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

Using Captum to interpret models

Captum [13.2] is an open source model interpretability library built by Facebook on top of PyTorch, and it is currently (at the time of writing) under active development. In this section, we will use the handwritten digits classification model that we had trained in the preceding section. We will also use some of the model interpretability tools offered by Captum to explain the predictions made by this model. The full code for the following exercise can be found in our github repository [13.3] .

Setting up Captum

The model training code is similar to the code shown under the Training the handwritten digits classifier – a recap section. In the following steps, we will use the trained model and a sample image to understand what happens inside the model while making a prediction for the given image:

  1. There are few extra imports related to Captum that we need to perform in order to use Captum's built-in model interpretability functions:
from...