用savedmodel导出Tensorflow实验模型

请说明如何使用保存此模型

我尝试用

export\u strategies=tf.export\u saved\u模型（output\u dir，serving\u input\u fn=create\u serving\u input\u fn（nitems，nusers））替换export\u策略

，它返回以下错误消息

AttributeError:module'tensorflow'没有属性'export\u saved\u model

还尝试了

export\u strategies=tf.saved\u model（输出目录、服务输入目录、创建服务输入目录、使用目录））

TypeError:“DeprecationWrapper”对象不可调用

train_steps = int(0.5 + (1.0 * num_epochs * nusers) / batch_size)
    steps_in_epoch = int(0.5 + nusers / batch_size)
    print("Will train for {} steps, evaluating once every {} steps".format(train_steps, steps_in_epoch))
    def experiment_fn(output_dir):
        return tf.contrib.learn.Experiment(
            tf.contrib.factorization.WALSMatrixFactorization(
                num_rows = nusers, 
                num_cols = nitems,
                embedding_dimension = n_embeds,
                model_dir = output_dir),
            train_input_fn = read_dataset(tf.estimator.ModeKeys.TRAIN, input_path,batch_size, nitems, nusers, num_epochs,n_embeds, output_dir),
            eval_input_fn = read_dataset(tf.estimator.ModeKeys.EVAL, input_path, batch_size, nitems, nusers, num_epochs, n_embeds, output_dir),
            train_steps = train_steps,
            eval_steps = 1,
            min_eval_frequency = steps_in_epoch,
            export_strategies = tf.contrib.learn.utils.saved_model_export_utils.make_export_strategy(serving_input_fn = create_serving_input_fn(nitems, nusers))
        )