import tensorflow as tf

            tf.logging.info("%s\tshape    %s", v.name[:-2].ljust(80),
                            str(v.shape).ljust(20))
            v_size = np.prod(np.array(v.shape.as_list())).tolist() # mutiple all dimension size
            total_size += v_size
        tf.logging.info("Total trainable variables size: %d", total_size)

        learning_rate = get_learning_rate_decay(params.learning_rate,
                                                global_step, params)
        learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
        tf.summary.scalar("learning_rate", learning_rate)

        # Create optimizer
        opt = tf.train.AdamOptimizer(learning_rate,
                                     beta1=params.adam_beta1,
                                     beta2=params.adam_beta2,
                                     epsilon=params.adam_epsilon)
