IssamLaradji / ada_sls

Ada-SLS

Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search) [Paper]

Our AMSGrad Armijo SLS and AdaGrad Armijo SLS consistently achieve best generalization results.

Installation

pip install git+https://github.com/IssamLaradji/ada_sls.git

Usage

Use AdaSls in your code by adding the following script.

import adasls
opt = adasls.AdaSLS(model.parameters())

for epoch in range(100):
    opt.zero_grad()
    closure = lambda : torch.nn.MSELoss()(model(X), Y)
    opt.step(closure=closure)

Experiments

Install the requirements

pip install -r requirements.txt

Run the experiments for the paper using the command below:

python trainval.py -e ${GROUP}_{BENCHMARK} -sb ${SAVEDIR_BASE} -d ${DATADIR} -r 1

with the placeholders defined as follows.

{GROUP}:

Defines the set of optimizers to run, which can either be,

• nomom for opimizers without momentum; or
• mom for opimizers with momentum.

{BENCHMARK}:

Defines the dataset, evaluation metric and model for the experiments (see exp_configs.py), which can be,

• syn for the synthetic experiments;
• kernels for the kernel experiments;
• mf for matrix factorization experiments;
• mnist for the mnist experiments;
• cifar10, cifar100, cifar10_nobn, cifar100_nobn for the cifar experiments; or
• imagenet200, imagenet10 for the imagenet experiments.

{SAVEDIR_BASE}:

Defines the absolute path to where the results will be saved.

{DATADIR}:

Defines the absolute path containing the downloaded datasets.

Cite

@article{vaswani2020adaptive,
  title={Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search)},
  author={Vaswani, Sharan and Kunstner, Frederik and Laradji, Issam and Meng, Si Yi and Schmidt, Mark and Lacoste-Julien, Simon},
  journal={arXiv preprint arXiv:2006.06835},
  year={2020}
}