Skip to content

Extreme Learning Machine implemented in Pytorch

Notifications You must be signed in to change notification settings

randomblbl/ELM-pytorch

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

14 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

ELM-pytorch

Extreme Learning Machine (ELM) implemented in Pytorch.

It's MNIST tutorial with basic ELM algorithm, Online Sequential ELM (OS-ELM), and Convolutional ELM.

You can run the code using cpu or gpu mode.

Requirements

  • Python 3.5+
  • Pytorch 0.3.1+

Extreme Learning Machine

Usage:

cd mnist

GPU mode: python main_ELM.py

CPU mode: python main_ELM.py --no-cuda

The training was completed in 2.0sec and the accuracy reached 97.77%. (Geforce GTX1080Ti 11GB, #hidden neurons=7000)

In CPU mode, the training was completed in 26.92sec and the accuracy was the same. (intel Core i7-6700K CPU 4.00GHz x 8 64GB RAM, #hidden neurons=7000)

If you do not have enough memory for the training process, reduce the number of hidden neurons and try again.

Online Sequential Extreme Learning Machine

Usage:

cd mnist

GPU mode: python main_ELM.py

CPU mode: python main_ELM.py --no-cuda

The training was completed in 10.0sec and the accuracy reached 97.77%. (Geforce GTX1080Ti 11GB, #hidden neurons=7000, batch_size=1000)

In CPU mode, the training was completed in 100.92sec and the accuracy was the same. (intel Core i7-6700K CPU 4.00GHz x 8 64GB RAM, #hidden neurons=7000, batch_size=1000)

If you do not have enough memory for the training process, reduce the number of hidden neurons and try again.

Convolutional Extreme Learning Machine

Usage:

cd mnist

GPU mode: python main_CNNELM.py

CPU mode: python main_CNNELM.py --no-cuda

The training was completed in 7.2sec and the accuracy reached 98.01%. (Geforce GTX1080Ti 11GB, the code used almost all RAM.)

Network configuration

ConvLayer1: kernel_size=5, #channel=10, padding=1 PoolLayer1: kernel_size=2 ReluLayer1: ConvLayer2: kernel_size=4, #channel=80, padding=1 PoolLayer2: kernel_size=2 ReluLayer2: FCLayer:

In CPU mode, the training was completed in 177.92sec and the accuracy was 98.80%. (intel Core i7-6700K CPU 4.00GHz x 8 64GB RAM, the code used almost all RAM.)

Network configuration

ConvLayer1: kernel_size=5, #channel=10, padding=1 PoolLayer1: kernel_size=2 ReluLayer1: ConvLayer2: kernel_size=4, #channel=450, padding=1 PoolLayer2: kernel_size=2 ReluLayer2: FCLayer:

If you do not have enough memory for the training process, reduce the number of hidden neurons and try again.

About

Extreme Learning Machine implemented in Pytorch

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%