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)

Generator outputs of InfoGAN

Similar to all previous GANs that have been presented to us, we've trained InfoGAN for 40,000 steps. After the training is completed, we're able to run the InfoGAN generator to generate new outputs using the model saved on the infogan_mnist.h5 file. The following validations are conducted:

  1. Generate digits 0 to 9 by varying the discrete labels from 0 to 9. Both continuous codes are set to zero. The results are shown in Figure 6.1.5. We can see that the InfoGAN discrete code can control the digits produced by the generator:

    python3 --generator=infogan_mnist.h5 --digit=0 --code1=0 --code2=0


    python3 --generator=infogan_mnist.h5 --digit=9 --code1=0 --code2=0
  2. Examine the effect of the first continuous code to understand which attribute has been affected. We vary the first continuous code from -2.0 to 2.0 for digits 0 to 9. The second continuous code is set to 0.0. Figure 6.1.6 shows that the first continuous...