class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 20, 5, 1) self.conv2 = nn.Conv2d(20, 50, 5, 1) self.fc1 = nn.Linear(4 * 4 * 50, 500) self.fc2 = nn.Linear(500, 10) def forward(self, x): x = F.relu(self.conv1(x)) x = F.max_pool2d(x, 2, 2) x = F.relu(self.conv2(x)) x = F.max_pool2d(x, 2, 2) x = x.view(-1, 4 * 4 * 50) x = F.relu(self.fc1(x)) x = self.fc2(x) return F.log_softmax(x, dim=1)
This is the structure of Neural Network.
And I have the derivative of
conv1.weight, conv2.weight, fc1.weight, fc2.weight…by
Then I introduce loss function
how to use the derivative of
output and the derivative of
conv1.weight... to compute the derivative of
conv1.weight...? (output has more than 1 element)
I mean I don’t want to compute directly by loss.backward(), but I’d like to compute separately.