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)
12
Index

GAN implementation in Keras


In the previous section, we learned that the principles behind GANs are straightforward. We also learned how GANs could be implemented by familiar network layers such as CNNs and RNNs. What differentiates GANs from other networks is they are notoriously difficult to train. Something as simple as a minor change in the layers can drive the network to training instability.

In this section, we'll examine one of the early successful implementations of GANs using deep CNNs. It is called DCGAN [3].

Figure 4.2.1 shows DCGAN that is used to generate fake MNIST images. DCGAN recommends the following design principles:

  • Use of strides > 1 convolution instead of MaxPooling2D or UpSampling2D. With strides > 1, the CNN learns how to resize the feature maps.

  • Avoid using Dense layers. Use CNN in all layers. The Dense layer is utilized only as the first layer of the generator to accept the z-vector. The output of the Dense layer is resized and becomes the input of the succeeding...