philipperemy/keract
Layers Outputs and Gradients in Keras. Made easy. observed · 2026-08-28
Health v2 · maintenance only
30/100
- Activity 15
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3395
- days_rel: n/a
- days_push: 514
- n_releases_24m: 0
Adoption not part of the score
1059 stars · 187 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Keract is a Python library that extracts layer activations (outputs) and gradients from TensorFlow/Keras models as NumPy arrays, with helpers to display them as images or heatmaps. It supports popular pretrained models and persists results to JSON.
Use cases
- get activations of each layer of a keras model
- visualize cnn layer outputs as heatmaps
- compute gradients of activations in tensorflow
- debug why my neural network layer outputs look wrong
- inspect lstm layer activations
- export keras layer outputs to json
When to avoid
- you are on TensorFlow 2.16+ or Keras 3, which is not supported
- your model relies heavily on nested models
- you need maintained, actively developed tooling
Facets
library · maturity maintenance
machine-learning data-visualization deep-learning deep-learning machine-learning data-visualization python keras tensorflow activations gradients neural-network-introspection heatmaps
1 source
- readme: https://github.com/philipperemy/keract · fetched 2026-08-28 · 7c43c4a098d7
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| philipperemy/keract | main | 30 |
For agents
markdown · JSON · MCP: product_card(name="philipperemy/keract")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem