# Captum — Model Interpretability for PyTorch

> Captum is Meta's open-source library for understanding PyTorch models through gradient-based attribution methods, providing explanations for individual predictions across vision, NLP, and tabular models.

## Install

Save as a script file and run:

# Captum — Model Interpretability for PyTorch

## Quick Use
```bash
pip install captum
```
```python
import torch
from captum.attr import IntegratedGradients
# Assume model is a trained PyTorch classifier
ig = IntegratedGradients(model)
input_tensor = torch.randn(1, 3, 224, 224, requires_grad=True)
attributions = ig.attribute(input_tensor, target=0)
print(attributions.shape)
```

## Introduction
Captum (Latin for "comprehension") is a model interpretability library for PyTorch, developed by Meta. It implements a wide range of gradient and perturbation-based attribution algorithms that explain which input features contribute most to a model's predictions. Captum works with any PyTorch model, including CNNs, RNNs, Transformers, and multi-modal architectures.

## What Captum Does
- Feature attribution: identify which input features drive a prediction
- Layer attribution: understand contributions of intermediate network layers
- Neuron attribution: analyze individual neuron activations
- Robustness analysis through input perturbation metrics
- Visualization tools for image, text, and tabular attributions

## Architecture Overview
Captum organizes attribution methods into three categories: primary attribution (Integrated Gradients, DeepLift, GradientSHAP, Feature Ablation), layer attribution (Layer Conductance, Layer GradCAM), and neuron attribution (Neuron Conductance). Each method implements a common Attribution interface with an attribute() method. The library integrates with Captum Insights, a web-based visualization tool, and provides utilities for convergence testing and sensitivity analysis.

## Self-Hosting & Configuration
- Install via pip: `pip install captum`
- Requires Python 3.6+ and PyTorch
- No GPU required for attribution (runs on the same device as the model)
- Captum Insights web UI: `pip install captum[insights]`
- Works with any PyTorch nn.Module without modification

## Key Features
- Implements 15+ attribution algorithms in a unified API
- Works with any PyTorch model out of the box
- Captum Insights provides interactive web-based visualization
- Supports multi-modal models with separate attributions per input
- Convergence delta metrics for verifying attribution quality

## Comparison with Similar Tools
- **LIME** — model-agnostic perturbation-based; Captum provides gradient-based methods specific to PyTorch
- **SHAP** — Shapley values with multiple backends; Captum integrates GradientSHAP natively for PyTorch
- **tf-explain** — TensorFlow-specific interpretability; Captum is the PyTorch counterpart
- **Alibi** — framework-agnostic with counterfactual explanations; Captum focuses on attribution methods

## FAQ
**Q: Does Captum work with Hugging Face Transformers?**
A: Yes. Any model that subclasses torch.nn.Module works with Captum, including Hugging Face models.

**Q: Which attribution method should I start with?**
A: Integrated Gradients is a good default. It satisfies sensitivity and implementation invariance axioms and works well across model types.

**Q: Can Captum explain NLP models?**
A: Yes. Captum supports token-level attribution for text models, including visualization of token importance scores.

**Q: Does attribution slow down inference?**
A: Attribution requires multiple forward/backward passes (depending on the method), so it is slower than a single inference pass. Use it for analysis, not production serving.

## Sources
- https://github.com/meta-pytorch/captum
- https://captum.ai/

---
Source: https://tokrepo.com/en/workflows/asset-60f2126f
Author: Script Depot