So far, most of the discussion has been focused around different models that do classification. These models are trained using object features and their labels to predict labels for hitherto unseen objects. The models also had a fairly simple architecture, all the ones we have seen so far have a linear pipeline modeled by the Keras sequential API.
In this chapter, we will focus on more complex architectures where the pipelines are not necessarily linear. Keras provides the functional API to deal with these sorts of architectures. We will learn how to define our networks using the functional API in this chapter. Note that the functional API can be used to build linear architectures as well.
The simplest extension of classification networks are regression networks. The two broad subcategories under supervised machine learning are classification and regression. Instead of predicting a category, the network now predicts a continuous value. You saw an...