diff --git a/vae/main.py b/vae/main.py
index c45382d3c1..46ab26cfe5 100644
--- a/vae/main.py
+++ b/vae/main.py
@@ -58,11 +58,11 @@ def encode(self, x):
 
     def reparameterize(self, mu, logvar):
         if self.training:
-          std = logvar.mul(0.5).exp_()
-          eps = Variable(std.data.new(std.size()).normal_())
-          return eps.mul(std).add_(mu)
+            std = logvar.mul(0.5).exp_()
+            eps = Variable(std.data.new(std.size()).normal_())
+            return eps.mul(std).add_(mu)
         else:
-          return mu
+            return mu
 
     def decode(self, z):
         h3 = self.relu(self.fc3(z))
@@ -129,10 +129,10 @@ def test(epoch):
         recon_batch, mu, logvar = model(data)
         test_loss += loss_function(recon_batch, data, mu, logvar).data[0]
         if i == 0:
-          n = min(data.size(0), 8)
-          comparison = torch.cat([data[:n],
+            n = min(data.size(0), 8)
+            comparison = torch.cat([data[:n],
                                   recon_batch.view(args.batch_size, 1, 28, 28)[:n]])
-          save_image(comparison.data.cpu(),
+            save_image(comparison.data.cpu(),
                      'results/reconstruction_' + str(epoch) + '.png', nrow=n)
 
     test_loss /= len(test_loader.dataset)
@@ -144,7 +144,7 @@ def test(epoch):
     test(epoch)
     sample = Variable(torch.randn(64, 20))
     if args.cuda:
-       sample = sample.cuda()
+        sample = sample.cuda()
     sample = model.decode(sample).cpu()
     save_image(sample.data.view(64, 1, 28, 28),
                'results/sample_' + str(epoch) + '.png')