Export Model For Serving But Tensor Type Dismatch - tensorflow

Question

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I am trying to export the model for serving , but it's report type error about inputs tensor.
but in the export and predict part , the inputs are the same type.
If possible, provide a minimal reproducible example (We usually don't have time to read hundreds of lines of your code)
here is a sample code for my exporting
```
named_graph_signature = {
'inputs': exporter.generic_signature({
'sparse_index': tf.placeholder(tf.int64, name="feature_index")
'sparse_ids': tf.placeholder(tf.int64,name = "feature_ids"),
'sparse_values':tf.placeholder(tf.int64, name ="feature_values"),
'sparse_shape':tf.placeholder(tf.int64, name="feature_shape")
}),
'outputs': exporter.generic_signature({
'prob': inference_softmax
})}
model_exporter.init(
sess.graph.as_graph_def(),
#default_graph_signature=named_graph_signature,
named_graph_signatures=named_graph_signature,
init_op=init_op)
model_exporter.export(export_path, tf.constant(export_version), sess)
print('Done exporting!')
```
here is my code for predicting
```
ins = "0 142635:1 250810:1 335229:1 375278:1 392970:1 506983:1 554566:1 631968:1 647823:1 658803:1 733446:1 856305:1 868202:1"
FEATURE_SIZE = 1000000
tokens = ins.split(" ")
feature_num = 0
feature_ids = []
feature_values = []
feature_index = []
for feature in tokens[1:]:
feature_id, feature_value = feature.split(":")
feature_ids.append(int(feature_id))
feature_values.append(float(feature_value))
feature_index.append([1, feature_num])
feature_num += 1
feature_shape = [1, FEATURE_SIZE]
sparse_index = tf.contrib.util.make_tensor_proto(numpy.asarray(feature_index), dtype=tf.int64)
sparse_ids = tf.contrib.util.make_tensor_proto(numpy.asarray(feature_ids), dtype=tf.int64)
sparse_values = tf.contrib.util.make_tensor_proto(numpy.asarray(feature_values), dtype=tf.float32)
sparse_shape= tf.contrib.util.make_tensor_proto(numpy.asarray(feature_shape), dtype=tf.int64)
channel = implementations.insecure_channel(host, port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = model_name
request.model_spec.version.value = model_version
print model_name,model_version
request.inputs['sparse_index'].CopyFrom(sparse_index)
request.inputs['sparse_ids'].CopyFrom(sparse_ids)
request.inputs['sparse_values'].CopyFrom(sparse_values)
request.inputs['sparse_shape'].CopyFrom(sparse_shape)
# Send request
result = stub.Predict(request, request_timeout)
```
Logs or other output that would be helpful
(If logs are large, please upload as attachment or provide link).
```
Traceback (most recent call last):
File "run.py", line 63, in <module>
main()
File "run.py", line 59, in main
result = stub.Predict(request, request_timeout)
File "/home/serving/.local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py", line 305, in __call__
self._request_serializer, self._response_deserializer)
File "/home/serving/.local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py", line 203, in _blocking_unary_unary
raise _abortion_error(rpc_error_call)
grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.INVALID_ARGUMENT, details="input size does not match signature")
```