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Deep Learning with PyTorch Lightning

You're reading from   Deep Learning with PyTorch Lightning Swiftly build high-performance Artificial Intelligence (AI) models using Python

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781800561618
Length 366 pages
Edition 1st Edition
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Authors (2):
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Dheeraj Arremsetty Dheeraj Arremsetty
Author Profile Icon Dheeraj Arremsetty
Dheeraj Arremsetty
Kunal Sawarkar Kunal Sawarkar
Author Profile Icon Kunal Sawarkar
Kunal Sawarkar
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Kickstarting with PyTorch Lightning
2. Chapter 1: PyTorch Lightning Adventure FREE CHAPTER 3. Chapter 2: Getting off the Ground with the First Deep Learning Model 4. Chapter 3: Transfer Learning Using Pre-Trained Models 5. Chapter 4: Ready-to-Cook Models from Lightning Flash 6. Section 2: Solving using PyTorch Lightning
7. Chapter 5: Time Series Models 8. Chapter 6: Deep Generative Models 9. Chapter 7: Semi-Supervised Learning 10. Chapter 8: Self-Supervised Learning 11. Section 3: Advanced Topics
12. Chapter 9: Deploying and Scoring Models 13. Chapter 10: Scaling and Managing Training 14. Other Books You May Enjoy

Creating new food items using a GAN

GANs are one of the most common and powerful algorithms used in generative modeling. GANs are used widely to generate fake faces, pictures, anime/cartoon characters, image style translations, semantic image translation, and so on.

We will start by creating an architecture for our GAN model:

Figure 6.3 – GAN architecture for creating a new food

Firstly, we will define the neural networks for the generator and the discriminator with multiple layers of convolution and fully connected layers. In the architecture that we will be building, we will have four convolutional and one fully connected layer for the discriminator, and we will be utilizing five transposed convolution layers for the generator. We will attempt to generate fake images by adding Gaussian noise and use the discriminator to detect these fake images. Then, we will use the Adam optimizer to optimize the neural network. For this use, we will use cross...

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