basic.py 文件源码

def grad(self, inputs, gout):
        (cond, ift, iff) = inputs
        (gz,) = gout
        first_part = switch(cond, gz, 0.)
        second_part = switch(cond, 0., gz)

        out = self(cond, ift, iff)
        if out.type.dtype in discrete_types:
            first_part = 0.
            second_part = 0.

        # cond does affect the elements of the output so it is connected.
        # For the sake of making the gradient convenient we assume that
        # condition + epsilon always triggers the same branch as condition
        condition_grad = cond.zeros_like().astype(theano.config.floatX)

        return (condition_grad, first_part, second_part)