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
