Hello Matlab Community. I have run into an issue with my neural network.
My network contains 2000 samples evenly distributed among 4 classes. Each sample contains 100 attributes. The data is best described as particular points in a time sequence (for example, one sample contains 100 data points over 100 seconds--or a 1*100 array). My inputs are a 100*2000 double, and my target is a 4*2000 binary matrix where ones describe the instance of a particular class.
Everything seems to work well, until I get my result.
My confusion matrices have been showing about a 25% accuracy rate.. and it varies every time I retrain. It looks like my neural network is clumping most of my data points into one class in the training phase, which is causing problems down the line. I would also like to note that each time I retrain, a bulk of the training data gets clumped into different classes. Sometimes, it's class 4 (as shown above), but other times it groups everything into class 1,2, or 3.
Here is what my data looks like plotted, each plot represents each of my four classes. I feel like these would be pretty easy to distinguish given their size alone, but that does not seem to be the case.
I have tried a number of things.. changing the training function, number of layers, layer sizes, ect... I'm still resulting in poor classification. Does anyone know what may be causing this? I would also like to note that I am using nnstart to work my way through the network design, then pulling the script from nntraintool. I have it set at the standard 70% train, 15% test, 15% train. Thanks
Edit: My code is below.
x = LargeRadar;
t = LargeRadarTarget;
trainFcn = 'trainscg';
hiddenLayerSize = 15;
net = patternnet(hiddenLayerSize, trainFcn);
net.input.processFcns = {'removeconstantrows','mapminmax'};
net.output.processFcns = {'removeconstantrows','mapminmax'};
net.divideFcn = 'dividerand';
net.divideMode = 'sample';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
net.performFcn = 'crossentropy';
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
'plotconfusion', 'plotroc'};
[net,tr] = train(net,x,t);
y = net(x);
e = gsubtract(t,y);
performance = perform(net,t,y)
tind = vec2ind(t);
yind = vec2ind(y);
percentErrors = sum(tind ~= yind)/numel(tind);
trainTargets = t .* tr.trainMask{1};
valTargets = t .* tr.valMask{1};
testTargets = t .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,y)
valPerformance = perform(net,valTargets,y)
testPerformance = perform(net,testTargets,y)
view(net)
genFunction(net,'myNeuralNetworkFunction');
y = myNeuralNetworkFunction(x);
end
Greg, I have re-uploaded my question with code included. Hope this helps
