lista_network.py 文件源码

def _get_cost(self, outputs):
        """Construct the cost function from the outputs of the last layer. This
        will be used through SGD to train the network.

        Parameters
        ----------
        outputs: tuple fo tensors (n_out)
            a tuple of tensor containing the output from the last layer of the
            network

        Returns
        -------
        cost: a tensor computing the cost function of the network.
        reg: a tensor for computing regularization of the parameters.
            It should be None if no regularization is needed.
        """
        Zk, X, lmbd = outputs

        with tf.name_scope("reconstruction_zD"):
            rec = tf.matmul(Zk, tf.constant(self.D))

        with tf.name_scope("norm_2"):
            Er = tf.multiply(
                tf.constant(.5, dtype=tf.float32),
                tf.reduce_mean(tf.reduce_sum(tf.squared_difference(rec, X),
                                             reduction_indices=[1])))

        with tf.name_scope("norm_1"):
            l1 = lmbd * tf.reduce_mean(tf.reduce_sum(
                tf.abs(Zk), reduction_indices=[1]))

        return tf.add(Er, l1, name="cost")