5. Conclusion
In this chapter, we've presented the Functional API as an advanced method for building complex deep neural network models using tf.keras. We also demonstrated how the Functional API could be used to build the multi-input-single-output Y-Network. This network, when compared to a single branch CNN network, achieves better accuracy. For the rest of the book, we'll find the Functional API indispensable in building more complex and advanced models. For example, in the next chapter, the Functional API will enable us to build a modular encoder, decoder, and autoencoder.
We also spent a significant amount of time exploring two important deep networks, ResNet and DenseNet. Both of these networks have been used not only in classification but also in other areas, such as segmentation, detection, tracking, generation, and visual semantic understanding. In Chapter 11, Object Detection, and Chapter 12, Semantic Segmentation, we will use ResNet for object detection and...
