Skip to content
dlADMM: Deep Learning Optimization via Alternating Direction Method of Multipliers
Python
Branch: master
Clone or download
Latest commit cfbc4f2 Oct 28, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
dlADMM code optimization Sep 1, 2019
README.md Update README.md Oct 28, 2019
main.py code optimization Sep 1, 2019
setup.py Add files via upload Jun 30, 2019

README.md

dlADMM: Deep Learning Optimization via Alternating Direction Method of Multipliers

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)

Installation

python setup.py install

Requirements

cupy-cuda90(>=6.0.0 is recommended)

tensorflow

keras

Run the Demo

python main.py

Data

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

Cite

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

@inproceedings{Wang:2019:AED:3292500.3330936,

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

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

booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},

series = {KDD '19},

year = {2019},

isbn = {978-1-4503-6201-6},

location = {Anchorage, AK, USA},

pages = {111--119},

numpages = {9},

url = {http://doi.acm.org/10.1145/3292500.3330936},

doi = {10.1145/3292500.3330936},

acmid = {3330936},

publisher = {ACM},

address = {New York, NY, USA},

keywords = {alternating direction method of multipliers, deep learning, global convergence},

}

You can’t perform that action at this time.