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Home > Data > Deep Learning > Generative Adversarial Networks Projects
Generative Adversarial Networks Projects
Generative Adversarial Networks Projects

Generative Adversarial Networks Projects: Build next-generation generative models using TensorFlow and Keras

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Generative Adversarial Networks Projects

3D-GAN - Generating Shapes Using GANs

A 3D-GAN is a GAN architecture for 3D shape generation. 3D shape generation is typically a complex problem, due to the complexities involved in processing 3D images. A 3D-GAN is a solution that can generate realistic and varied 3D shapes and was introduced by Jiajun Wu, Chengkai Zhang, Tianfan Xue, and others in the paper titled Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling. This paper is available at http://3dgan.csail.mit.edu/papers/3dgan_nips.pdf. In this chapter, we will implement a 3D-GAN using the Keras framework.

We will cover the following topics:

  • Introduction to the basics of 3D-GANs
  • Setting up the project
  • Preparing the data
  • A Keras implementation of a 3D-GAN
  • Training the 3D-GAN
  • Hyperparameter optimization
  • Practical applications of 3D-GANs
...

Introduction to 3D-GANs

3D Generative Adversarial Networks (3D-GANs) is a variant of GANs, just like StackGANs, CycleGANs, and Super-Resolution Generative Adversarial Networks (SRGANs). Similar to a vanilla GAN, it has a generator and a discriminator model. Both of the networks use 3D convolutional layers, instead of using 2D convolutions. If provided with enough data, it can learn to generate 3D shapes with good visual quality.

Let's understand 3D convolutions before looking closer at the 3D-GAN network.

3D convolutions

In short, 3D convolution operations apply a 3D filter to the input data along the three directions, which are x, y, and z. This operation creates a stacked list of 3D feature maps. The shape of the output...

Setting up a project

The source code for the project is available on GitHub at the following link: https://github.com/PacktPublishing/Generative-Adversarial-Networks-Projects.

Run the following commands to set up the project:

  1. Start by navigating to the parent directory, as follows:
cd Generative-Adversarial-Networks-Projects
  1. Next, change directory from the current directory to the Chapter02 directory:
cd Chapter02
  1. Next, create a Python virtual environment for this project:
virtualenv venv
  1. After that, activate the virtual environment:
source venv/bin/activate
  1. Finally, install all the requirements that are indicated in the requirements.txt file:
pip install -r requirements.txt

We have now successfully set up the project. For more information, refer to the README.md file included with the code repository.

Preparing the data

In this chapter, we will use the 3D ShapeNets dataset, available at http://3dshapenets.cs.princeton.edu/3DShapeNetsCode.zip. It was released by Wu and Song et al. and consists of properly annotated 3D shapes for 40 object categories. We will use the volumetric data available in the directory, which we will discuss in more detail later on in this chapter. In the next few sections, we will download, extract, and explore the dataset.

The 3D ShapeNets dataset is for academic use only. If you intend to use the dataset for commercial purposes, request permission from the authors of the paper, who can be reached at the following email address: shurans@cs.princeton.edu.

Download and extract the dataset

Run the following...

A Keras implementation of a 3D-GAN

In this section, we will implement the generator network and the discriminator network in the Keras framework. We need to create two Keras models. Both of the networks will have their own separate weights values. Let's start with the generator network.

The generator network

To implement the generator network, we need to create a Keras model and add the neural network layers. The steps required to implement the generator network are as follows:

  1. Start by specifying the values for different hyperparameters:
z_size = 200
gen_filters = [512, 256, 128, 64, 1]
gen_kernel_sizes = [4, 4, 4, 4, 4]
gen_strides = [1, 2, 2, 2, 2]
gen_input_shape = (1, 1, 1, z_size)
gen_activations = ['relu&apos...

Training a 3D-GAN

Training a 3D-GAN is similar to training a vanilla GAN. We first train the discriminator network on both the generated images and the real images but freeze the generator network. Then, we train the generator network but freeze the discriminator network. We repeat this process for a specified number of epochs. During one iteration, we train both of the networks in a sequence. Training a 3D-GAN is an end-to-end training process. Let's work on these steps one by one.

Training the networks

To train the 3D-GAN, perform the following steps:

  1. Start by specifying the values for the different hyperparameters required for the training, shown as follows:
gen_learning_rate = 0.0025
dis_learning_rate = 0.00001
beta...

Hyperparameter optimization

The model that we trained might not be a perfect model, but we can optimize the hyperparameters to improve it. There are many hyperparameters in a 3D-GAN that can be optimized. These include the following:

  • Batch size: Experiment with values of 8, 16, 32, 54, or 128 for the batch size.
  • The number of epochs: Experiment with 100 epochs and gradually increase it to 1,000-5,000.
  • Learning rate: This is the most important hyperparameter. Experiment with 0.1, 0.001, 0.0001, and other small learning rates.
  • Activation functions in different layers of the generator and the discriminator network: Experiment with sigmoid, tanh, ReLU, LeakyReLU, ELU, SeLU, and other activation functions.
  • The optimization algorithm: Experiment with Adam, SGD, Adadelta, RMSProp, and other optimizers available in the Keras framework.
  • Loss functions: Binary cross entropy is the loss...

