text_classification_model_simple.py 文件源码

def optimize(self, loss, global_step,
                 learning_rate_initial=TC_LEARNING_RATE_INITIAL,
                 learning_rate_decay=TC_LEARNING_RATE_DECAY,
                 learning_rate_decay_steps=TC_LEARNING_RATE_DECAY_STEPS):
        """
        Creates a learning rate and an optimizer for the loss
        :param tf.Tensor loss: the tensor with the loss of the model
        :param tf.Tensor global_step: the global step for training
        :param int learning_rate_initial: the initial learning rate
        :param int learning_rate_decay: the decay of the learning rate
        :param int learning_rate_decay_steps: the number of steps to decay the learning rate
        :return (tf.Tensor, tf.Tensor): a tuple with the optimizer and the learning rate
        """
        learning_rate = tf.train.exponential_decay(learning_rate_initial, global_step,
                                                   learning_rate_decay_steps,
                                                   learning_rate_decay,
                                                   staircase=True, name='learning_rate')
        # optimizer
        optimizer = tf.train.RMSPropOptimizer(learning_rate)
        # optimizer = tf.train.GradientDescentOptimizer(learning_rate)
        # optimizer = tf.train.AdamOptimizer(learning_rate)
        optimizer = optimizer.minimize(loss, global_step=global_step)
        return optimizer, learning_rate