It is confusing for the new TensorSpace developer to preprocess the pre-trained models: "What is a model preprocessing?", "Why do we need to preprocess the model?" and " How can we make it?". Then this introduction should somehow help you to understand the preprocessing.

A model preprocessing for TensorSpace is the process to detect necessary data (intermediate layers/tensors), extract intermediate outputs from hidden layers and convert to TensorSpace compatible tfjs model format.

Typically, the trained model consumes the input data from the users and then computes among different layers/tensors and finally returns the meaningful outputs which can be used for further evaluations.

TensorSpace is a flexible library: we can construct a model without any existed network or trained weights to show the general structure of the model. It is intuitive to design and explain the prototype of a network before any construction and training.

However, the beauties of TensorSpace as a 3D data visualization model are not only about showing the model structure - how to construct a network, but also about presenting the data interactions among different intermediate layers - how to generate the final outputs step by step.

Hence, we need to find a way to collect the intermediate outputs from not only the last few output layers, but also from the intermediate hidden layers.

To fully apply the core functionality of TensorSpace, we need to transfer the classic model (only returns the final output) into a new model (generates all intermediate outputs we want to present). For the following sections, we introduce how to use TensorFlow-Converter to preprocess and use TensorSpace to visualize the preprocessed models built by TensorFlow, Keras, and TensorFlow.js.