Hi all,
I am new to pytorch and also new to autograd.  My question is : Do I need to compute the partial derivatives for my functions parameters?
For example: My new layer want to compute a 1-d gaussian probability density value, the function is,
f(x)=a * exp^((x-b)^2 / c)) ,
where a,b,c are the parameters need to be updated. I think all of these operations are basic operation and the output is a scalar. Do I still need to write backward code like we do in Caffe?  Or May I just define a new module only with forward function then pytorch will compute the parameters’ derivatives automatically for me?