Making an even deeper model.

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 4481049..cb94184 100755 (executable)
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -264,6 +264,61 @@ class DeepNet2(nn.Module):
 
 ######################################################################
 
+class DeepNet3(nn.Module):
+    name = 'deepnet3'
+
+    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, 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)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = fn.max_pool2d(x, kernel_size=2)
+        x = fn.relu(x)
+
+        x = self.conv2(x)
+        x = fn.max_pool2d(x, kernel_size=2)
+        x = fn.relu(x)
+
+        x = self.conv3(x)
+        x = fn.relu(x)
+
+        x = self.conv4(x)
+        x = fn.relu(x)
+
+        x = self.conv5(x)
+        x = fn.max_pool2d(x, kernel_size=2)
+        x = fn.relu(x)
+
+        x = self.conv6(x)
+        x = fn.relu(x)
+
+        x = self.conv7(x)
+        x = fn.relu(x)
+
+        x = x.view(-1, 4096)
+
+        x = self.fc1(x)
+        x = fn.relu(x)
+
+        x = self.fc2(x)
+        x = fn.relu(x)
+
+        x = self.fc3(x)
+
+        return x
+
+######################################################################
+
 def nb_errors(model, data_set):
     ne = 0
     for b in range(0, data_set.nb_batches):
@@ -385,7 +440,7 @@ else:
 
 ########################################
 model_class = None
-for m in [ AfrozeShallowNet, AfrozeDeepNet, DeepNet2 ]:
+for m in [ AfrozeShallowNet, AfrozeDeepNet, DeepNet2, DeepNet3 ]:
     if args.model == m.name:
         model_class = m
         break
@@ -415,7 +470,6 @@ for problem_number in map(int, args.problems.split(',')):
     ##################################################
     # Tries to load the model
 
-    need_to_train = False
     try:
         model_state_dict, nb_epochs_done = torch.load(model_filename)
         model.load_state_dict(model_state_dict)
@@ -469,7 +523,7 @@ for problem_number in map(int, args.problems.split(',')):
     ##################################################
     # Test if necessary
 
-    if need_to_train or args.test_loaded_models:
+    if nb_epochs_done < args.nb_epochs or args.test_loaded_models:
 
         t = time.time()