: {:.6f}'.format(eval_loss / (len(
        test_dataset)), eval_acc / (len(test_dataset))))
    print()