Create model.py

model.py

@@ -0,0 +1,37 @@
+import torch
+import torch.nn as nn
+
+NOISE_DIM = 256
+
+class Generator(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.fc = nn.Linear(NOISE_DIM, 4*4*512)
+
+        self.net = nn.Sequential(
+            nn.BatchNorm2d(512),
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(512, 256, 3, padding=1),
+            nn.BatchNorm2d(256),
+            nn.ReLU(True),
+
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(256, 128, 3, padding=1),
+            nn.BatchNorm2d(128),
+            nn.ReLU(True),
+
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(128, 64, 3, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(True),
+
+            nn.Upsample(scale_factor=2),
+            nn.Conv2d(64, 3, 3, padding=1),
+            nn.Tanh()
+        )
+
+    def forward(self, noise):
+        x = self.fc(noise)
+        x = x.view(-1, 512, 4, 4)
+        return self.net(x)