Brtdku

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)

    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);

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)

    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);

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)

    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);

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)

    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);

Your Answer

StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});


}
});