diff --git a/utils.py b/utils.py
index 54ab8357..422680f3 100755
--- a/utils.py
+++ b/utils.py
@@ -46,7 +46,7 @@ def log(self, losses=None, images=None):
                 sys.stdout.write('%s: %.4f | ' % (loss_name, self.losses[loss_name]/self.batch))
 
         batches_done = self.batches_epoch*(self.epoch - 1) + self.batch
-        batches_left = self.batches_epoch*(self.n_epochs - self.epoch) + self.batches_epoch - self.batch 
+        batches_left = self.batches_epoch*(self.n_epochs - self.epoch) + self.batches_epoch - self.batch
         sys.stdout.write('ETA: %s' % (datetime.timedelta(seconds=batches_left*self.mean_period/batches_done)))
 
         # Draw images
@@ -61,7 +61,7 @@ def log(self, losses=None, images=None):
             # Plot losses
             for loss_name, loss in self.losses.items():
                 if loss_name not in self.loss_windows:
-                    self.loss_windows[loss_name] = self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]), 
+                    self.loss_windows[loss_name] = self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]),
                                                                     opts={'xlabel': 'epochs', 'ylabel': loss_name, 'title': loss_name})
                 else:
                     self.viz.line(X=np.array([self.epoch]), Y=np.array([loss/self.batch]), win=self.loss_windows[loss_name], update='append')
@@ -74,7 +74,7 @@ def log(self, losses=None, images=None):
         else:
             self.batch += 1
 
-        
+
 
 class ReplayBuffer():
     def __init__(self, max_size=50):
@@ -111,8 +111,7 @@ def step(self, epoch):
 def weights_init_normal(m):
     classname = m.__class__.__name__
     if classname.find('Conv') != -1:
-        torch.nn.init.normal(m.weight.data, 0.0, 0.02)
+        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
     elif classname.find('BatchNorm2d') != -1:
-        torch.nn.init.normal(m.weight.data, 1.0, 0.02)
+        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
         torch.nn.init.constant(m.bias.data, 0.0)
-