Update main.py

PhysikerErlangen · web-flow · commit 8a824389d9fc · 2017-09-15T18:05:51.000+08:00
I got confused by the use of the binary cross entropy. In particular it wasn't clear to me why the variable real_labels are used in the training of the generator.

I have added some comments. I am not sure if they are correct, so you might want to double check them.
diff --git a/tutorials/02-intermediate/generative_adversarial_network/main.py b/tutorials/02-intermediate/generative_adversarial_network/main.py
@@ -64,19 +64,22 @@ 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
         outputs = D(images)
+        # Apply BCE loss. Second term is always zero since real_labels == 1
         d_loss_real = criterion(outputs, real_labels)
         real_score = outputs
         
         # Compute loss with fake images
         z = to_var(torch.randn(batch_size, 64))
         fake_images = G(z)
         outputs = D(fake_images)
+        # Apply BCE loss. First term is always zero since fake_labels == 0
         d_loss_fake = criterion(outputs, fake_labels)
         fake_score = outputs
         
@@ -91,6 +94,11 @@ def denorm(x):
         z = to_var(torch.randn(batch_size, 64))
         fake_images = G(z)
         outputs = D(fake_images)
+        # remember that min log(1-D(G(z))) has the same fix point as max log(D(G(z)))
+        # Here we maximize log(D(G(z))), which is exactly the first term in the BCE loss
+        # with t=1. (see definition of BCE for info on t)
+        # t==1 is valid for real_labels, thus we use them as input for the BCE loss.
+        # Don't get yourself confused by this. It is just convenient to use to the BCE loss.
         g_loss = criterion(outputs, real_labels)
         
         # Backprop + Optimize
@@ -116,4 +124,4 @@ 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')
