I don’t understand how to calculate the running_loss value when training a model.
In Training a classifier tutorial 20 when training, the running_loss is added with loss.item() and set to 0.0 every 2000 mini-batches as follow:
running_loss += loss.item()
if i % 2000 == 1999:    # print every 2000 mini-batches
	print('[%d, %5d] loss: %.3f' %
		(epoch + 1, i + 1, running_loss / 2000))
	running_loss = 0.0

But, in Transfer learning tutorial 11 the running_loss is added with loss.item() * inputs.size(0) and it is not set to 0.0:
	...
	# statistics
	running_loss += loss.item() * inputs.size(0)
	running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
	scheduler.step()

epoch_loss = running_loss / dataset_sizes[phase]

What is the difference?