默认bert是ckpt，在进行后期优化和部署时，savedmodel方式更加友好写。

train完成后，调用如下函数：

def save_savedmodel(estimator, serving_dir, seq_length, is_tpu_estimator):
    feature_map = {
        "input_ids": tf.placeholder(tf.int32, shape=[None, seq_length], name='input_ids'),
        "input_mask": tf.placeholder(tf.int32, shape=[None, seq_length], name='input_mask'),
        "segment_ids": tf.placeholder(tf.int32, shape=[None, seq_length], name='segment_ids'),
        "label_ids": tf.placeholder(tf.int32, shape=[None], name='label_ids'),
    }
    serving_input_receiver_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(feature_map)
    estimator.export_savedmodel(serving_dir,
                                serving_input_receiver_fn,
                                strip_default_attrs=True)
    print("保存savedmodel")

estimator：estimator = Estimator(model_fn=model_fn,params={},config=run_config)

serving_dir：存储目录

seq_length：样本长度

is_tpu_estimator: tpu标志位