basic.py 文件源码

def grad(self, inp, grads):
        s, = inp
        dt, = grads
        if s.type.dtype in float_dtypes:
            assert dt.type.dtype in float_dtypes
            return [scalar_from_tensor(dt)]

        # If the input dtype is an integer, then so is the output dtype,
        # and the "zero" gradient can be represented in that int dtype.
        # Currently, theano.grad insists that the dtype of the returned
        # gradient has a float dtype, so we use floatX.
        if s.type.dtype in discrete_dtypes:
            return [s.zeros_like().astype(theano.config.floatX)]

        raise NotImplementedError("grad not implemented for complex dtypes")