VMLMF: Vector multiplication on Low-rank Matrix Factorization

This package provide implementations of VMLMF learning a compressed LSTM model.

Overview

• RNN compression based on Low-rank Matrix Factorization
  • Version 1.0
  • Last Updated 10.15.21

Install

Environment

• Ubuntu 16.04(LTS)
• CUDA 10.1
• Python 3.8
• PyTorch 1.7.1

How to use

Code structure

VMLMF
  │ 
  ├── src
  │    │     
  │    ├── models
  │    │     ├── vmlmf_group.py: vmlmf with group structure cell and network code
  │    │     ├── vmlmf_lm.py: vmlmf for language model cell and network code
  │    │     └── vmlmf.py: vmlmf cell and network code
  │    │      
  │    └── train_test
  │    |     ├── main.py: control training and testing 
  │    |     ├── train.py: train the models on the Human Activity Recognition tasks 
  |    |     └── test.py: test the models on the Human Activity Recognition tasks 
  |    |  
  │    └── utils
  |           └── utilities for the package
  │    
  └── scripts: shell scripts for training and testing

Datasets description

*Opportunity dataset [Homepage] *UCI dataset [Homepage] *Pen Tree Bank dataset [Homepage] * Visit the official hompage to check the detail information. * You can download the datasets on the website.

How to use

Download the zip file

cd VMLMF

Install the required packages

install pytorch 1.7.1 proper to your environment  (1.7.1 is required!!)
pip install -r requirements.txt

If other packages are required, use "pip install" to install them.

Download Datasets

sh preprocess.sh

Run the demo

sh ./script/demo.sh

Training & Evaluation

• You can test the models you want:

  bash demo.sh
  

Contact us

• Hyojin Jeon (tarahjjeon@snu.ac.kr)
• U Kang (ukang@snu.ac.kr)
• Data Mining Lab. at Seoul National University.

This software may be used only for research evaluation purposes. For other purposes (e.g., commercial), please contact the authors.