And fix ...
authorFrancois Fleuret <francois@fleuret.org>
Fri, 30 Jun 2017 08:47:43 +0000 (10:47 +0200)
committerFrancois Fleuret <francois@fleuret.org>
Fri, 30 Jun 2017 08:47:43 +0000 (10:47 +0200)
cnn-svrt.py

index fab2772..7dc6dff 100755 (executable)
@@ -250,10 +250,10 @@ class DeepNet2(nn.Module):
         super(DeepNet2, self).__init__()
         self.nb_channels = 512
         self.conv1 = nn.Conv2d(  1,  32, kernel_size=7, stride=4, padding=3)
-        self.conv2 = nn.Conv2d( 32, nb_channels, kernel_size=5, padding=2)
-        self.conv3 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
-        self.conv4 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
-        self.conv5 = nn.Conv2d(nb_channels, nb_channels, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d( 32, self.nb_channels, kernel_size=5, padding=2)
+        self.conv3 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+        self.conv4 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+        self.conv5 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
         self.fc1 = nn.Linear(16 * self.nb_channels, 512)
         self.fc2 = nn.Linear(512, 512)
         self.fc3 = nn.Linear(512, 2)