Jx-DLT : Deep Learning Toolbox

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"Toward Talent Scientist: Sharing and Learning Together" --- Jingwei Too

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Introduction

• This toolbox contains deep learning algorithm - Convolution neural network ( CNN )
• The Main file shows examples of how to use CNN programs with the benchmark data set

Input

• imgs : feature vector ( height x width x channel x Instances )
• label : label vector ( Instances x 1 )
• opts : parameter settings
  • kfold : number of folds in k-fold cross-validation
  • LR : learning rate
  • nB : number of mini batch
  • MaxEpochs : maximum number of epochs
  • FC : number of fully connect layer ( number of classes )
  • nC : number of convolutional layer ( up to 3 )
  • nF1 : number of filter in 1st convolutional layer
  • sF1 : size of filter in 1st convolutional layer
  • nF2 : number of filter in 2nd convolutional layer
  • sF2 : size of filter in 2nd convolutional layer
  • nF3 : number of filter in 3rd convolutional layer
  • sF3 : size of filter in 3rd convolutional layer

Output

• CNN : Deep learning model ( It contains several results )
  • acc : overall accuracy
  • con : confusion matrix
  • t : computational time (s)

Example 1 : Convolutional Neural Network ( CNN ) with single convolutional layer

% Benchmark dataset
[imgs,label] = digitTrain4DArrayData; 

% Parameter setting
opts.kfold     = 5;
opts.LR        = 0.01; 
opts.nB        = 100; 
opts.MaxEpochs = 20; 
opts.nC        = 1; 
opts.FC        = 10;
opts.nF1       = 16; 
opts.sF1       = [3, 3];
% Convolutional Neural Network
CNN = jCNN(imgs,label,opts);

% Accuracy
accuray = CNN.acc;
% Confusion matrix
confmat = CNN.con;

Example 2 : Convolutional Neural Network ( CNN ) with three convolutional layers

% Benchmark dataset
[imgs,label] = digitTrain4DArrayData; 

% Parameter setting
opts.kfold     = 5;
opts.LR        = 0.01; 
opts.nB        = 100; 
opts.MaxEpochs = 20; 
opts.nC        = 3; 
opts.FC        = 10;
opts.nF1       = 16; 
opts.sF1       = [3, 3];
opts.nF2       = 32; 
opts.sF2       = [3, 3]; 
opts.nF3       = 64; 
opts.sF3       = [3, 3];
% Convolutional Neural Network
CNN = jCNN(imgs,label,opts);

% Accuracy
accuray = CNN.acc;
% Confusion matrix
confmat = CNN.con;

Requirement

• MATLAB 2017 or above
• Statistics and Machine Learning Toolbox
• Neural Network Toolbox

Cite As

@article{too2019featureless,
  title={Featureless EMG pattern recognition based on convolutional neural network},
  author={Too, Jingwei and Abdullah, A and Saad, N Mohd and Ali, N Mohd and Zawawi, TT},
  journal={Indonesian Journal of Electrical Engineering and Computer Science},
  volume={14},
  number={3},
  pages={1291--1297},
  year={2019}
}