Bugs In MNIST (pytorch#175)

mnist/main.py

@@ -89,7 +89,7 @@ def train(epoch):
                 epoch, batch_idx * len(data), len(train_loader.dataset),
                 100. * batch_idx / len(train_loader), loss.data[0]))
 
-def test(epoch):
+def test():
     model.eval()
     test_loss = 0
     correct = 0
@@ -98,17 +98,16 @@ def test(epoch):
             data, target = data.cuda(), target.cuda()
         data, target = Variable(data, volatile=True), Variable(target)
         output = model(data)
-        test_loss += F.nll_loss(output, target).data[0]
+        test_loss += F.nll_loss(output, target, size_average=False).data[0] # 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
-    test_loss /= len(test_loader) # loss function already averages over batch size
+    test_loss /= len(test_loader.dataset)
     print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
         test_loss, correct, len(test_loader.dataset),
         100. * correct / len(test_loader.dataset)))
 
 
 for epoch in range(1, args.epochs + 1):
     train(epoch)
-    test(epoch)
+    test()