bckenstler/CLR
None observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
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: 3452
- days_rel: n/a
- days_push: 2280
- n_releases_24m: 0
Adoption not part of the score
1206 stars · 243 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Keras callback implementing cyclical learning rate (CLR) policies from Leslie Smith's paper, with built-in triangular, triangular2, and exp_range modes plus custom scaling functions. It helps neural network training converge faster by cycling the learning rate between bounds.
Use cases
- implement cyclical learning rate in keras
- speed up neural network convergence
- find optimal learning rate for training
- escape saddle points during training
- experiment with custom lr scheduling policies
- train cifar-10 classifier faster
When to choose
- You train Keras/TensorFlow models and want faster convergence via cyclical learning rates
- You want to experiment with custom LR scaling policies
When to avoid
- You use PyTorch or another framework without Keras
- You need actively maintained tooling - the repo hasn't released since 2020
Facets
library · maturity maintenance
machine-learning deep-learning deep-learning machine-learning python keras learning-rate-scheduling callback hyperparameter-tuning
1 source
- readme: https://github.com/bckenstler/CLR · fetched 2026-08-28 · f27ae8572693
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| bckenstler/CLR | main | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem