預設bert是ckpt，在進行後期優化和部署時，savedmodel方式更加友好寫。

train完成後，呼叫如下函式：

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("儲存savedmodel")

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

serving_dir：儲存目錄

seq_length：樣本長度

is_tpu_estimator: tpu標誌位