philipperemy/keras-attention
Keras Attention Layer (Luong and Bahdanau scores). observed · 2026-08-28
Health v2 · maintenance only
55/100
- Activity 71
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3389
- days_rel: n/a
- days_push: 174
- n_releases_24m: 0
Adoption not part of the score
2809 stars · 648 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Keras layer implementing attention mechanisms with Luong (multiplicative) and Bahdanau (additive) score functions for TensorFlow 2.x. It plugs into sequential models like LSTMs to compute attention-weighted context vectors.
Use cases
- add attention to a keras LSTM model
- implement luong attention in tensorflow
- implement bahdanau attention in keras
- visualize attention weights of a sequence model
- improve sequence classification accuracy with attention
When to choose
- you use TensorFlow 2.x / Keras and want a drop-in attention layer
- you need classic Luong or Bahdanau attention scores
- you want to inspect attention weights for interpretability
When to avoid
- you use PyTorch or JAX instead of TensorFlow/Keras
- you need transformer-style self-attention (use built-in MultiHeadAttention)
- you need the latest Keras 3 multi-backend support
Facets
library · maturity maintenance
machine-learning deep-learning machine-learning deep-learning python keras attention-mechanism tensorflow luong-attention bahdanau-attention neural-networks natural-language-processing
1 source
- readme: https://github.com/philipperemy/keras-attention · fetched 2026-08-28 · 129f5e456eee
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| philipperemy/keras-attention | main | 55 |
For agents
markdown · JSON · MCP: product_card(name="philipperemy/keras-attention")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem