# tensorflow/lucid

A collection of infrastructure and tools for research in neural network interpretability.

Repository: https://github.com/tensorflow/lucid
Canonical: https://ross.abutalabs.com/products/lucid
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: tensorflow, interpretability, visualization, machine-learning, colab, jupyter-notebook
Archived: true
Last push: 2023-02-06T16:41:16+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3142, "days_push": 1304, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4704, forks 642 (observed 2026-08-28T04:08:57.216866+00:00)

## What it is
Lucid is a collection of infrastructure and tools for research in neural network interpretability, built on TensorFlow 1.x. It provides feature visualization techniques (like DeepDream-style optimization) and runnable Jupyter/Colab notebooks for visualizing and understanding neural networks.

## Use cases
- visualize what neurons in a neural network have learned
- run feature visualization experiments like DeepDream
- interpret and explain convolutional neural network activations
- explore neural network interpretability in Colab without setup
- visualize my own TensorFlow model's internal features
- learn feature visualization techniques through tutorial notebooks

## When to choose
- you research neural network interpretability or feature visualization
- you work with TensorFlow 1.x models and want to visualize their internals
- you want ready-to-run Colab notebooks for interpretability experiments

## When to avoid
- you need TensorFlow 2.x support, which Lucid does not currently support
- you need production-grade, well-supported software with technical support
- you need interpretability tools for PyTorch or other frameworks

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, data-visualization, deep-learning
- domain: machine-learning, deep-learning, data-visualization, artificial-intelligence
- platform: python, browser, cross-platform
- tags: feature-visualization, interpretability, tensorflow, deepdream, neural-networks, colab, research-code

## Member repositories
- tensorflow/lucid (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:57.216866+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:19:11.937550+00:00, confidence not recorded.
  - readme: https://github.com/tensorflow/lucid (fetched 2026-08-28T04:08:57.216866+00:00, sha 7aa0cb695f7d)
  - registry_pypi: https://pypi.org/pypi/lucid/json (fetched 2026-08-29T09:03:04.987542+00:00, sha 3ee7368b3354)
- Data as of 2026-08-30T08:39:29.467469+00:00.