Book Image

Advanced Deep Learning with Keras

By : Rowel Atienza
Book Image

Advanced Deep Learning with Keras

By: Rowel Atienza

Overview of this book

Recent developments in deep learning, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Deep Reinforcement Learning (DRL) are creating impressive AI results in our news headlines - such as AlphaGo Zero beating world chess champions, and generative AI that can create art paintings that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep learning techniques available today, so you can create your own cutting-edge AI. Using Keras as an open-source deep learning library, you'll find hands-on projects throughout that show you how to create more effective AI with the latest techniques. The journey begins with an overview of MLPs, CNNs, and RNNs, which are the building blocks for the more advanced techniques in the book. You’ll learn how to implement deep learning models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You then learn all about GANs, and how they can open new levels of AI performance. Next, you’ll get up to speed with how VAEs are implemented, and you’ll see how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans - a major stride forward for modern AI. To complete this set of advanced techniques, you'll learn how to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.
Table of Contents (13 chapters)

The CycleGAN Model

Figure 7.1.3 shows the network model of the CycleGAN. The objective of the CycleGAN is to learn the function:

y' = G(x) (Equation 7.1.1)

That generates fake images, y ', in the target domain as a function of the real source image, x. Learning is unsupervised by capitalizing only on the available real images, x, in the source domain and real images, y, in the target domain.

Unlike regular GANs, CycleGAN imposes the cycle-consistency constraint. The forward cycle-consistency network ensures that the real source data can be reconstructed from the fake target data:

x' = F(G(x)) (Equation 7.1.2)

This is done by minimizing the forward cycle-consistency L1 loss:

The CycleGAN Model

(Equation 7.1.3)

The network is symmetric. The backward cycle-consistency network also attempts to reconstruct the real target data from the fake source data:

y ' = G(F(y)) (Equation 7.1.4)

This is done by minimizing the backward cycle-consistency L1...