fft.py 文件源码

def grad(self, inputs, output_grads):
        gout, = output_grads
        s = inputs[1]
        gf = curfft_op(gout, s)
        # Multiply the last dimension of the gradient by 2, they represent
        # both positive and negative frequencies, except the first
        # and last elements (for even transforms) which are unique.
        idx = [slice(None)] * (gf.ndim - 2) \
            + [slice(1, (s[-1] // 2) + (s[-1] % 2))] + [slice(None)]
        gf = T.set_subtensor(gf[idx], gf[idx] * 2)
        return [gf, DisconnectedType()()]