Functional API
In the sequential model that we first introduced in Chapter 1, Introducing Advanced Deep Learning with Keras, a layer is stacked on top of another layer. Generally, the model will be accessed through its input and output layers. We also learned that there is no simple mechanism if we find ourselves wanting to add an auxiliary input at the middle of the network, or even to extract an auxiliary output before the last layer.
That model also had its downside, for example, it doesn't support graph-like models or models that behave like Python functions. In addition, it's also difficult to share layers between the two models. Such limitations are addressed by the functional API and are the reason why it's a vital tool for anyone wanting to work with deep learning models.
The Functional API is guided by the following two concepts:
A layer is an instance that accepts a tensor as an argument. The output of a layer is another tensor. To build a model, the layer instances are objects that...
