我遵循了这个很棒的教程，并成功地在CloudML上训练了一个模型。我的代码还离线进行预测，但是现在我正在尝试使用Cloud
ML进行预测并遇到一些问题。

为了部署我的模型，我遵循了本教程。现在，我有一个生成TFRecordsvia的代码，apache_beam.io.WriteToTFRecord并且我想对此进行预测TFRecords。为此，我关注本文，我的命令如下所示：

gcloud ml-engine jobs submit prediction $JOB_ID --model $MODEL --input-paths gs://"$FILE_INPUT".gz --output-path gs://"$OUTPUT"/predictions --region us-west1 --data-format TF_RECORD_GZIP

但是我只有错误： 'Exception during running the graph: Expected serialized to be a scalar, got shape: [64]

看起来它期望数据采用不同的格式。我在这里找到了JSON的格式规范，但找不到如何使用TFrecords进行格式化。

更新：这是输出 saved_model_cli show --all --dir

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

signature_def['prediction']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['example_proto'] tensor_info:
    dtype: DT_STRING
    shape: unknown_rank
    name: input:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['probability'] tensor_info:
    dtype: DT_FLOAT
    shape: (1, 1)
    name: probability:0
  Method name is: tensorflow/serving/predict

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['example_proto'] tensor_info:
    dtype: DT_STRING
    shape: unknown_rank
    name: input:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['probability'] tensor_info:
    dtype: DT_FLOAT
    shape: (1, 1)
    name: probability:0
  Method name is: tensorflow/serving/predict