Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras

Key Features

• Understand the common architecture of different types of GANs
• Train, optimize, and deploy GAN applications using TensorFlow and Keras
• Build generative models with real-world data sets, including 2D and 3D data

Book Description

Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand.

This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use.

By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.

What you will learn

• Structure a GAN architecture in pseudocode
• Understand the common architecture for each of the GAN models you will build
• Implement different GAN architectures in TensorFlow and Keras
• Use different datasets to enable neural network functionality in GAN models
• Combine different GAN models and learn how to fine-tune them
• Produce a model that can take 2D images and produce 3D models
• Develop a GAN to do style transfer with Pix2Pix

Who this book is for

This book is for data scientists, machine learning developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and working knowledge of Python programming language will help you get the most out of the book.

Table of Contents

1. What is a Generative Adversarial Network?
2. Data First - How to prepare your dataset
3. My First GAN in under 100 lines
4. Dreaming new Kitchens using DCGAN
5. Pix2Pix Image-to-Image Translation
6. Style Transfering Your image using CycleGAN
7. Use Simulated Images to Create Photo Realistic Eyeballs using simGAN
8. From Image to 3D Models using GANs

簡化下一代深度學習，使用Python、TensorFlow和Keras實現強大的生成模型

主要特點：

- 理解不同類型GAN的常見架構
- 使用TensorFlow和Keras訓練、優化和部署GAN應用
- 使用真實世界數據集構建生成模型，包括2D和3D數據

書籍描述：

開發生成對抗網絡（GAN）是一項複雜的任務，往往很難找到易於理解的代碼。

本書通過八個不同的現代GAN實現示例，包括CycleGAN、simGAN、DCGAN和2D圖像到3D模型生成，引導您在Python、TensorFlow和Keras中構建共同架構的有用配方，以易於閱讀的格式探索越來越困難的GAN架構。本書首先介紹不同類型的GAN架構，以幫助您理解模型的工作原理。本書還包含直觀的配方，幫助您處理涉及DCGAN、Pix2Pix等用例。為了理解這些複雜的應用，您將使用不同的真實世界數據集並將其應用。

通過閱讀本書，您將能夠應對在使用GAN模型時可能遇到的挑戰和問題，因為本書提供了易於遵循的代碼解決方案，您可以立即實施。

學到的內容：

- 使用偽代碼結構化GAN架構
- 理解您將構建的每個GAN模型的常見架構
- 在TensorFlow和Keras中實現不同的GAN架構
- 使用不同的數據集在GAN模型中實現神經網絡功能
- 結合不同的GAN模型，並學習如何微調它們
- 創建一個可以接受2D圖像並生成3D模型的模型
- 使用Pix2Pix開發進行風格轉換的GAN

本書適合對GAN領域的挑戰和任務感興趣的數據科學家、機器學習開發人員和深度學習從業者。熟悉機器學習概念並具備Python編程語言的工作知識將幫助您充分利用本書。

目錄：

1. 什麼是生成對抗網絡？
2. 數據優先-如何準備數據集
3. 在100行以下完成我的第一個GAN
4. 使用DCGAN夢想新廚房
5. Pix2Pix圖像到圖像翻譯
6. 使用CycleGAN進行風格轉換
7. 使用simGAN使用模擬圖像創建逼真的眼球
8. 從圖像到3D模型使用GAN