Chapter 7. Advanced deep-learning best practices
This chapter covers
- The Keras functional API
- Using Keras callbacks
- Working with the TensorBoard visualization tool
- Important best practices for developing state-of-the-art models
This chapter explores a number of powerful tools that will bring you closer to being able to develop state-of-the-art models on difficult problems. Using the Keras functional API, you can build graph-like models, share a layer across different inputs, and use Keras models just like Python functions. Keras callbacks and the TensorBoard browser-based visualization tool let you monitor models during training. We’ll also discuss several other best practices including batch normalization, residual connections, hyperparameter optimization, and model ensembling.
Until now, all neural networks introduced in this book have been implemented using the Sequential model. The Sequential model makes the assumption that the network has exactly one input and exactly one output, and that it consists of a linear stack of layers (see figure 7.1).