GANs in Action: Deep learning with Generative Adversarial Networks

241 Pages·2019·21.962 MB·English

by  Jakub Langr, Vladimir Bok

Deep learning with Generative Adversarial Networks
Jakub Langr
Vladimir Bok
M A N N I N G
Training Generative Adversarial Networks (GANs)
Repeat for each iteration
1.Train the Discriminator
(a) (c) and (d)
x
Discriminator Classification
error
(b) x*
Generator
z
2.Train the Generator
(b) and (c)
x
Discriminator Classification
error
(a) x*
Generator
z
GANs in Action
DEEP LEARNING WITH
GENERATIVE ADVERSARIAL NETWORKS
JAKUB LANGR
VLADIMIR BOK
MANNING
SHELTER ISLAND
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To those who will consider the jokes more of a pun-ishment than the math.
—Jakub Langr
To Michael Reitano, for helping me become a better writer;
to Simone Reitano, for helping me become a better person.
—Vladimir Bok

brief contents
PART 1 INTRODUCTION TO GANS AND GENERATIVE MODELING...
1
1 ■ Introduction to GANs 3
2 ■ Intro to generative modeling with autoencoders 16
3 ■ Your first GAN: Generating handwritten digits 36
4 ■ Deep Convolutional GAN 51
PART 2 ADVANCED TOPICS IN GANS........................................67
5 ■ Training and common challenges:
GANing for success 69
6 ■ Progressing with GANs 92
7 ■ Semi-Supervised GAN 110
8 ■ Conditional GAN 127
9 ■ CycleGAN 143
PART 3 WHERE TO GO FROM HERE........................................163
10 ■ Adversarial examples 165
11 ■ Practical applications of GANs 181
12 ■ Looking ahead 196
v

contents
preface xiii
acknowledgments xv
about this book xvii
about the cover illustration xxiii
PART 1 INTRODUCTION TO GANS AND
GENERATIVE MODELING .....................................................1
1 Introduction to GANs 3
1.1 What are Generative Adversarial Networks? 5
1.2 How do GANs work? 5
1.3 GANs in action 6
GAN training 8 ■ Reaching equilibrium 11
1.4 Why study GANs? 12
2 Intro to generative modeling with autoencoders 16
2.1 Introduction to generative modeling 17
2.2 How do autoencoders function on a high level? 18
2.3 What are autoencoders to GANs? 19
2.4 What is an autoencoder made of? 20
vii
viii CONTENTS
2.5 Usage of autoencoders 21
2.6 Unsupervised learning 22
New take on an old idea 23 ■ Generation using an
autoencoder 24 ■ Variational autoencoder 25
2.7 Code is life 25
2.8 Why did we try aGAN? 32
3 Your first GAN: Generating handwritten digits 36
3.1 Foundations of GANs: Adversarial training 37
Cost functions 38 ■ Training process 39
3.2 The Generator and the Discriminator 40
Conflicting objectives 41 ■ Confusion matrix 41
3.3 GAN training algorithm 42
3.4 Tutorial: Generating handwritten digits 43
Importing modules and specifying model input dimensions 44
Implementing the Generator 44 ■ Implementing the
Discriminator 45 ■ Building the model 45 ■ Training 46
Outputting sample images 48 ■ Running the model 48
Inspecting the results 49
3.5 Conclusion 50
4 Deep Convolutional GAN 51
4.1 Convolutional neural networks 52
Convolutional filters 52 ■ Parameter sharing 52
ConvNets visualized 52
4.2 Brief history of the DCGAN 54
4.3 Batch normalization 54
Understanding normalization 55 ■ Computing batch
normalization 55
4.4 Tutorial: Generating handwritten digits with DCGAN 56
Importing modules and specifying model input dimensions 57
Implementing the Generator 58 ■ Implementing the
Discriminator 60 ■ Building and running the DCGAN 61
Model output 64
4.5 Conclusion 65