target_fake = target_fake.cuda()
# for make Fake data
noise_size = 100
z = Variable(torch.randn((batch_size, noise_size, 1, 1)))
if torch.cuda.is_available():
z = z.cuda()
discriminator = discriminator.cuda()
generator = generator.cuda()
if i % 1 == 0:
# Training D with Real Data
D_optimizer.zero_grad()
D_real_decision = discriminator.forward(real_data)
D_real_loss = D_loss_function(D_real_decision, target_real)
# Training D with Fake Data
fake_data = generator.forward(z)
D_fake_decision = discriminator.forward(fake_data)
D_fake_loss = D_loss_function(D_fake_decision, target_fake)
D_loss = D_real_loss + D_fake_loss
if i % 10 == 0:
print('{0}: D_loss is {1}'.format(i, D_loss))
D_loss.backward()
D_optimizer.step()
if i % 1 == 0:
# for make Fake data
noise_size = 100
z = torch.randn(batch_size, noise_size)
fake_label = Variable(
torch.LongTensor(np.random.randint(0, 10, batch_size)))
if torch.cuda.is_available():
z = z.cuda()
fake_label = fake_label.cuda()
discriminator = discriminator.cuda()
generator = generator.cuda()
if i % 1 == 0:
# Training D with Real Data
D_optimizer.zero_grad()
D_real_decision = discriminator.forward(real_data, real_label)
D_real_loss = D_loss_function(D_real_decision, target_real)
# Training D with Fake Data
fake_data = generator.forward(z, fake_label)
D_fake_decision = discriminator.forward(fake_data, fake_label)
D_fake_loss = D_loss_function(D_fake_decision, target_fake)
D_loss = D_real_loss + D_fake_loss
if i % 10 == 0:
print('{0}: D_loss is {1}'.format(i, D_loss))
D_loss.backward()
D_optimizer.step()
if i % 1 == 0:
