how can concstruct tensor when predict online after loading tf saving model












0















As trainning tensorflow models offilne ,i used higher level api,like feature_column and save model using export api like that



feature_spec = tf.feature_column.make_parse_example_spec(columns)

example_input_fn = (

    tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec))

model.export_savedmodel(export_dir, example_input_fn)


and Now i load this model using c++ api 



    tensorflow::LoadSavedModel(sess_options, run_options, modelpath, tags, &bundle)

  and then how can i concact the input tensor,so that can finish predicting.

  std::vector<std::pair<std::string, Tensor> > inputs;

  status = session->Run(inputs, {"pctr"}, {}, &outputs);


Any examples for this?






tensorflow







share|improve this question



























    0















    As trainning tensorflow models offilne ,i used higher level api,like feature_column and save model using export api like that



    feature_spec = tf.feature_column.make_parse_example_spec(columns)
    
example_input_fn = (
    
    tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec))
    
model.export_savedmodel(export_dir, example_input_fn)


    and Now i load this model using c++ api 



        tensorflow::LoadSavedModel(sess_options, run_options, modelpath, tags, &bundle)
    
  and then how can i concact the input tensor,so that can finish predicting.
    
  std::vector<std::pair<std::string, Tensor> > inputs;
    
  status = session->Run(inputs, {"pctr"}, {}, &outputs);


    Any examples for this?






    tensorflow







    share|improve this question

























      0












      0








      0








      As trainning tensorflow models offilne ,i used higher level api,like feature_column and save model using export api like that



      feature_spec = tf.feature_column.make_parse_example_spec(columns)
      
example_input_fn = (
      
    tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec))
      
model.export_savedmodel(export_dir, example_input_fn)


      and Now i load this model using c++ api 



          tensorflow::LoadSavedModel(sess_options, run_options, modelpath, tags, &bundle)
      
  and then how can i concact the input tensor,so that can finish predicting.
      
  std::vector<std::pair<std::string, Tensor> > inputs;
      
  status = session->Run(inputs, {"pctr"}, {}, &outputs);


      Any examples for this?






      tensorflow







      share|improve this question














      As trainning tensorflow models offilne ,i used higher level api,like feature_column and save model using export api like that



      feature_spec = tf.feature_column.make_parse_example_spec(columns)
      
example_input_fn = (
      
    tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec))
      
model.export_savedmodel(export_dir, example_input_fn)


      and Now i load this model using c++ api 



          tensorflow::LoadSavedModel(sess_options, run_options, modelpath, tags, &bundle)
      
  and then how can i concact the input tensor,so that can finish predicting.
      
  std::vector<std::pair<std::string, Tensor> > inputs;
      
  status = session->Run(inputs, {"pctr"}, {}, &outputs);


      Any examples for this?






      tensorflow




      tensorflow






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked 19 hours ago









      刘米兰刘米兰

      7319




      7319
























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