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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15
Index

Generating recipes with deep learning

A final example we will discuss is related to earlier examples in this book, on generating textual descriptions of images using GANs. A more complex version of this same problem is to generate a structured description of an image that has multiple components, such as the recipe for a food depicted in an image. This description is also more complex because it relies on a particular sequence of these components (instructions) in order to be coherent (Figure 13.12):

Figure 13.12: A recipe generated from an image of food17

As Figure 13.13 demonstrates, this "inverse cooking" problem has also been studied using generative models17 (Salvador et al.).

Figure 13.13: Architecture of a generative model for inverse cooking17

Like many of the examples we've seen in prior chapters, an "encoder" network receives an image as input, and then "decodes" using a sequence model into text representations...