So far I’ve been adding layers to my neural nets by using nn.Linear
, but suppose I wanted to do a forward pass on my layer by doing self.nlin(self.Psi(x)
, where nlin
would be a nonlinearity, say nn.softshrink
, parametrized by some lambda
, and I wanted to make my lambda
a training parameter.
How would I do this? Do I have to write my own version of softshrink
? If so what will I need to do in order to play nice with the other nn
modules and get autodifferentiation to work properly?