Publish trained generator

model.py

@@ -33,21 +33,3 @@ class Generator(nn.Module):
         conditioned = torch.cat([noise, self.label_embedding(labels)], dim=1)
         features = self.project(conditioned).view(-1, 128, 7, 7)
         return self.decoder(features)
-
-
-class Discriminator(nn.Module):
-    def __init__(self, num_classes=NUM_CLASSES):
-        super().__init__()
-        self.label_embedding = nn.Embedding(num_classes, 28 * 28)
-        self.features = nn.Sequential(
-            nn.utils.spectral_norm(nn.Conv2d(2, 64, 4, 2, 1)),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.utils.spectral_norm(nn.Conv2d(64, 128, 4, 2, 1)),
-            nn.LeakyReLU(0.2, inplace=True),
-            nn.Flatten(),
-            nn.utils.spectral_norm(nn.Linear(128 * 7 * 7, 1)),
-        )
-
-    def forward(self, images, labels):
-        label_map = self.label_embedding(labels).view(-1, 1, 28, 28)
-        return self.features(torch.cat([images, label_map], dim=1)).flatten()