l2 norm without sav-loss

src/model/transformations.py

@@ -40,9 +40,15 @@ class CNNProjection(nn.Module):
         x3 = self.conv_and_pool(x,self.conv15) #(N,Co)
         x = torch.cat((x1, x2, x3), 1) # (N,len(Ks)*Co)
         '''
+
+        x = F.relu(self.fc1(x))  # (N, C)
+
+        norm = x.norm(p=2, dim=1, keepdim=True)
+        x = x.div(norm.expand_as(x))
+
         x = self.dropout(x)  # (N, len(Ks)*Co)
-        logit = F.relu(self.fc1(x))  # (N, C)
-        return logit
+
+        return x
 
     def space_dimensions(self):
         return self.output_size