Python Tensorflow'；找不到元图Def

我正在尝试使用Tensorflow服务（加载到docker上，如果这有什么区别的话）为经过培训的Tensorflow模型提供服务

培训完模型后，我使用以下代码保存了它：

prediction_signature = (
            tf.saved_model.signature_def_utils.build_signature_def(
                inputs={'verif': tensor_info_input, 'enroll': tensor_info_input},
                outputs={'similarity_matrix': tensor_info_output},
                method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))

with tf.Session(graph=loaded_graph) as sess:
    # Restore from checkpoint
    loader = tf.train.import_meta_graph(trained_checkpoint_prefix + '.meta')
    loader.restore(sess, trained_checkpoint_prefix)

    # Export checkpoint to SavedModel
    builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
    builder.add_meta_graph_and_variables(sess,
                                         [tf.saved_model.TRAINING, tf.saved_model.SERVING],
                                         signature_def_map={
                                            tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature, 
                                         })
    builder.save()

在版本文件夹上使用--all标志运行saved_model_cli后，我得到以下响应：

但是-在尝试发球时，我仍然会遇到以下错误：

加载可服务：{name:服务版本：0}失败：找不到：无法 找不到与提供的标记匹配的元图def:{serve}

你知道这是什么原因吗

---

谢谢

经过多次尝试和错误，问题似乎是我有tf.saved_model.TRAINING和tf.saved_model.service标签

当我在构建模型时移除tf.saved_model.TRAINING标签时，一切都正常了

MetaGraphDef with tag-set: 'train, serve' contains the following SignatureDefs:

signature_def['serving_default']:   The given SavedModel SignatureDef contains the following input(s):
    inputs['enroll'] tensor_info:
        dtype: DT_FLOAT
        shape: (80, 20, 40)
        name: Const:0
    inputs['verif'] tensor_info:
        dtype: DT_FLOAT
        shape: (80, 20, 40)
        name: Const:0   The given SavedModel SignatureDef contains the following output(s):
    outputs['similarity_matrix'] tensor_info:
        dtype: DT_FLOAT
        shape: (20, 4)
        name: add_1:0   Method name is: tensorflow/serving/predict