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我复制并使用了这段代码。网址： http: //fdahms.com/2017/03/05/tensorflow-serving-jvm-client/

但是部署版本时出现错误。

Model validation failed: Serving metagraph must contain exactly one SignatureDef with key: serving_default

我试图参考https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/mnist_saved_model.py修复代码

import tensorflow as tf


x = tf.placeholder(tf.float32, shape=(None))
y = tf.placeholder(tf.float32, shape=(None))

three = tf.Variable(3, dtype= tf.float32)
z = tf.scalar_mul(three, x) + y

import os
from tensorflow.python.util import compat

model_version = 1
path = os.path.join("three_x_plus_y", str(model_version))
builder = tf.saved_model.builder.SavedModelBuilder(path)

legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')

tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
tensor_info_z = tf.saved_model.utils.build_tensor_info(z)

prediction_signature = (
      tf.saved_model.signature_def_utils.build_signature_def(
          inputs= {'egg': tensor_info_x, 'bacon': tensor_info_y},
          outputs= {'spam': tensor_info_z},
          method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))


with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    builder.add_meta_graph_and_variables(
        sess,[tf.saved_model.tag_constants.SERVING],
        signature_def_map= {
            "magic_model": prediction_signature},
        legacy_init_op=legacy_init_op
        )
    builder.save()

但我得到了同样的错误......

我正在使用“谷歌云机器运行引擎”我需要帮助..感谢您的阅读。

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1 回答 1

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signature_def_map将from中的键更改magic_model为serving_default。

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    builder.add_meta_graph_and_variables(
        sess,[tf.saved_model.tag_constants.SERVING],
        signature_def_map= {
            "serving_default": prediction_signature},
        legacy_init_op=legacy_init_op
        )
于 2017-07-20T07:25:25.440 回答