Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Rowel Atienza
Book Image

Advanced Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Rowel Atienza

Overview of this book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Introducing Advanced Deep Learning with Keras
Introducing Advanced Deep Learning with Keras
1. Why is Keras the perfect deep learning library?
2. MLP, CNN, and RNN
3. Multilayer Perceptron (MLP)
4. Convolutional Neural Network (CNN)
5. Recurrent Neural Network (RNN)
6. Conclusion
7. References
Free Chapter
2
Deep Neural Networks
Deep Neural Networks
1. Functional API
2. Deep Residual Network (ResNet)
3. ResNet v2
4. Densely Connected Convolutional Network (DenseNet)
5. Conclusion
6. References
3
Autoencoders
Autoencoders
1. Principles of autoencoders
2. Building an autoencoder using Keras
3. Denoising autoencoders (DAEs)
4. Automatic colorization autoencoder
5. Conclusion
6. References
4
Generative Adversarial Networks (GANs)
Generative Adversarial Networks (GANs)
1. An Overview of GANs
2. Implementing DCGAN in Keras
3. Conditional GAN
4. Conclusion
5. References
5
Improved GANs
Improved GANs
1. Wasserstein GAN
2. Least-squares GAN (LSGAN)
3. Auxiliary Classifier GAN (ACGAN)
4. Conclusion
5. References
6
Disentangled Representation GANs
Disentangled Representation GANs
1. Disentangled representations
2. StackedGAN
4. Conclusion
5. References
7
Cross-Domain GANs
Cross-Domain GANs
1. Principles of CycleGAN
2. Conclusion
3. References
8
Variational Autoencoders (VAEs)
Variational Autoencoders (VAEs)
1. Principles of VAE
2. Conditional VAE (CVAE)
3. 𝛽-VAE – VAE with disentangled latent representations
4. Conclusion
5. References
9
Deep Reinforcement Learning
Deep Reinforcement Learning
1. Principles of Reinforcement Learning (RL)
2. The Q value
3. Q-learning example
4. Nondeterministic environment
5. Temporal-difference learning
6. Deep Q-Network (DQN)
7. Conclusion
8. References
10
Policy Gradient Methods
Policy Gradient Methods
1. Policy gradient theorem
2. Monte Carlo policy gradient (REINFORCE) method
3. REINFORCE with baseline method
4. Actor-Critic method
5. Advantage Actor-Critic (A2C) method
6. Policy Gradient methods using Keras
7. Performance evaluation of policy gradient methods
8. Conclusion
9. References
11
Object Detection
Object Detection
1. Object detection
2. Anchor boxes
3. Ground truth anchor boxes
4. Loss functions
5. SSD model architecture
6. SSD model architecture in Keras
7. SSD objects in Keras
8. SSD model in Keras
9. Data generator model in Keras
10. Example dataset
11. SSD model training
12. Non-Maximum Suppression (NMS) algorithm
13. SSD model validation
14. Conclusion
15. References
12
Semantic Segmentation
Semantic Segmentation
1. Segmentation
2. Semantic segmentation network
3. Semantic segmentation network in Keras
4. Example dataset
5. Semantic segmentation validation
6. Conclusion
7. References
13
Unsupervised Learning Using Mutual Information
Unsupervised Learning Using Mutual Information
1. Mutual Information
2. Mutual Information and Entropy
3. Unsupervised learning by maximizing the Mutual Information of discrete random variables
4. Encoder network for unsupervised clustering
5. Unsupervised clustering implementation in Keras
6. Validation using MNIST
7. Unsupervised learning by maximizing the Mutual Information of continuous random variables
8. Estimating the Mutual Information of a bivariate Gaussian
9. Unsupervised clustering using continuous random variables in Keras
10. Conclusion
11. References
14
Other Books You May Enjoy
Other Books You May Enjoy
15
Index
Index

Improved GANs

Since the introduction of Generative Adversarial Networks (GANs) in 2014[1], their popularity has rapidly increased. GANs have proven to be a useful generative model that can synthesize new data that looks real. Many of the research papers in deep learning that followed proposed measures to address the difficulties and limitations of the original GAN.

As we discussed in previous chapters, GANs can be notoriously difficult to train, and are prone to mode collapse. Mode collapse is a situation where the generator is producing outputs that look the same even though the loss functions are already optimized. In the context of MNIST digits, with mode collapse, the generator may only be producing digits 4 and 9 since they look similar. The Wasserstein GAN (WGAN)[2] addressed these problems by arguing that stable training and mode collapse can be avoided by simply replacing the GAN loss function based on Wasserstein 1, also known as the Earth Mover's Distance...

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