Feed_Forward.py

      
    def forward(self, x):
        ## TODO: Define the feedforward behavior of this model
        ## x is the input image and, as an example, here you may choose to include a pool/conv step:
        ## x = self.pool(F.relu(self.conv1(x)))
      
        x = self.pool1(F.relu(self.conv1(x)))
        x = self.drop1(x)
        x = self.pool2(F.relu(self.conv2(x)))
        x = self.drop2(x)
        x = self.pool3(F.relu(self.conv3(x)))
        x = self.drop3(x)
        x = self.pool4(F.relu(self.conv4(x)))
        x = self.drop4(x)
        x = self.pool5(F.relu(self.conv5(x)))
        x = self.drop5(x)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = self.drop6(x)
        x = self.fc2(x)
        # a modified x, having gone through all the layers of your model, should be returned
        return x