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Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras.

Author: Kailash Ahirwar
Publisher: Birmingham : Packt Publishing Ltd, 2019.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Ahirwar, Kailash
Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras
Birmingham : Packt Publishing Ltd,c2019
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Kailash Ahirwar
ISBN: 9781789134193 1789134196
OCLC Number: 1086098053
Notes: Description based upon print version of record.
Training the DCGAN
Description: 1 online resource (310 p.)
Contents: Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Generative Adversarial Networks; What is a GAN?; What is a generator network?; What is a discriminator network?; Training through adversarial play in GANs; Practical applications of GANs; The detailed architecture of a GAN; The architecture of the generator ; The architecture of the discriminator; Important concepts related to GANs; Kullback-Leibler divergence; Jensen-Shannon divergence; Nash equilibrium; Objective functions; Scoring algorithms; The inception score The Fréchet inception distanceVariants of GANs; Deep convolutional generative adversarial networks; StackGANs; CycleGANs; 3D-GANs; Age-cGANs; pix2pix; Advantages of GANs; Problems with training GANs; Mode collapse; Vanishing gradients; Internal covariate shift; Solving stability problems when training GANs; Feature matching; Mini-batch discrimination; Historical averaging; One-sided label smoothing; Batch normalization; Instance normalization; Summary; Chapter 2: 3D-GAN --
Generating Shapes Using GANs; Introduction to 3D-GANs; 3D convolutions; The architecture of a 3D-GAN The architecture of the generator networkThe architecture of the discriminator network; Objective function; Training 3D-GANs; Setting up a project; Preparing the data; Download and extract the dataset; Exploring the dataset; What is a voxel?; Loading and visualizing a 3D image; Visualizing a 3D image; A Keras implementation of a 3D-GAN; The generator network; The discriminator network; Training a 3D-GAN; Training the networks; Saving the models; Testing the models; Visualizing losses; Visualizing graphs; Hyperparameter optimization; Practical applications of 3D-GANs; Summary Chapter 3: Face Aging Using Conditional GANIntroducing cGANs for face aging; Understanding cGANs; The architecture of the Age-cGAN; The encoder network; The generator network; The discriminator network; Face recognition network; Stages of the Age-cGAN; Conditional GAN training; The training objective function; Initial latent vector approximation; Latent vector optimization; Setting up the project; Preparing the data; Downloading the dataset; Extracting the dataset; A Keras implementation of an Age-cGAN; The encoder network; The generator network; The discriminator network; Training the cGAN Training the cGANInitial latent vector approximation; Latent vector optimization; Visualizing the losses; Visualizing the graphs; Practical applications of Age-cGAN; Summary; Chapter 4: Generating Anime Characters Using DCGANs; Introducing to DCGANs; Architectural details of a DCGAN; Configuring the generator network; Configuring the discriminator network; Setting up the project; Downloading and preparing the anime characters dataset; Downloading the dataset; Exploring the dataset; Cropping and resizing images in the dataset; Implementing a DCGAN using Keras; Generator; Discriminator

Abstract:

In this book, we will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced. It consists of 8 full-fledged projects covering approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, and CycleGAN with real-world use cases.

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