Update models/jason_cnn.py

models/jason_cnn.py

@@ -1,14 +1,17 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-class CNN(nn.Module):
-    def __init__(self, in_channels=1, num_classes=7, filters=(16, 32), dropout=0.25):
-        super(CNN, self).__init__()
+class CNN_4Layer(nn.Module):
+    def __init__(self, in_channels, num_classes, filters=(16,32,64,128), dropout=0.25):
+        super(CNN_4Layer, self).__init__()
+
         self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=filters[0], kernel_size=3, padding=1)
         self.conv2 = nn.Conv2d(in_channels=filters[0], out_channels=filters[1], kernel_size=3, padding=1)
+        self.conv3 = nn.Conv2d(in_channels=filters[1], out_channels=filters[2], kernel_size=3, padding=1)
+        self.conv4 = nn.Conv2d(in_channels=filters[2], out_channels=filters[3], kernel_size=3, padding=1)
         self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
         self.dropout = nn.Dropout(dropout)
-        self.fc1 = nn.Linear(filters[1] * 12 * 12, num_classes)
+        self.fc1 = nn.Linear(filters[3] * 3 * 3, num_classes)
 
     def forward(self, x):
         x = F.relu(self.conv1(x))
@@ -17,6 +20,12 @@ class CNN(nn.Module):
         x = F.relu(self.conv2(x))
         x = self.pool(x)
         x = self.dropout(x)
+        x = F.relu(self.conv3(x))
+        x = self.pool(x)
+        x = self.dropout(x)
+        x = F.relu(self.conv4(x))
+        x = self.pool(x)
+        x = self.dropout(x)
         x = x.reshape(x.shape[0], -1)
         x = self.fc1(x)
         return x