Advanced Deep Learning with Keras

Deep Neural Networks
While this chapter's focus is on deep neural networks; we'll begin this chapter by
discussing an important feature of Keras called the Functional API. This API acts
as an alternative method for building networks in Keras and enables us to build
more complex networks that cannot be accomplished by the sequential model. The
reason why we're focusing so much on this API is that it will become a very useful
tool for building deep networks such as the two we're focusing on in this chapter.
It's recommended that you've completed, Chapter 1, Introducing Advanced Deep
Learning with Keras, before moving onto this chapter as we'll refer to introductory
level code and concepts explored in that chapter as we take them to an advanced
level in this chapter.
The goals of this chapter is to introduce:
• The Functional API in Keras, as well as exploring examples of networks
running it
• Deep Residual Networks (ResNet versions 1 and 2) implementation in Keras
• The implementation of Densely Connected Convolutional Networks
(DenseNet) into Keras
• Explore two popular deep learning models, ResNet, and DenseNet
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.
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