I have one CSV with around 10k rows and around 370 columns mostly numerical (int or float) and ID columns which is unique and I know the target column (integer type column) which needs to be used as inference for What-If Tool in Tensorboard. I'm not much experienced in tensorflow, but I could not find the documentation that fit my purposes correctly.
Initially, I built my model using this documentation: https://www.tensorflow.org/tutorials/load_data/pandas_dataframe
To serve the model I went through this documentation: https://www.tensorflow.org/tensorboard/what_if_tool
Where it said in the requirements: The model(s) you wish to explore must be served using TensorFlow Serving using the classify, regress, or predict API.
This leads to this link: https://github.com/tensorflow/serving
I was able to build the saved_model.pb file and use it for serving using docker successfully, but when I use it in Tensorboard What-If Tool I get an error saying "Expected one input Tensor"
And then I went through these links for doing the changes to the model for serving to add input and outputs: https://www.tensorflow.org/tfx/tutorials/serving/rest_simple https://www.tensorflow.org/guide/saved_model
But I still can't understand how or what to give as input and output as I only have a target integer column I know about from my CSV. Neither do I understand how to add signatures properly for all 3 APIs.
I checked the UCI Census Demo model and loaded the model and in signatures, I could see classification, regression, and such and all of them are pruned Concrete Functions which I have no idea about.
My client requires me to load the CSV with model understanding and predict features enabled with both Classification and Regression.