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Comgra: A Tool for Analyzing and Debugging Neural Networks

arXiv 2407.21656 ↗First posted 31 Jul 2024cs.LGcited by 0

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Abstract (arXiv)

Neural Networks are notoriously difficult to inspect. We introduce comgra, an open source python library for use with PyTorch. Comgra extracts data about the internal activations of a model and organizes it in a GUI (graphical user interface). It can show both summary statistics and individual data points, compare early and late stages of training, focus on individual samples of interest, and visualize the flow of the gradient through the network. This makes it possible to inspect the model's behavior from many different angles and save time by rapidly testing different hypotheses without having to rerun it. Comgra has applications for debugging, neural architecture design, and mechanistic interpretability. We publish our library through Python Package Index (PyPI) and provide code, documentation, and tutorials at https://github.com/FlorianDietz/comgra.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 31 Jul 2024.

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References · 7

  1. 2023A Comprehensive Overview of Large Language Modelscited by 639
  2. 2022Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasetscited by 599
  3. 2020Captum: A unified and generic model interpretability library for PyTorchcited by 542
  4. 2024Mechanistic Interpretability for AI Safety -- A Reviewcited by 415
  5. 2020A Survey on Neural Network Interpretabilitycited by 291
  6. 2023Inseq: An Interpretability Toolkit for Sequence Generation Modelscited by 78
  7. 2024pyvene: A Library for Understanding and Improving PyTorch Models via Interventionscited by 53

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