if this is our network:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
        self.fc4 = nn.Linear(320, 40)
        self.fc5 = nn.Linear(40, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc4(x))
        x = F.dropout(x, training=self.training)
        x = self.fc5(x)
        return F.log_softmax(x)

model = Net()
if args.cuda:
    model.cuda()


when you use print(model)
it just print the network components where you defined in init, like this:

Net (
  (conv1): Conv2d(1, 10, kernel_size=(5, 5), stride=(1, 1))
  (conv2): Conv2d(10, 20, kernel_size=(5, 5), stride=(1, 1))
  (conv2_drop): Dropout2d (p=0.5)
  (fc1): Linear (320 -> 50)
  (fc2): Linear (50 -> 10)
  (fc4): Linear (320 -> 40)
  (fc5): Linear (40 -> 10)
)


they are not the real data flow in the network. Please notice that i do not use fc1 and fc2, i use fc4 and fc5.
How can i know the real data flow gragh in my network? i need know what my network looks like. There was an embarassing bug that my network was not connected in the way i thought. It ends in the half way during the backprop prosess.