update for new version of torch

tutorial-contents/406_GAN.py

@@ -55,23 +55,21 @@ opt_G = torch.optim.Adam(G.parameters(), lr=LR_G)
 plt.ion()   # something about continuous plotting
 
 for step in range(10000):
-    artist_paintings = artist_works()           # real painting from artist
-    G_ideas = torch.randn(BATCH_SIZE, N_IDEAS)  # random ideas
+    artist_paintings = artist_works()  # real painting from artist
+    G_ideas = torch.randn(BATCH_SIZE, N_IDEAS, requires_grad=True)  # random ideas\n
     G_paintings = G(G_ideas)                    # fake painting from G (random ideas)
-
-    prob_artist0 = D(artist_paintings)          # D try to increase this prob
     prob_artist1 = D(G_paintings)               # D try to reduce this prob
-
-    D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))
-    G_loss = torch.mean(torch.log(1. - prob_artist1))
-
-    opt_D.zero_grad()
-    D_loss.backward(retain_graph=True)      # reusing computational graph
-    opt_D.step()
-
+    G_loss = torch.mean(torch.log(1. - prob_artist1))  
     opt_G.zero_grad()
     G_loss.backward()
     opt_G.step()
+     
+    prob_artist0 = D(artist_paintings)          # D try to increase this prob
+    prob_artist1 = D(G_paintings.detach())  # D try to reduce this prob
+    D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1. - prob_artist1))
+    opt_D.zero_grad()
+    D_loss.backward(retain_graph=True)      # reusing computational graph
+    opt_D.step()
 
     if step % 50 == 0:  # plotting
         plt.cla()