lstm_model.py 文件源码

def add_optimizer(self):
        self.global_step = tf.Variable(0, trainable=False)

        learning_rate = tf.train.exponential_decay(0.01, self.global_step, 50,
                                   0.1, staircase=True)

        optimizer = tf.train.GradientDescentOptimizer(learning_rate)
        gradients = optimizer.compute_gradients(self.loss)

        self.apply_gradient_op = optimizer.apply_gradients(gradients, self.global_step)

        for var in tf.trainable_variables():
            tf.histogram_summary(var.op.name, var)

        for grad, var in gradients:
            if grad is not None:
                tf.histogram_summary(var.op.name + '/gradients', grad)

        return self.apply_gradient_op