Hands-On Generative Adversarial Networks with Keras电子书

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Foreword

Contributors

About the author

About the reviewer

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Preface

Who this book is for

What this book covers

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Conventions used

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Section 1: Introduction and Environment Setup

Deep Learning Basics and Environment Setup

Deep learning basics

Artificial Neural Networks (ANNs)

The parameter estimation

Backpropagation

Loss functions

L1 loss

L2 loss

Categorical crossentropy loss

Non-linearities

Sigmoid

Tanh

ReLU

A fully connected layer

The convolution layer

The max pooling layer

Deep learning environment setup

Installing Anaconda and Python

Setting up a virtual environment in Anaconda

Installing TensorFlow

Installing Keras

Installing data visualization and machine learning libraries

The matplotlib library

The Jupyter library

The scikit-learn library

NVIDIA's CUDA Toolkit and cuDNN

The deep learning environment test

Summary

Introduction to Generative Models

Discriminative and generative models compared

Comparing discriminative and generative models

Generative models

Autoregressive models

Variational autoencoders

Reversible flows

Generative adversarial networks

GANs – building blocks

The discriminator

The generator

Real and fake data

Random noise

Discriminator and generator loss

GANs – strengths and weaknesses

Summary

Section 2: Training GANs

Implementing Your First GAN

Technical requirements

Imports

Implementing a Generator and Discriminator

Generator

Discriminator

Auxiliary functions

Training your GAN

Summary

Further reading

Evaluating Your First GAN

The evaluation of GANs

Image quality

Image variety

Domain specifications

Qualitative methods

k-nearest neighbors

Mode analysis

Other methods

Quantitative methods

The Inception score

The Frechét Inception Distance

Precision, Recall, and the F1 Score

GANs and the birthday paradox

Summary

Improving Your First GAN

Technical requirements

Challenges in training GANs

Mode collapse and mode drop

Training instability

Sensitivity to hyperparameter initialization

Vanishing gradients

Tricks of the trade

Tracking failure

Working with labels

Working with discrete inputs

Adding noise

Input normalization

Modified objective function

Distribute latent vector

Weight normalization

Avoid sparse gradients

Use a different optimizer

Learning rate schedule

GAN model architectures

ResNet GAN

GAN algorithms and loss functions

Least Squares GAN

Wasserstein GAN

Wasserstein GAN with gradient penalty

Relativistic GAN

Summary

Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio

Progressive Growing of GANs

Technical requirements

Progressive Growing of GANs

Increasing variation using minibatch standard deviation

Normalization in the generator and the discriminator

Pixelwise feature vector normalization in the generator

Experimental setup

Training

Helper functions

Initializations

Training loops

Model implementation

Custom layers

The discriminator

The generator

GANs

Summary

Generation of Discrete Sequences Using GANs

Technical requirements

Natural language generation with GANs

Experimental setup

Data

Auxiliary training functions

Training

Imports and global variables

Initializations

Training loop

Logging

Model implementation

Helper functions

Discriminator

Generator

Inference

Model trained on words

Model trained on characters

Summary

Text-to-Image Synthesis with GANs

Technical Requirements

Text-to-image synthesis

Experimental setup

Data utils

Logging utils

Training

Initial setup

The training loop

Model implementation

Wrapper

Discriminator

Generator

Improving the baseline model

Training

Inference

Sampling the generator

Interpolation in the Latent Space

Interpolation in the text-embedding space

Inferencing with arithmetic in the text-embedding space

Summary

TequilaGAN - Identifying GAN Samples

Technical requirements

Identifying GAN samples

Related work

Feature extraction

Centroid

Slope

Metrics

Jensen-Shannon divergence

Kolgomorov-Smirnov Two-Sample test

Experiments

MNIST

Summary

References

Whats next in GANs

What we've GANed so far

Generative models

Architectures

Loss functions

Tricks of the trade

Implementations

Unanswered questions in GANs

Are some losses better than others?

Do GANs do distribution learning?

All about that inductive bias

How can you kill a GAN?

Artistic GANs

Visual arts

GANGogh

Image inpainting

Vid2Vid

GauGAN

Sonic arts

MuseGAN

GANSynth

Recent and yet-to-be-explored GAN topics

Summary

Closing remarks

Further reading