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Mastering PyTorch

You're reading from   Mastering PyTorch Build powerful neural network architectures using advanced PyTorch 1.x features

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781789614381
Length 450 pages
Edition 1st Edition
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Author (1):
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Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
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Table of Contents (20) Chapters Close

Preface 1. Section 1: PyTorch Overview
2. Chapter 1: Overview of Deep Learning using PyTorch FREE CHAPTER 3. Chapter 2: Combining CNNs and LSTMs 4. Section 2: Working with Advanced Neural Network Architectures
5. Chapter 3: Deep CNN Architectures 6. Chapter 4: Deep Recurrent Model Architectures 7. Chapter 5: Hybrid Advanced Models 8. Section 3: Generative Models and Deep Reinforcement Learning
9. Chapter 6: Music and Text Generation with PyTorch 10. Chapter 7: Neural Style Transfer 11. Chapter 8: Deep Convolutional GANs 12. Chapter 9: Deep Reinforcement Learning 13. Section 4: PyTorch in Production Systems
14. Chapter 10: Operationalizing PyTorch Models into Production 15. Chapter 11: Distributed Training 16. Chapter 12: PyTorch and AutoML 17. Chapter 13: PyTorch and Explainable AI 18. Chapter 14: Rapid Prototyping with PyTorch 19. Other Books You May Enjoy

Using Captum to interpret models

Captum (https://captum.ai/) 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 here: https://github.com/PacktPublishing/Mastering-PyTorch/blob/master/Chapter13/captum_interpretability.ipynb.

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...
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