class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool  = nn.MaxPool2d(2,2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1   = nn.Linear(16*5*5, 120)
        self.fc2   = nn.Linear(120, 84)
        self.fc3   = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16*5*5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

net = Net()

In Pytorch tutorial we have the above network model, but I was wondering about the input size of the first fully connected layer - 16 * 5 * 5.
First I think the 16 refers to the output channel of the last conv layer, yet I am not convinced that
x = x.view(-1, 1655)
actually flatten the tensor by their channel. So is my understanding correct?
Also I don’t know why there is the 55: Is it referring to the kernel size in the above conv layer? However I think the tensor we would like to send to fc layer is the image of 16 channels, so 55 doesn’t have any specific meaning here?
Thanks!