spectral normalization

def _spectral_norm(self):
        """ spectral normalization """
        w = self.params.get('weight').data(self.ctx)
        w_mat = nd.reshape(w, [w.shape[0], -1])

        _u = self.u.data(self.ctx)
        _v = None

        for _ in range(POWER_ITERATION):
            _v = nd.L2Normalization(nd.dot(_u, w_mat))
            _u = nd.L2Normalization(nd.dot(_v, w_mat.T))

        sigma = nd.sum(nd.dot(_u, w_mat) * _v)
        if sigma == 0.:
            sigma = EPSILON

        with autograd.pause():
            self.u.set_data(_u)

        return w / sigma