training.py

# Train the Model
for epoch in range(num_epochs):
    total_batch = int(len(newsgroups_train.data)/batch_size)
    for i in range(total_batch):
        batch_x,batch_y = get_batch(newsgroups_train,i,batch_size)
        articles = Variable(torch.FloatTensor(batch_x))
        labels = Variable(torch.FloatTensor(batch_y))
 
        # Forward + Backward + Optimize
        optimizer.zero_grad() # zero the gradient buffer
	outputs = net(articles)
	loss = criterion(outputs, labels)
	loss.backward()
	optimizer.step()
	
	print (‘Epoch [%d/%d], Step [%d/%d], Loss: %.4f’ 
 				%(epoch+1, num_epochs, i+1, len(newsgroups_train.data)//batch_size, loss.data[0]))