I wanted to build a monotonic neural net in PyTorch, specifically such that the weights of all layers are restricted to be positive. I want to know if this is possible to do within the object class itself, instead of adding constraints during optimization or instantiation.
As an example, consider the network -
class NeuralNet(nn.Module): def __init__(self, in_features): self.layer1 = nn.Linear(in_features, 10, bias=False) self.layer2 = nn.Linear(10, 20, bias=False) def forward(self, x): return self.layer2(self.layer1(x))
Now, I would like to, at every forward pass, set every weight value which is negative (<0) to 0. How do I do this appropriately? How do I ensure that autograd tracks weight updates correctly in this scenario?