Making DeepNet even bigger.

diff --git a/cnn-svrt.py b/cnn-svrt.py
index fb1cad9..4481049 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -223,11 +223,11 @@ class DeepNet2(nn.Module):
     def __init__(self):
         super(DeepNet2, self).__init__()
         self.conv1 = nn.Conv2d(  1,  32, kernel_size=7, stride=4, padding=3)
     def __init__(self):
         super(DeepNet2, self).__init__()
         self.conv1 = nn.Conv2d(  1,  32, kernel_size=7, stride=4, padding=3)

-        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.fc1 = nn.Linear(2048, 512)
+        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.fc1 = nn.Linear(4096, 512)

         self.fc2 = nn.Linear(512, 512)
         self.fc3 = nn.Linear(512, 2)
 
         self.fc2 = nn.Linear(512, 512)
         self.fc3 = nn.Linear(512, 2)
 


@@ -250,7 +250,7 @@ class DeepNet2(nn.Module):
         x = fn.max_pool2d(x, kernel_size=2)
         x = fn.relu(x)
 
         x = fn.max_pool2d(x, kernel_size=2)
         x = fn.relu(x)
 

-        x = x.view(-1, 2048)
+        x = x.view(-1, 4096)

 
         x = self.fc1(x)
         x = fn.relu(x)
 
         x = self.fc1(x)
         x = fn.relu(x)