A learning rate recommending and benchmarking tool.
Project description
LRBench
Introduction
A learning rate benchmarking and recommending tool, which will help practitioners efficiently select and compose good learning rate policies.
- Semi-automatic Learning Rate Tuning
- Evaluation: A set of Useful Metrics, covering Utility, Cost, and Robustness.
- Verification: Near-optimal Learning Rate
If you find this tool useful, please cite the following paper:
@ARTICLE{lrbench2019,
author = {{Wu}, Yanzhao and {Liu}, Ling and {Bae}, Juhyun and {Chow}, Ka-Ho and
{Iyengar}, Arun and {Pu}, Calton and {Wei}, Wenqi and {Yu}, Lei and
{Zhang}, Qi},
title = "{Demystifying Learning Rate Polices for High Accuracy Training of Deep Neural Networks}",
journal = {arXiv e-prints},
keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
year = "2019",
month = "Aug",
eid = {arXiv:1908.06477},
pages = {arXiv:1908.06477},
archivePrefix = {arXiv},
eprint = {1908.06477},
primaryClass = {cs.LG},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190806477W},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Problem
Installation
Supported Platforms
Development / Contributing
Issues
Status
Contributors
See the people page for the full listing of contributors.
License
Copyright (c) 20XX-20XX Georgia Tech DiSL
Licensed under the Apache License.
Project details
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