Practical applications of 3D-GANs

3D-GANs can potentially be used in a wide variety of industries, as follows:

  • Manufacturing: 3D-GANs can be a creative tool to help create prototypes quickly. They can come up with creative ideas and can help in simulating and visualizing 3D models.
  • 3D printing: 3D images generated by 3D-GANs can be used to print objects in 3D printing. The manual process of creating 3D models is very lengthy.
  • Design processes: 3D generated models can provide a good estimate of the eventual outcome of a particular process. They can show us what is going to get built.
  • New samples: Similar to other GANs, 3D-GANs can generate images to train a supervised model.

Summary

In this chapter, we have explored 3D-GANs. We started with an introduction to a 3D-GAN and covered the architecture and the configurations of the generator and the discriminator. Then, we went through the different steps required to set up a project. We also looked at how to prepare the dataset. Finally, we implemented a 3D-GAN in the Keras framework and trained it on our dataset. We also explored different hyperparameter options. We concluded the chapter by exploring the practical applications of 3D-GANs.

In the next chapter, we will learn how to perform face aging using Conditional Generative Adversarial Networks (cGANs).

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Key benefits

  • Use different datasets to build advanced projects in the Generative Adversarial Network domain
  • Implement projects ranging from generating 3D shapes to a face aging application
  • Explore the power of GANs to contribute in open source research and projects

Description

Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.

Who is this book for?

If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for you.

What you will learn

  • Train a network on the 3D ShapeNet dataset to generate realistic shapes
  • Generate anime characters using the Keras implementation of DCGAN
  • Implement an SRGAN network to generate high-resolution images
  • Train Age-cGAN on Wiki-Cropped images to improve face verification
  • Use Conditional GANs for image-to-image translation
  • Understand the generator and discriminator implementations of StackGAN in Keras
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Table of Contents

10 Chapters
Introduction to Generative Adversarial Networks Chevron down icon Chevron up icon
Introduction to Generative Adversarial Networks
What is a GAN?
Practical applications of GANs
The detailed architecture of a GAN
Variants of GANs
Advantages of GANs
Problems with training GANs
Solving stability problems when training GANs
Summary
3D-GAN - Generating Shapes Using GANs Chevron down icon Chevron up icon
3D-GAN - Generating Shapes Using GANs
Introduction to 3D-GANs
Setting up a project
Preparing the data
A Keras implementation of a 3D-GAN
Training a 3D-GAN
Hyperparameter optimization
Practical applications of 3D-GANs
Summary
Face Aging Using Conditional GAN Chevron down icon Chevron up icon
Face Aging Using Conditional GAN
Introducing cGANs for face aging
Setting up the project
Preparing the data
A Keras implementation of an Age-cGAN
Training the cGAN
Practical applications of Age-cGAN
Summary
Generating Anime Characters Using DCGANs Chevron down icon Chevron up icon
Generating Anime Characters Using DCGANs
Introducing to DCGANs
Setting up the project
Downloading and preparing the anime characters dataset
Implementing a DCGAN using Keras
Training the DCGAN
Practical applications of DCGAN
Summary
Using SRGANs to Generate Photo-Realistic Images Chevron down icon Chevron up icon
Using SRGANs to Generate Photo-Realistic Images
Introducing SRGANs
Setting up the project
Downloading the CelebA dataset
The Keras implementation of SRGAN
Training the SRGAN
Practical applications of SRGANs
Summary
StackGAN - Text to Photo-Realistic Image Synthesis Chevron down icon Chevron up icon
StackGAN - Text to Photo-Realistic Image Synthesis
Introduction to StackGAN
Architecture of StackGAN
Setting up the project
Data preparation
A Keras implementation of StackGAN
Training a StackGAN
Practical applications of StackGAN
Summary
CycleGAN - Turn Paintings into Photos Chevron down icon Chevron up icon
CycleGAN - Turn Paintings into Photos
An introduction to CycleGANs
Setting up the project
Downloading the dataset
Keras implementation of CycleGAN
Training the CycleGAN
Practical applications of CycleGANs
Summary
Further reading
Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks Chevron down icon Chevron up icon
Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks
Introducing Pix2pix
Setting up the project
Preparing the data
A Keras implementation of pix2pix
Training the pix2pix network
Practical applications of a pix2pix network
Summary
Predicting the Future of GANs Chevron down icon Chevron up icon
Predicting the Future of GANs
Our predictions about the future of GANs
Potential future applications of GANs
Exploring GANs
Summary
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Customer reviews

Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.3
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 0%
2 star 0%
1 star 66.7%
Micheal Lanahm May 04, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great comprehensive book on some basic GAN techniques.
Amazon Verified review Amazon
mandersm8 Jul 20, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Book doesn't actually explain how the code works in detail or what each line does, it tells you an overall of what the code does. Once coded up, the code didn't work and when issues are opened on the books GitHub repo, the author is unresponsive. Would not recommend this book.
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Matthew Darnell Apr 26, 2019
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Not really useful or informative as a stand-alone body of work. The external dependencies actually make this completely superfluous and superficial.
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About the author

Profile icon Ahirwar
Ahirwar
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Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI), ranging from natural language processing and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches. He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution.
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Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela