pytorch tuto

train_model

n = 100
for epoch in range(n):
	epoch +=1
	#convert numpy array into torch variable:
	inputs = Variable(torch.from_numpy(x_train))
	labels = Variable(torch.from_numpy(y_train))

	#clear gradient w.r.t parameters:
	optimizer.zero_grad()

	#Forward:
	outputs = model(inputs)

	#calculate Loss:
	loss = criterion(outputs, labels)

	#getting gradient w.r.t parameters:
	loss.backward()

	#updating parameters:
	optimizer.step()

	print('epoch {}, loss {}'.format(epoch, loss.item()))