How to integrate it in Pytorch or Paddlepaddle training loop?

[Vũ Hồng Quang]

    model.train()
    for epoch_ in range(epoch):
        optimizer.clear_grad()
        for batch_idx, (data, label) in enumerate(train_loader):
            # data = data.cuda()
            # label = label.cuda()
            # print(np.array(data).shape)
            # optimizer.clear_grad()
            outputs = model(data, label)
            # print(outputs.shape)
            # print(label.shape)
            outputs, label = paddle.to_tensor(outputs), paddle.to_tensor(label)
            loss = criterion(outputs, label)
            loss.backward()
            optimizer.step()
            if batch_idx % 10 == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                    epoch_, batch_idx * len(data), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), loss.item()))
            
def test(test_loader, model):
    model.eval()
    losses = []
    for batch_idx, (data, label) in enumerate(test_loader):
        outputs = model(data, label)
        outputs, label = paddle.to_tensor(outputs), paddle.to_tensor(label)
        loss = criterion(outputs, label)
        loss.backward()
        if batch_idx % 10 == 0:
            print("Batch {}: loss {}".format(batch_idx, loss.item()))
        losses.append(loss.item())
    
    print('Test set: Average loss: {:.4f}"'.format(sum(losses) / len(losses)))```


How can I do that in this loop? Could you give more details in the document?

[Kick28]

Hi! Did you find the solution?