D_loss = D_real_loss + D_fake_loss
D_loss.backward()
D_optimizer.step()
'''
训练生成器
'''
Net_G.zero_grad()
Noise_var = Variable(torch.randn(BATCH_SIZE, NOISE_DIM))
image_fake = Net_G(Noise_var,label_var)
D_fake = Net_D(image_fake,label_var)
G_loss = BCELoss()(D_fake, label_true_var)
G_loss.backward()
G_optimizer.step()
proBar.show(D_loss.data[0], G_loss.data[0])
Noise_var = Variable(torch.randn(BATCH_SIZE, NOISE_DIM))
y = (torch.ones(BATCH_SIZE) * 7).long()
y = one_hot(y)
y = Variable(y.cuda() if GPU_NUMS > 1 else y)
samples = Net_G(Noise_var,y)[:24]
img = torchvision.utils.make_grid( samples.data)
npimg = img.cpu().numpy()
plt.imshow(np.transpose(npimg, (1,2,0)))
if not os.path.exists('out/'):
os.makedirs('out/')
label_fake_G_var = Variable(onehot[label_fake].cuda() if CONFIG["GPU_NUMS"] > 0 else onehot[label_fake])
label_fake_D_var = Variable(fill[label_fake].cuda() if CONFIG["GPU_NUMS"] > 0 else fill[label_fake])
g_result = NetG(img_fake_var, label_fake_G_var)
d_result = NetD(g_result, label_fake_D_var)
d_result = d_result.squeeze()
D_LOSS_FAKE = BCELoss()(d_result, label_false_var)
D_train_loss = D_LOSS_REAL + D_LOSS_FAKE
D_train_loss.backward()
D_optimizer.step()
NetG.zero_grad()
img_fake = torch.randn((mini_batch, 100)).view(-1, 100, 1, 1)
label_fake = (torch.rand(mini_batch, 1) * 10).type(torch.LongTensor).squeeze()
img_fake_var = Variable(img_fake.cuda() if CONFIG["GPU_NUMS"] > 0 else img_fake)
label_fake_G_var = Variable(onehot[label_fake].cuda() if CONFIG["GPU_NUMS"] > 0 else onehot[label_fake])
label_fake_D_var = Variable(fill[label_fake].cuda() if CONFIG["GPU_NUMS"] > 0 else fill[label_fake])
g_result = NetG(img_fake_var, label_fake_G_var)
d_result = NetD(g_result, label_fake_D_var)
d_result = d_result.squeeze()
G_train_loss= BCELoss()(d_result, label_true_var)
G_train_loss.backward()
G_optimizer.step()
bar.show(epoch, D_train_loss.item(), G_train_loss.item())
test_images = NetG(fixed_z_, fixed_y_label_)
torchvision.utils.save_image(test_images.data[:100],'outputs/mnist_%03d.png' % (epoch),nrow=10,
normalize=True,range=(-1,1), padding=0)
