rnn.py 文件源码

def minimize(self, data, t_label, s, optimizer):
        label = tf.Variable(t_label, name="label", trainable=False, dtype=tf.float32)
        s.run(tf.initialize_variables([label]))
        with tf.name_scope('cost_function'):
            cost = self.output_layer.compute_loss(tf.transpose(self.ht), label)
        with tf.name_scope('minimization'):
            #optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
            return optimizer.minimize(cost, aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)