Captum · Model Interpretability for PyTorch Howto
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Captum is an open-source, extensible model interpretability library for PyTorch that supports multi-modal models and provides state-of-the-art attribution algorithms.
View MoreHow to Use Captum · Model Interpretability for PyTorch
Install Captum: Install Captum using conda (recommended) with 'conda install captum -c pytorch' or using pip with 'pip install captum'
Import required libraries: Import necessary libraries including numpy, torch, torch.nn, and Captum attribution methods like IntegratedGradients
Create and prepare your PyTorch model: Define your PyTorch model class, initialize the model, and set it to evaluation mode with model.eval()
Set random seeds: To make computations deterministic, set random seeds for both PyTorch and numpy
Prepare input and baseline tensors: Define your input tensor and a baseline tensor (usually zeros) with the same shape as your input
Choose and instantiate an attribution algorithm: Select an attribution algorithm from Captum (e.g., IntegratedGradients) and create an instance of it, passing your model as an argument
Apply the attribution method: Call the attribute() method of your chosen algorithm, passing in the input, baseline, and any other required parameters
Analyze the results: Examine the returned attributions to understand which features contributed most to the model's output
Visualize the attributions (optional): Use Captum's visualization utilities to create visual representations of the attributions, especially useful for image inputs
Captum · Model Interpretability for PyTorch FAQs
Captum is an open-source model interpretability and understanding library for PyTorch. It provides state-of-the-art algorithms to help researchers and developers understand which features are contributing to a model's output.
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