fix

09-Generative Adversarial network/simple_Gan.py

@@ -25,18 +25,15 @@ def to_img(x):
 
 # Image processing
 img_transform = transforms.Compose([
-                transforms.ToTensor(),
-                transforms.Normalize(mean=(0.5, 0.5, 0.5),
-                                     std=(0.5, 0.5, 0.5))])
+    transforms.ToTensor(),
+    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
+])
 # MNIST dataset
-mnist = datasets.MNIST(root='./data/',
-                       train=True,
-                       transform=img_transform,
-                       download=True)
+mnist = datasets.MNIST(
+    root='./data/', train=True, transform=img_transform, download=True)
 # Data loader
-dataloader = torch.utils.data.DataLoader(dataset=mnist,
-                                         batch_size=batch_size,
-                                         shuffle=True)
+dataloader = torch.utils.data.DataLoader(
+    dataset=mnist, batch_size=batch_size, shuffle=True)
 
 
 # Discriminator
@@ -47,10 +44,7 @@ def __init__(self):
             nn.Linear(784, 256),
             nn.LeakyReLU(0.2),
             nn.Linear(256, 256),
-            nn.LeakyReLU(0.2),
-            nn.Linear(256, 1),
-            nn.Sigmoid()
-        )
+            nn.LeakyReLU(0.2), nn.Linear(256, 1), nn.Sigmoid())
 
     def forward(self, x):
         x = self.dis(x)
@@ -64,11 +58,7 @@ def __init__(self):
         self.gen = nn.Sequential(
             nn.Linear(100, 256),
             nn.ReLU(True),
-            nn.Linear(256, 256),
-            nn.ReLU(True),
-            nn.Linear(256, 784),
-            nn.Tanh()
-        )
+            nn.Linear(256, 256), nn.ReLU(True), nn.Linear(256, 784), nn.Tanh())
 
     def forward(self, x):
         x = self.gen(x)
@@ -125,17 +115,17 @@ def forward(self, x):
         g_loss.backward()
         g_optimizer.step()
 
-        if (i+1) % 100 == 0:
+        if (i + 1) % 100 == 0:
             print('Epoch [{}/{}], d_loss: {:.6f}, g_loss: {:.6f} '
-                  'D real: {:.6f}, D fake: {:.6f}'
-                  .format(epoch, num_epoch, d_loss.data[0], g_loss.data[0],
-                          real_scores.data.mean(), fake_scores.data.mean()))
+                  'D real: {:.6f}, D fake: {:.6f}'.format(
+                      epoch, num_epoch, d_loss.data[0], g_loss.data[0],
+                      real_scores.data.mean(), fake_scores.data.mean()))
     if epoch == 0:
         real_images = to_img(real_img.cpu().data)
         save_image(real_images, './img/real_images.png')
 
     fake_images = to_img(fake_img.cpu().data)
-    save_image(fake_images, './img/fake_images-{}.png'.format(epoch+1))
+    save_image(fake_images, './img/fake_images-{}.png'.format(epoch + 1))
 
 torch.save(G.state_dict(), './generator.pth')
 torch.save(D.state_dict(), './discriminator.pth')