How to handle multi tensors input? about serving HOT 6 CLOSED

I have solved my problem by pre-define dropout_prob as a tf.constant before export the model.
While I'm still looking forward the multi input and output example. :)

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Unfortunately, neither our standard ClassificationSignature nor RegressionSignature supports multiple input binding for now. You can however, use GenericSignature to specify arbitrary number of inputs and outputs.

You can use exporter.generic_signature() to create it. Then provide it as default_signature in exporter.export().

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Got it. Thanks for explanation.

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@doubler would it be possible for you to share a code example of how you set the dropout_prob using tf.constant? I'm confused about how to fit this into the export step.

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@insectatorious no need to set the dropout_prob, just feed it with 1.0 as input when serving online

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@doubler I don't understand your explanation. What do you mean by "feed it with 1.0 as input"?

Below is the inception signature for a standard image classification:

      classification_signature = (
          tf.saved_model.signature_def_utils.build_signature_def(
              inputs={
                  tf.saved_model.signature_constants.CLASSIFY_INPUTS:
                      classify_inputs_tensor_info
              },
              outputs={
                  tf.saved_model.signature_constants.CLASSIFY_OUTPUT_CLASSES:
                      classes_output_tensor_info,
                  tf.saved_model.signature_constants.CLASSIFY_OUTPUT_SCORES:
                      scores_output_tensor_info
              },
              method_name=tf.saved_model.signature_constants.
              CLASSIFY_METHOD_NAME))

Could you show how is yours with two inputs? Do you have to redefine the tf.saved_mode.signature_constants?

Thanks for help.

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