diff --git a/tutorials/02-intermediate/generative_adversarial_network/main.py b/tutorials/02-intermediate/generative_adversarial_network/main.py
index 29b7f35..23964ff 100644
--- a/tutorials/02-intermediate/generative_adversarial_network/main.py
+++ b/tutorials/02-intermediate/generative_adversarial_network/main.py
@@ -64,12 +64,14 @@ for epoch in range(200):
         # 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
         
@@ -77,6 +79,7 @@ for epoch in range(200):
         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 @@ for epoch in range(200):
         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 @@ for epoch in range(200):
 
 # Save the trained parameters 
 torch.save(G.state_dict(), './generator.pkl')
-torch.save(D.state_dict(), './discriminator.pkl')
\ No newline at end of file
+torch.save(D.state_dict(), './discriminator.pkl')