Book Image

Deep Learning with PyTorch Lightning

By : Kunal Sawarkar
3.5 (2)
Book Image

Deep Learning with PyTorch Lightning

3.5 (2)
By: Kunal Sawarkar

Overview of this book

Building and implementing deep learning (DL) is becoming a key skill for those who want to be at the forefront of progress.But with so much information and complex study materials out there, getting started with DL can feel quite overwhelming. Written by an AI thought leader, Deep Learning with PyTorch Lightning helps researchers build their first DL models quickly and easily without getting stuck on the complexities. With its help, you’ll be able to maximize productivity for DL projects while ensuring full flexibility – from model formulation to implementation. Throughout this book, you’ll learn how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. You’ll build a neural network architecture, deploy an application from scratch, and see how you can expand it based on your specific needs, beyond what the framework can provide. In the later chapters, you’ll also learn how to implement capabilities to build and train various models like Convolutional Neural Nets (CNN), Natural Language Processing (NLP), Time Series, Self-Supervised Learning, Semi-Supervised Learning, Generative Adversarial Network (GAN) using PyTorch Lightning. By the end of this book, you’ll be able to build and deploy DL models with confidence.
Table of Contents (15 chapters)
1
Section 1: Kickstarting with PyTorch Lightning
6
Section 2: Solving using PyTorch Lightning
11
Section 3: Advanced Topics

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...