Added DeepNet3.

diff --git a/cnn-svrt.py b/cnn-svrt.py
index cb94184..8baaacb 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -268,17 +268,17 @@ class DeepNet3(nn.Module):
     name = 'deepnet3'
 
     def __init__(self):
-        super(DeepNet2, self).__init__()
+        super(DeepNet3, self).__init__()
         self.conv1 = nn.Conv2d(  1,  32, kernel_size=7, stride=4, padding=3)
-        self.conv2 = nn.Conv2d( 32, 256, kernel_size=5, padding=2)
-        self.conv3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
-        self.conv4 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
-        self.conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
-        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
-        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
-        self.fc1 = nn.Linear(4096, 512)
-        self.fc2 = nn.Linear(512, 512)
-        self.fc3 = nn.Linear(512, 2)
+        self.conv2 = nn.Conv2d( 32, 128, kernel_size=5, padding=2)
+        self.conv3 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+        self.conv5 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+        self.conv6 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+        self.conv7 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(2048, 256)
+        self.fc2 = nn.Linear(256, 256)
+        self.fc3 = nn.Linear(256, 2)
 
     def forward(self, x):
         x = self.conv1(x)
@@ -305,7 +305,7 @@ class DeepNet3(nn.Module):
         x = self.conv7(x)
         x = fn.relu(x)
 
-        x = x.view(-1, 4096)
+        x = x.view(-1, 2048)
 
         x = self.fc1(x)
         x = fn.relu(x)