Skip to main content

A learning rate recommending and benchmarking tool.

Project description

LRBench


GitHub license Version

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

LRBench-0.0.0.1.tar.gz (5.9 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page