Scratch_August_23_2019.txt

# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0

print("Starting Training Loop...")

# For each epoch
for epoch in range(num_epochs*25):

    # For each batch in the dataloader
    for i, data in enumerate(dataloader, 0):
		############################
        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
        ###########################

        ## Train with all-real batch
        netD.zero_grad()
		
        # Format batch
        real_cpu = data[0].to(device)
        b_size = real_cpu.size(0)
        label = torch.full((b_size,), real_label, device=device)

        # Forward pass real batch through D
        output = netD(real_cpu).view(-1)
        # Calculate loss on all-real batch
        errD_real = criterion(output, label)
        # Calculate gradients for D in backward pass
        errD_real.backward()
        D_x = output.mean().item()

        ## Train with all-fake batch
		
        # Generate batch of latent vectors
        noise = torch.randn(b_size, nz, 1, 1, device=device)
		
        # Generate fake image batch with G
        fake = netG(noise)
        label.fill_(fake_label)
		
        # Classify all fake batch with D
        output = netD(fake.detach()).view(-1)
		
        # Calculate D\'s loss on the all-fake batch
        errD_fake = criterion(output, label)
		
        # Calculate the gradients for this batch
        errD_fake.backward()
        D_G_z1 = output.mean().item()
		
        # Add the gradients from the all-real and all-fake batches
        errD = errD_real + errD_fake
        # Update D\n        optimizerD.step()

        ############################
        # (2) Update G network: maximize log(D(G(z)))
        ###########################
        netG.zero_grad()
        label.fill_(real_label)  # fake labels are real for generator cost
        # Since we just updated D, perform another forward pass of all-fake batch through D
        output = netD(fake).view(-1)

        # Calculate G\'s loss based on this output
        errG = criterion(output, label)
		
        # Calculate gradients for G
        errG.backward()
		
        D_G_z2 = output.mean().item()
		
        # Update G
        optimizerG.step()
		
		# Output training stats
        if i % 50 == 0:
			print(\'[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f\'
				% (epoch, num_epochs, i, len(dataloader),
					errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))        
			
		# Save Losses for plotting later
        G_losses.append(errG.item())
        D_losses.append(errD.item())

        # Check how the generator is doing by saving G\'s output on fixed_noise
        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
			with torch.no_grad():
				fake = netG(fixed_noise).detach().cpu()
			img_list.append(vutils.make_grid(fake, padding=2, normalize=True))

		iters += 1