7 Working with Keras: A deep dive
This chapter covers:
- Creating Keras models with keras_model_ sequential(), the Functional API, and model subclassing
- Using built-in Keras training and evaluation loops
- Using Keras callbacks to customize training
- Using TensorBoard to monitor training and evaluation metrics
- Writing training and evaluation loops from scratch
You’ve now got some experience with Keras—you’re familiar with the Sequential model, dense layers, and built-in APIs for training, evaluation, and inference— compile(), fit(), evaluate(), and predict(). You even learned in chapter 3 how to use new_layer_class() to create custom layers, and how to use the TensorFlow GradientTape() to implement a step-by-step training loop.
In the coming chapters, we’ll dig into computer vision, time-series forecasting, natural language processing, and generative deep learning. These complex applications will require much more than a keras_model_sequential() architecture and the default fit() loop. So, let’s first turn you into a Keras expert! In this chapter, you’ll get a complete overview of the key ways to work with Keras APIs: everything you’re going to need to handle the advanced deep learning use cases you’ll encounter next.