Summary

In this chapter, we learned how to save and load model parameters in different ML frameworks. We saw that all the frameworks we used in the Shogun, Shark-ML, Dlib, and PyTorch libraries have an API for model parameter serialization. Usually, these are quite simple functions that work with model objects and some input and output streams. Also, we discussed another type of serialization API that can be used to save and load the overall model architecture. At the time of writing, the frameworks we used don't fully support such functionality. The Shogun toolkit can load neural network architectures from the JSON descriptions, but can't export them. The Dlib library can export neural networks in XML format but can't load them. The PyTorch C++ API lacks a model architecture that supports exporting, but it can load and evaluate model architectures that have been...