Adding Interpretability to PyTorch Models with Captum

Captum, Open Source Library for Pytorch Model Explainability

Introduction:

In the world of machine learning and deep learning, model interpretability has become increasingly important. Understanding why a model makes certain predictions or decisions can provide valuable insights, improve trust, and enable debugging and refinement. One powerful tool for model interpretability in PyTorch is Captum.

In this article, we will explore Captum, a PyTorch library designed for model interpretability. We will discuss its key features, demonstrate how to use it with PyTorch models, and showcase some popular interpretability techniques provided by Captum.

Table of Contents:

1. What is Captum?
2. Key Features of Captum
3. Getting Started with Captum

• Installation
• Importing Captum and PyTorch

4. Explaining Model Predictions with Captum

• Integrated Gradients
• Layer Attribution
• DeepLift

5. Visualizing Captum Results

• Heatmap Visualization
• Bar Plot Visualization

6. Simple Example: Interpreting an RNN Regression Model