This is a implementation of deep learning Alternating Direction Method of Multipliers(dlADMM) for the task of fully-connected neural network problem, as described in our paper:

Junxiang Wang, Fuxun Yu, Xiang Chen, and Liang Zhao. ADMM for Efficient Deep Learning with Global Convergence. (KDD 2019)

python setup.py install

cupy-cuda90(>=6.0.0 is recommended)

tensorflow

keras

python main.py

Two benchmark datasets MNIST and Fashion-MNIST are included in this package.

Please cite our paper if you use this code in your own work:

@article{wang2019admm,

title={ADMM for Efficient Deep Learning with Global Convergence},

author={Wang, Junxiang and Yu, Fuxun and Chen, Xiang and Zhao, Liang},

journal={arXiv preprint arXiv:1905.13611},

year={2019}

}