Book Image

Generative AI with Python and TensorFlow 2

By : Joseph Babcock, Raghav Bali
4 (1)
Book Image

Generative AI with Python and TensorFlow 2

4 (1)
By: Joseph Babcock, Raghav Bali

Overview of this book

Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.
Table of Contents (16 chapters)
14
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Index

Painting Pictures with Neural Networks Using VAEs

As you saw in Chapter 4, Teaching Networks to Generate Digits, deep neural networks are a powerful tool for creating generative models for complex data such as images, allowing us to develop a network that can generate images from the MNIST hand-drawn digit database. In that example, the data is relatively simple; images can only come from a limited set of categories (the digits 0 through 9) and are low-resolution grayscale data.

What about more complex data, such as color images drawn from the real world? One example of such "real world" data is the Canadian Institute for Advanced Research 10 class dataset, denoted as CIFAR-10.1 It is a subset of 60,000 examples from a larger set of 80 million images, divided into ten classes – airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. While still an extremely limited set in terms of the diversity of images we would encounter in the real world...