This repository was archived by the owner on Oct 15, 2019. It is now read-only.
This repository was archived by the owner on Oct 15, 2019. It is now read-only.
Can I create a variable shared by forward and back propagation in customop('numpy')? #173
Description
I do not quite understand the mechanism behind @customop('numpy').
I find that there's an intermediate variable 'Q' which is expensive to compute and appears in computing both the output and gradient.
By the way, I also wonder if I can create gradient function for multiple parameters (e.g. w1 and w2 as in the code below).
e.g.
@customop('numpy')
def my_operator(X,w1,w2):
Q = f(X,w1,w2)
H = g1(Q)
return H
def my_operator_grad1(ans,X,w1,w2):
def grad1(g):
Q = f(X,w1,w2)
R = g2(Q)
return R
return grad1
def my_operator_grad2(ans,X,w1,w2):
def grad2(g):
Q = f(X,w1,w2)
R = g3(Q)
return R
return grad2
my_operator.def_grad(my_operator_grad1,argnum=1)
my_operator.def_grad(my_operator_grad2,argnum=2)
Thanks!