vanilla gan added

Author: yunjey

tutorials/10 - Generative Adversarial Network/main-gpu.py

@@ -27,20 +27,22 @@ class Discriminator(nn.Module):
     def __init__(self):
         super(Discriminator, self).__init__()
         self.fc1 = nn.Linear(784, 256)
-        self.fc2 = nn.Linear(256, 1)
+        self.fc2 = nn.Linear(256, 256)
+        self.fc3 = nn.Linear(256, 1)
 
     def forward(self, x):
         h = F.relu(self.fc1(x))
-        out = F.sigmoid(self.fc2(h))
+        h = F.relu(self.fc2(h))
+        out = F.sigmoid(self.fc3(h))
         return out
 
 # Generator Model
 class Generator(nn.Module):
     def __init__(self):
         super(Generator, self).__init__()
         self.fc1 = nn.Linear(128, 256)
-        self.fc2 = nn.Linear(256, 512)
-        self.fc3 = nn.Linear(512, 784)
+        self.fc2 = nn.Linear(256, 256)
+        self.fc3 = nn.Linear(256, 784)
 
     def forward(self, x):
         h = F.leaky_relu(self.fc1(x))
@@ -101,7 +103,7 @@ def forward(self, x):
     # Save the sampled images
     fake_images = fake_images.view(fake_images.size(0), 1, 28, 28)
     torchvision.utils.save_image(fake_images.data, 
-        './data2/fake_samples_%d.png' %epoch+1)
+        './data/fake_samples_%d.png' %(epoch+1))
 
 # Save the Models 
 torch.save(generator.state_dict(), './generator.pkl')

tutorials/10 - Generative Adversarial Network/main.py

@@ -27,20 +27,22 @@ class Discriminator(nn.Module):
     def __init__(self):
         super(Discriminator, self).__init__()
         self.fc1 = nn.Linear(784, 256)
-        self.fc2 = nn.Linear(256, 1)
+        self.fc2 = nn.Linear(256, 256)
+        self.fc3 = nn.Linear(256, 1)
 
     def forward(self, x):
         h = F.relu(self.fc1(x))
-        out = F.sigmoid(self.fc2(h))
+        h = F.relu(self.fc2(h))
+        out = F.sigmoid(self.fc3(h))
         return out
 
 # Generator Model
 class Generator(nn.Module):
     def __init__(self):
         super(Generator, self).__init__()
         self.fc1 = nn.Linear(128, 256)
-        self.fc2 = nn.Linear(256, 512)
-        self.fc3 = nn.Linear(512, 784)
+        self.fc2 = nn.Linear(256, 256)
+        self.fc3 = nn.Linear(256, 784)
 
     def forward(self, x):
         h = F.leaky_relu(self.fc1(x))
@@ -101,7 +103,7 @@ def forward(self, x):
     # Save the sampled images
     fake_images = fake_images.view(fake_images.size(0), 1, 28, 28)
     torchvision.utils.save_image(fake_images.data, 
-        './data2/fake_samples_%d.png' %epoch+1)
+        './data/fake_samples_%d.png' %(epoch+1))
 
 # Save the Models 
 torch.save(generator.state_dict(), './generator.pkl')