/Hypergradient-Descent

MLOP class project.

Primary LanguageJupyter Notebook

Hypergradient-Descent

Course Project: CSCI-6961/4961 Machine Learning & Optimization (Fall-2021)
Group: Mohbat Tharani, Momin Abbas, Huzaifa Arif, Tobias Park, Xinyan Sun

Language: Python API: Pytorch

Instructions

The pytorch/data folder contains the datasets used for training (MNIST,CIFAR-10,CIFAR-100). Results of our experiments are located in the pytorch/results folder. To reproduce all of the experiments, simply download all the files in the pytorch folder and run pytorch/main.py as python main.py.

Pytorch optimizers for all of the Hypergradient Descent algorithms are located in pytorch/optimizers.py if you want to utilize these algorithms for your own purposes or to run your own experiments. They are normal pytorch optimizers that can simply be plugged in and used in the same way as any normal pytorch optimizer. pytorch/optimizer.py includes:

  • SGD and SGD-HD
  • SGDN and SGDN-HD
  • Adam and Adam-HD

To select which models, dataset or optimizers you want to use to regenerate the results, just add that dataset in the respective files. pytorch/model.py contains model classes, pytorch/util.py has codes for dataloaders for datasets including MNIST, CIFAR10 and CIFAR100. However, you can use your dataloader for your dataset(s).

pytroch/beta.py has the code that generates convergence trends for different beta values.

Reference

@article{baydin2017online,
  title={Online learning rate adaptation with hypergradient descent},
  author={Baydin, Atilim Gunes and Cornish, Robert and Rubio, David Martinez and Schmidt, Mark and Wood, Frank},
  journal={arXiv preprint arXiv:1703.04782},
  year={2017}
}