Merge pull request yunjey#61 from PhysikerErlangen/patch-1

Update main.py

Author: yunjey

tutorials/02-intermediate/generative_adversarial_network/main.py

@@ -64,16 +64,20 @@ def denorm(x):
         # Build mini-batch dataset
         batch_size = images.size(0)
         images = to_var(images.view(batch_size, -1))
+        
+        # Create the labels which are later used as input for the BCE loss
         real_labels = to_var(torch.ones(batch_size))
         fake_labels = to_var(torch.zeros(batch_size))
 
         #============= Train the discriminator =============#
-        # Compute loss with real images
+        # Compute BCE_Loss using real images where BCE_Loss(x, y): - y * log(D(x)) - (1-y) * log(1 - D(x))
+        # Second term of the loss is always zero since real_labels == 1
         outputs = D(images)
         d_loss_real = criterion(outputs, real_labels)
         real_score = outputs
 
-        # Compute loss with fake images
+        # Compute BCELoss using fake images
+        # First term of the loss is always zero since fake_labels == 0
         z = to_var(torch.randn(batch_size, 64))
         fake_images = G(z)
         outputs = D(fake_images)
@@ -91,6 +95,9 @@ def denorm(x):
         z = to_var(torch.randn(batch_size, 64))
         fake_images = G(z)
         outputs = D(fake_images)
+        
+        # We train G to maximize log(D(G(z)) instead of minimizing log(1-D(G(z)))
+        # For the reason, see the last paragraph of section 3. https://arxiv.org/pdf/1406.2661.pdf
         g_loss = criterion(outputs, real_labels)
 
         # Backprop + Optimize
@@ -116,4 +123,4 @@ def denorm(x):
 
 # Save the trained parameters 
 torch.save(G.state_dict(), './generator.pkl')
-torch.save(D.state_dict(), './discriminator.pkl')
+torch.save(D.state_dict(), './discriminator.pkl')