Fix formatting to comply with PEP8 (pytorch#654)

mnist/main.py

@@ -24,7 +24,8 @@ def forward(self, x):
         x = F.relu(self.fc1(x))
         x = self.fc2(x)
         return F.log_softmax(x, dim=1)
-
+
+
 def train(args, model, device, train_loader, optimizer, epoch):
     model.train()
     for batch_idx, (data, target) in enumerate(train_loader):
@@ -39,6 +40,7 @@ def train(args, model, device, train_loader, optimizer, epoch):
                 epoch, batch_idx * len(data), len(train_loader.dataset),
                 100. * batch_idx / len(train_loader), loss.item()))
 
+
 def test(args, model, device, test_loader):
     model.eval()
     test_loss = 0
@@ -47,8 +49,8 @@ def test(args, model, device, test_loader):
         for data, target in test_loader:
             data, target = data.to(device), target.to(device)
             output = model(data)
-            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
-            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
+            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
+            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
             correct += pred.eq(target.view_as(pred)).sum().item()
 
     test_loss /= len(test_loader.dataset)
@@ -57,6 +59,7 @@ def test(args, model, device, test_loader):
         test_loss, correct, len(test_loader.dataset),
         100. * correct / len(test_loader.dataset)))
 
+
 def main():
     # Training settings
     parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
@@ -76,7 +79,7 @@ def main():
                         help='random seed (default: 1)')
     parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                         help='how many batches to wait before logging training status')
-    
+
     parser.add_argument('--save-model', action='store_true', default=False,
                         help='For Saving the current Model')
     args = parser.parse_args()
@@ -101,16 +104,16 @@ def main():
                        ])),
         batch_size=args.test_batch_size, shuffle=True, **kwargs)
 
-
     model = Net().to(device)
     optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
 
     for epoch in range(1, args.epochs + 1):
         train(args, model, device, train_loader, optimizer, epoch)
         test(args, model, device, test_loader)
 
-    if (args.save_model):
-        torch.save(model.state_dict(),"mnist_cnn.pt")
-
+    if args.save_model:
+        torch.save(model.state_dict(), "mnist_cnn.pt")
+
+
 if __name__ == '__main__':
     main()