Hi everyone!
I’m new to Pytorch, and I’m having some trouble understanding computing layer sizes/the number of channels works.
I’m currently looking at this code from a NN for the Fashion-MNIST dataset (this neural net is working on the Fashion MNIST data in batch sizes of 64, using SGD, running for 10 epochs).
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.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

My confusion is where the number “320” comes from, both in x.view(-1, 320), and as the first parameter in the first fully connected layer. How does one arrive at this number? What values contribute to it being 320?
Thank you! I look forward to exploring Pytorch much more over the coming months and years.