Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Defining the generator and discriminator networks

As mentioned earlier, GANs are composed of two components – the generator and the discriminator. Both of these are essentially neural networks. Generators and discriminators with different neural architectures produce different types of GANs. You can find a list of different types of GANs along with their PyTorch implementations in this reference list [1].

For any GAN that is used to generate some kind of real data, the generator usually takes random noise as input and produces an output with the same dimensions as the real data. We call this generated output fake data. The discriminator, on the other hand, works as a binary classifier. It takes in the generated fake data and the real data (one at a time) as input and predicts whether the input data is real or fake. Figure 9.1 shows a diagram of the overall GAN model schematic:

Figure 8.1 – A GAN schematic

Figure 9.1: A GAN schematic

The discriminator network is optimized like any binary...