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Generative AI with Python and TensorFlow 2

You're reading from  Generative AI with Python and TensorFlow 2

Product type Book
Published in Apr 2021
Publisher Packt
ISBN-13 9781800200883
Pages 488 pages
Edition 1st Edition
Languages
Authors (2):
Joseph Babcock Joseph Babcock
Profile icon Joseph Babcock
Raghav Bali Raghav Bali
Profile icon Raghav Bali
View More author details

Table of Contents (16) Chapters

Preface 1. An Introduction to Generative AI: "Drawing" Data from Models 2. Setting Up a TensorFlow Lab 3. Building Blocks of Deep Neural Networks 4. Teaching Networks to Generate Digits 5. Painting Pictures with Neural Networks Using VAEs 6. Image Generation with GANs 7. Style Transfer with GANs 8. Deepfakes with GANs 9. The Rise of Methods for Text Generation 10. NLP 2.0: Using Transformers to Generate Text 11. Composing Music with Generative Models 12. Play Video Games with Generative AI: GAIL 13. Emerging Applications in Generative AI 14. Other Books You May Enjoy
15. Index

The MNIST database

In developing the DBN model, we will use a dataset that we have discussed before – the MNIST database, which contains digital images of hand-drawn digits from 0 to 91. This database is a combination of two sets of earlier images from the National Institute of Standards and Technology (NIST): Special Database 1 (digits written by US high school students) and Special Database 3 (written by US Census Bureau employees),2 the sum of which is split into 60,000 training images and 10,000 test images.

The original images in the dataset were all black and white, while the modified dataset normalized them to fit into a 20x20-pixel bounding box and removed jagged edges using anti-aliasing, leading to intermediary grayscale values in cleaned images; they are padded for a final resolution of 28x28 pixels.

In the original NIST dataset, all the training images came from Bureau employees, while the test dataset came from high school students, and the modified version...

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