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 (16 chapters)
Advanced Deep Learning with Keras
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Conditional VAE (CVAE)

Conditional VAE [2] is similar to the idea of CGAN. In the context of the MNIST dataset, if the latent space is randomly sampled, VAE has no control over which digit will be generated. CVAE is able to address this problem by including a condition (a one-hot label) of the digit to produce. The condition is imposed on both the encoder and decoder inputs.

Formally, the core equation of VAE in Equation 8.1.10 is modified to include the condition c:

(Equation 8.2.1)

Similar to VAEs, Equation 8.2.1 means that if we want to maximize the output conditioned on c,

, then the two loss terms must be minimized:

  • Reconstruction loss of the decoder given both the latent vector and the condition.

  • KL loss between the encoder given both the latent vector and the condition and the prior distribution given the condition. Similar to a VAE, we typically choose


Listing 8.2.1, shows us the Keras code of CVAE using CNN layers. In the code that is highlighted showcases...