nlinalg.py 文件源码

def grad(self, inputs, g_outputs):
        r"""The gradient function should return

            .. math:: V\frac{\partial X^{-1}}{\partial X},

        where :math:`V` corresponds to ``g_outputs`` and :math:`X` to
        ``inputs``. Using the `matrix cookbook
        <http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=3274>`_,
        one can deduce that the relation corresponds to

            .. math:: (X^{-1} \cdot V^{T} \cdot X^{-1})^T.

        """
        x, = inputs
        xi = self(x)
        gz, = g_outputs
        # TT.dot(gz.T,xi)
        return [-matrix_dot(xi, gz.T, xi).T]