TinyML

Chapter 19. Porting Models from TensorFlow to TensorFlow Lite

If you’ve made it this far, you’ll understand that we’re in favor of reusing existing models for new tasks whenever you can. Training an entirely new model from scratch can take a lot of time and experimentation, and even experts often can’t predict the best approach ahead of time without trying a lot of different prototypes. This means that a full guide to creating new architectures is beyond the scope of this book, and we recommend looking in Chapter 21 for further reading on the topic. There are some aspects (like working with a restricted set of operations or preprocessing demands) that are unique to resource-constrained, on-device machine learning, though, so this chapter offers advice on those.

Understand What Ops Are Needed

This book is focused on models created in TensorFlow because the authors work on the team at Google, but even within a single framework there are a lot of different ways of creating models. If you look at the speech commands training script, you’ll see that it’s building a model using core TensorFlow ops directly as building blocks, and manually running a training loop. This is quite an old-fashioned way of working these days (the script was originally written in 2017), and modern examples with TensorFlow 2.0 are likely to use Keras as a high-level API that takes care of a lot of the details.

The downside to this is that the underlying operations that a model uses are no longer obvious from ...