How to save a neural network to test on a new dataset?

Hi,

I am using the following code to train and test NN for 2-class classification. I need to save the trained network to test on a diffreent data set. I tried the save net command, but it just saved the results and not the trained model.

Can nayone please help to get that.?

load iris.mat; % Matlab also provides this dataset (load fisheriris.mat)

% Call features & labels

feat=f; label=l;

% Programmer: Jingwei Too

function NN=jNN(feat,label,kfold,Hiddens,Maxepochs)

% Layer

if length(Hiddens)==1

h1=Hiddens(1); net=patternnet(h1);

elseif length(Hiddens)==2

h1=Hiddens(1); h2=Hiddens(2); net=patternnet([h1 h2]);

elseif length(Hiddens)==3

h1=Hiddens(1); h2=Hiddens(2); h3=Hiddens(3);

net=patternnet([h1 h2 h3]);

end

% rng('default');

% Divide data into k-folds

fold=cvpartition(label,'kfold',kfold,'stratify',true);

% Pre

pred2=[]; ytest2=[]; Afold=zeros(kfold,1);

% Neural network start

for i=1:kfold

% Call index of training & testing sets

trainIdx=fold.training(i); testIdx=fold.test(i);

% Call training & testing features and labels

xtrain=feat(trainIdx,:); ytrain=label(trainIdx);

xtest=feat(testIdx,:); ytest=label(testIdx);

% Set Maximum epochs

net.trainParam.epochs= Maxepochs;

% to prevent early stopping

net.trainParam.max_fail = 500;

net.trainParam.min_grad = 0.000000000000001;

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...

'plotconfusion', 'plotroc'};

% Training model

net=train(net,xtrain',dummyvar(ytrain)');

% Perform testing

pred=net(xtest');

% Confusion matrix

[~,con]=confusion(dummyvar(ytest)',pred);

% Get accuracy for each fold

Afold(i)=100*sum(diag(con))/sum(con(:));

% Store temporary result for each fold

pred2=[pred2(1:end,:),pred]; ytest2=[ytest2(1:end);ytest];

end

% Overall confusion matrix

save net

[~,confmat]=confusion(dummyvar(ytest2)',pred2); confmat=transpose(confmat);

% Average accuracy over k-folds

acc=mean(Afold);

% Store results

NN.fold=Afold; NN.acc=acc; NN.con=confmat;

fprintf('\n Classification Accuracy (NN): %g %%',acc);

% figure, plotperform(tr)

%figure, plottrainstate(tr)

% figure, ploterrhist(e)

% figure, plotconfusion(ytest2,pred)

% figure, plotroc(Labels,y)

end

Answer 1

[Dheeraj Singh]

save will generally save all the variables present in the workspace at point of time.

So, instead of saving the model inside the function, you can return the model

function [net,NN]=jNN(feat,label,kfold,Hiddens,Maxepochs)

and then use the save command

save net

Also, if you trying to use the iris dataset in MATLAB use iris.dat

load iris.dat
feat=iris(:,1:4); label=iris(:,5);

I used the following code and it is working for me:

feat=iris(:,1:4); label=iris(:,5);
% Programmer: Jingwei Too
[net,NN]=jNN(feat,label,5,[10 10 10],10000)
save net

[Joana]

Hi Dheeraj,

Thanks for your answer.

I have one more question regarding data preparation for the Input. I have EEG data for 2-classes, recorded at 1200hz, with 32 EEG channels. i have extracted each class for 1 second for 100 trials. So the data is in the format of number of channels x sampling frequency x trials = 32x1200x200.

I tried the above program by converting the data to 2D as: 38,400x200.

SO input layers neurons are 38,400. and 3 hidden layers with neurons [10 10 10], one output neuron.

Is it the right way to do it.? or should i try something else.? (This gives acceptable results though but i am confused if inout neurons can be this much)

I tried using cell array function but it gives an error that 'Data distribution doesn't have equal number of time steps'.

I'll highly appreciate if you can guide me on this one. :)

[Dheeraj Singh]

This approach looks fine.

But to get bettwe results you may try using Feature Extraction by Means of Spatial Filtering (Common Spatial Patterns) as done in the following blog:

https://in.mathworks.com/company/newsletters/articles/eeg-data-processing-and-classification-with-gbsanalyze-under-matlab.html

You can also refer to These File Exchange Links for EEG Data Analysis:

https://in.mathworks.com/matlabcentral/fileexchange/71322-eeg-signal-analysis?s_tid=FX_rc1_behav

https://in.mathworks.com/matlabcentral/fileexchange/55112-eeg-analysis-and-classification