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)

DQN on Keras

To illustrate DQN, the CartPole-v0 environment of the OpenAI Gym is used. CartPole-v0 is a pole balancing problem. The goal is to keep the pole from falling over. The environment is 2D. The action space is made of two discrete actions (left and right movements). However, the state space is continuous and is made of four variables:

  1. Linear position
  2. Linear velocity
  3. Angle of rotation
  4. Angular velocity

The CartPole-v0 is shown in Figure 9.6.1.

Initially, the pole is upright. A reward of +1 is provided for every timestep that the pole remains upright. The episode ends when the pole exceeds 15 degrees from the vertical or 2.4 units from the center. The CartPole-v0 problem is considered solved if the average reward is 195.0 in 100 consecutive trials:

DQN on Keras

Figure 9.6.1: The CartPole-v0 environment

Listing 9.6.1 shows us the DQN implementation for CartPole-v0. The DQNAgent class represents the agent using DQN. Two Q-Networks are created:

  1. Q-Network or Q in Algorithm 9.6.1
  2. Target Q-Network or Qtarget...