# GANs-in-Action/gans-in-action

Companion repository to GANs in Action: Deep learning with Generative Adversarial Networks

Repository: https://github.com/GANs-in-Action/gans-in-action
Canonical: https://ross.abutalabs.com/products/gans-in-action
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: gan, gans, generative-adversarial-network, artificial-intelligence, ai, deep-learning, deep-neural-networks, artificial-intelligence-algorithms, semi-supervised-learning, dcgan, conditional-gan, cycle-gan
Last push: 2025-07-23T00:48:29+00:00

## Health v2 (maintenance only)
Score: 47/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 33, release rhythm 35, longevity 100
- inputs: {"age_days": 2952, "days_push": 407, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1035, forks 423 (observed 2026-08-28T04:03:18.950136+00:00)

## What it is
The official companion repository for the book 'GANs in Action', containing Jupyter notebook implementations of GAN architectures from vanilla GAN to CycleGAN using Keras and TensorFlow. It lets readers reproduce, study, and extend every hands-on example from the book.

## Use cases
- learn how generative adversarial networks work
- implement a GAN from scratch in Keras
- understand DCGAN and CycleGAN with runnable code
- study adversarial training with worked examples
- find tutorial notebooks for GAN variants
- reproduce book examples on generative models

## When to choose
- you are reading or studying the GANs in Action book
- you want educational, chapter-by-chapter GAN implementations
- you prefer learning through runnable Jupyter notebooks

## When to avoid
- you need a production-ready GAN training framework
- you want up-to-date code for modern TensorFlow versions
- you need state-of-the-art generative models like diffusion models

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: deep-learning, machine-learning, artificial-intelligence, tutorials
- platform: python, cross-platform
- tags: gan, generative-adversarial-networks, jupyter-notebooks, keras, tensorflow, dcgan, cyclegan, book-companion, educational

## Member repositories
- GANs-in-Action/gans-in-action (main) score 47

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.950136+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 2026-08-30T07:04:44.679559+00:00, confidence not recorded.
  - readme: https://github.com/GANs-in-Action/gans-in-action (fetched 2026-08-28T04:03:18.950136+00:00, sha d080433cd26a)
- Data as of 2026-08-30T08:39:29.467469+00:00.