add comments

mnist/main.py

@@ -99,12 +99,11 @@ def test():
         data, target = Variable(data, volatile=True), Variable(target)
         current_batch_size = data.data.size()[0]
         output = model(data)
-        # F.nll_loss is averaged among each batch
-        test_loss += F.nll_loss(output, target).data[0] * current_batch_size
+        test_loss += F.nll_loss(output, target).data[0] * current_batch_size # sum up batch loss
         pred = output.data.max(1)[1] # get the index of the max log-probability
         correct += pred.eq(target.data).cpu().sum()
 
-    test_loss = test_loss # sum of loss function
+    test_loss = test_loss # sum of loss function over all data points
     test_loss /= len(test_loader.dataset)
     print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
         test_loss, correct, len(test_loader.dataset),