diff --git a/sagan/model.py b/sagan/model.py
index 2717b14..d0774e6 100644
--- a/sagan/model.py
+++ b/sagan/model.py
@@ -22,21 +22,21 @@ def __init__(self, img_channels, h_dim, img_size):
         initialize_modules(self, init_type='ortho')
 
     def forward(self, x):
-        x = self.disc(x)
-        x = x.view(-1, self.in_features)
-        return self.fc(x)
+        x = self.disc(x)    # (bs, ch, h, w)      -> (bs, h_dim*8, 1, 1)
+        x = x.squeeze()     # (bs, h_dim*8, 1, 1) -> (bs, h_dim*8)
+        return self.fc(x)   # (bs, h_dim*8)       -> (bs, 1)
 
 
 class Generator(nn.Module):
     def __init__(self, h_dim, z_dim, img_channels, img_size):
         super().__init__()
-        self.min_hw = (img_size // (2 ** 6)) ** 2
+        self.min_hw = (img_size // (2 ** 6))
         self.h_dim = h_dim
-        self.project = SN_Linear(in_features=z_dim, out_features=h_dim*8 * self.min_hw ** 2, bias=False)
+        self.project = SN_Linear(in_features=z_dim, out_features=h_dim*8 * self.min_hw**2, bias=False)
         self.gen = nn.Sequential(
             nn.BatchNorm2d(h_dim*8, momentum=0.9),
             nn.ReLU(),
-            ConvNormAct(h_dim*8, h_dim*8, 'sn', None, activation='relu', normalization='bn'),
+            ConvNormAct(h_dim*8, h_dim*8, 'sn', 'up', activation='relu', normalization='bn'),
             ConvNormAct(h_dim*8, h_dim*8, 'sn', 'up', activation='relu', normalization='bn'),
             ConvNormAct(h_dim*8, h_dim*4, 'sn', 'up', activation='relu', normalization='bn'),
             ConvNormAct(h_dim*4, h_dim*2, 'sn', 'up', activation='relu', normalization='bn'),
@@ -50,6 +50,10 @@ def __init__(self, h_dim, z_dim, img_channels, img_size):
 
     def forward(self, x):
         batch_size = x.size(0)
-        x = self.project(x)
-        x = x.view(batch_size, self.h_dim*8, self.min_hw, self.min_hw)
-        return self.gen(x)
+        x = self.project(x)     # (bs, z_dim)                   -> (bs, h_dim*8*hw_min**2)
+        x = x.view(
+            batch_size,
+            self.h_dim*8,
+            self.min_hw,
+            self.min_hw)        # (bs, h_dim*8*hw_min**2)       -> (bs, h_dim*8, hw_min, hw_min)
+        return self.gen(x)      # (bs, h_dim*8, hw_min, hw_min) -> (bs, ch, w, h)