ranknet.py 文件源码

def _setup_training(self):
        """
        Set up a data flow graph for fine tuning
        """
        layer_num = self.layer_num
        act_func = ACTIVATE_FUNC[self.activate_func]
        sigma = self.sigma
        lr = self.learning_rate
        weights = self.weights
        biases = self.biases
        data1, data2 = self.data1, self.data2
        batch_size = self.batch_size
        optimizer = OPTIMIZER[self.optimizer]
        with tf.name_scope("training"):
            s1 = self._obtain_score(data1, weights, biases, act_func, "1")
            s2 = self._obtain_score(data2, weights, biases, act_func, "2")
            with tf.name_scope("cost"):
                sum_cost = tf.reduce_sum(tf.log(1 + tf.exp(-sigma*(s1-s2))))
                self.cost = cost = sum_cost / batch_size
        self.optimize = optimizer(lr).minimize(cost)

        for n in range(layer_num-1):
            tf.histogram_summary("weight"+str(n), weights[n])
            tf.histogram_summary("bias"+str(n), biases[n])
        tf.scalar_summary("cost", cost)