basic.py 文件源码

def grad(self, inputs, gout):
        (x, y) = inputs
        (gz,) = gout
        z = self(x, y)
        if z.type.dtype in discrete_types:
            # The gradient does not flow in if the output is discrete
            return [x.zeros_like(dtype=theano.config.floatX),
                    y.zeros_like(dtype=theano.config.floatX)]
        return [gz,
                -(x // y) * gz]