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A learning rate recommending and benchmarking tool.

Project description


GitHub license Version


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:

  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 = {},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}



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Copyright (c) 20XX-20XX Georgia Tech DiSL
Licensed under the Apache License.

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