Get predictions from deployed TF for poet model in google cloud - tensorflow

Question

I deployed my retrained TF for poet model in google cloud. currently, I am trying to get predictions from that. But it gives the following error.
"error": "Prediction failed: Error during model execution: AbortionError(code=StatusCode.INVALID_ARGUMENT, details=\"contents must be scalar, got shape [1]\n\t [[{{node DecodeJpeg}}]]\")"
Following code, I used to get serving model
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': 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()
request.json generating code
python -c 'import base64, sys, json; img = base64.b64encode(open(sys.argv[1], "rb").read()); print json.dumps({"image_bytes": {"b64": img}})' test.jpg &> request.json

Answer 1

You can try:
{"instances": [{"image_bytes": {"b64": encoded_string}, "key": "0"}]}