This repository contains our pytorch implementation of Partially Adaptive Momentum Estimation method (Padam) in the paper Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks (accepted by IJCAI 2020).

• Pytorch
• CUDA

Use python to run run_cnn_test_cifar10.py for experiments on Cifar10 and run_cnn_test_cifar100.py for experiments on Cifar100

• --lr: (start) learning rate
• --method: optimization method, e.g., "sgdm", "adam", "amsgrad", "padam"
• --net: network architecture, e.g. "vggnet", "resnet", "wideresnet"
• --partial: partially adaptive parameter for Padam method
• --wd: weight decay
• --Nepoch: number of training epochs
• --resume: whether resume from previous training process

• Run experiments on Cifar10:

  -  python run_cnn_test_cifar10.py  --lr 0.1 --method "padam" --net "vggnet"  --partial 0.125 --wd 5e-4

• Run experiments on Cifar100:

  -  python run_cnn_test_cifar100.py  --lr 0.1 --method "padam" --net "resnet"  --partial 0.125 --wd 5e-4

Please check our paper for technical details and full results.

@inproceedings{chen2020closing,
  title={Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks},
  author={Chen, Jinghui and Zhou, Dongruo and Tang, Yiqi and Yang, Ziyan and Cao, Yuan and Gu, Quanquan},
  booktitle={International Joint Conferences on Artificial Intelligence},
  year={2020}
}