Conclusion

In this chapter, we've presented Functional API as an advanced method for building complex deep neural network models using 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, archives 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 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. We need to remember that it's more important that we understand the model design decisions in ResNet and DenseNet more closely than just following the original implementation. In that manner, we'll be able to use the key concepts of ResNet and DenseNet for our purposes.