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

Model interpretability in PyTorch

In this section, we will dissect a trained handwritten digits classification model using PyTorch in the form of an exercise. More precisely, we will be looking at the details of the convolutional layers of the trained handwritten digits classification model to understand what visual features the model is learning from the handwritten digit images. We will look at the convolutional filters/kernels along with the feature maps produced by those filters.

Such details will help us to understand how the model is processing input images and, therefore, making predictions. The full code for the exercise can be found in our github repository [13.1] .

Training the handwritten digits classifier – a recap

We will quickly revisit the steps involved in training the handwritten digits classification model, as follows:

  1. First, we import the relevant libraries, and then set the random seeds to be able to reproduce the results of this exercise:
import torch
np...