预测失败：内容必须是标量

我已成功训练、导出并上传我的“retrained_graph.pb”到 ML Engine。我的导出脚本如下：

import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.saved_model import builder as saved_model_builder

input_graph = 'retrained_graph.pb'
saved_model_dir = 'my_model'

with tf.Graph().as_default() as graph:
  # Read in the export graph
  with tf.gfile.FastGFile(input_graph, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      tf.import_graph_def(graph_def, name='')

  # Define SavedModel Signature (inputs and outputs)
  in_image = graph.get_tensor_by_name('DecodeJpeg/contents:0')
  inputs = {'image_bytes': tf.saved_model.utils.build_tensor_info(in_image)}

  out_classes = graph.get_tensor_by_name('final_result:0')
  outputs = {'prediction_bytes': tf.saved_model.utils.build_tensor_info(out_classes)}

  signature = tf.saved_model.signature_def_utils.build_signature_def(
      inputs=inputs,
      outputs=outputs,
      method_name='tensorflow/serving/predict'
  )

  with tf.Session(graph=graph) as sess:
    # Save out the SavedModel.
    b = saved_model_builder.SavedModelBuilder(saved_model_dir)
    b.add_meta_graph_and_variables(sess,
                               [tf.saved_model.tag_constants.SERVING],
                               signature_def_map={'serving_default': signature})
    b.save() 

我使用以下命令构建预测 Json：

# Copy the image to local disk.
gsutil cp gs://cloud-ml-data/img/flower_photos/tulips/4520577328_a94c11e806_n.jpg flower.jpg

# Create request message in json format.
python -c 'import base64, sys, json; img = base64.b64encode(open(sys.argv[1], "rb").read()); print json.dumps({"image_bytes": {"b64": img}}) ' flower.jpg &> request.json

# Call prediction service API to get classifications
gcloud ml-engine predict --model ${MODEL_NAME} --json-instances request.json

然而，这失败了，响应如下：

{
  "error": "Prediction failed: Error during model execution: AbortionError(code=StatusCode.INVALID_ARGUMENT, details=\"contents must be scalar, got shape [1]\n\t [[Node: Deco
deJpeg = DecodeJpeg[_output_shapes=[[?,?,3]], acceptable_fraction=1, channels=3, dct_method=\"\", fancy_upscaling=true, ratio=1, try_recover_truncated=false, _device=\"/job:l
ocalhost/replica:0/task:0/device:CPU:0\"](_arg_DecodeJpeg/contents_0_0)]]\")"
}

感谢任何帮助，我很接近我可以品尝它：D

为什么你有这一行：

 in_image = graph.get_tensor_by_name('DecodeJpeg/contents:0')?

inputs = {'image_bytes': tf.saved_model.utils.build_tensor_info(in_image)}

这里的形状是标量。您能否确保使用形状创建输入[None]

https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/flowers/trainer/model.py#L364 https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/flowers/trainer/model.py#L364