AngularGrad Optimizer
This repository contains the oficial implementation for AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks in PyTorch.
AngularGrad reduces the zig-zag effect in the optimization trajectory. Fluctuations are significantly smoothed, tracing a more direct path towards the minimum of the cost function.
You can import the optimizer as follows:
from myoptims.tanangulargrad import tanangulargrad
from myoptims.cosangulargrad import cosangulargrad
...
model = YourModel()
optimizer = tanangulargrad(model.parameters())
...
for input, output in data:
optimizer.zero_grad()
loss = loss_function(output, model(input))
loss.backward()
optimizer.step()
...
If you have questions or suggestions, please feel free to open an issue. Please cite as:
@article{roy2021angulargrad,
title={AngularGrad: A New Optimization Technique for Angular Convergence of Convolutional Neural Networks},
author={S.K. Roy, M.E. Paoletti, J.M. Haut, S.R. Dubey, P. Kar, A. Plaza and B.B. Chaudhuri},
journal={arXiv preprint arXiv:2105.10190},
year={2021}
}
Experiments
Experiments in the paper:
Analitycal
cd analitycal/
python main.py
CIFAR-10/100
cd cifar/
python main.py --dataset <cifar10/cifar100> --model <r18/r34/.../vgg16/d121> --alg <adam/sgd/.../cosangulargrad/tanangulargrad> --lr <float>
Example:
python main.py --dataset cifar10 --model r50 --alg cosangulargrad --lr 1e-3
Mini-ImageNet:
cd mini-imagenet/
wget URL dataset
python main.py DATADIR --alg <adam/sgd/.../cosangulargrad/tanangulargrad> --lr <float>
Example:
python main.py ./split_mini/ --alg cosangulargrad --model r50 --lr 1e-3
Fine-Grained:
cd fine-grained/
wget URL datasets
python main.py DATADIR --dataset <dsetname> --alg <adam/sgd/.../cosangulargrad/tanangulargrad> --lr <float>
Example:
python main.py ./data/Car196/ --dataset cars --alg adam --lr 1e-3
