Deep Learning with R, Second Edition

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, ...