Hi,
How do you calculate the validation loss per iteration?
Say you’ve your train_loader and test_loader.
Normally for training per iteration to get the training loss, we do  loss = criterion(outputs, labels where criterion = nn.CrossEntropyLoss() .
For validation per iteration,
            test_loss_iter = []
            for images, labels in test_loader:
                images = Variable(images.view(-1, 784).cuda())
                outputs = net(images)
                test_loss = criterion(outputs, labels)
                test_loss_iter.append(test_loss)
            iteration_test_loss = np.mean(test_loss_iter)

But I get error AttributeError: 'torch.LongTensor' object has no attribute 'requires_grad'