I don't know how to interpret this ANN classification code.

I constructed following code by utilizing other's code and this code is considerably operated well. I got the 5 confusion matrix after running the code. but I can't interpret this result and code. Do five confusion matrix mean the individual result of K-Fold cross validation? I don't know why 5 confusion matrix were shown. and above all, Do my code is correctly wrotten? I use 6 inputs and 2 outputs for 32 samples.

rng(3); % initialize the RNG to the same state before training to obtain reproducibility

% conversion of table to matrix after loading .csv file.

input = table2array(inputs);

target = table2array(targets);

% hidden layers

hiddenLayerSize = 10;

net = patternnet(hiddenLayerSize);

% Choose the Divide Function

net.divideFcn = 'divideind';

% Choose the Performance Function

net.trainFcn = 'trainscg'

% K-Fold cross validation

k = 5;

cvFolds = crossvalind('Kfold', size(target,2), k);

for i = 1:k

net = configure(net, input, target);

testIdx = (cvFolds == i);

trainIdx = ~testIdx;

trInd = find(trainIdx)

tstInd = find(testIdx)

net.trainParam.epochs = 100;

net.divideParam.trainInd = trInd

net.divideParam.testInd = tstInd

% Performance Function

net.performFcn = 'mse'; % Mean squared error

% Train the network

[net, tr] = train(net,input,target);

% test using test instances

output = net(input);

errors = gsubtract(target, output);

performance = perform(net,target,output)

% Recalculate Training, Validation and Test Performance

trainTargets = target .* tr.trainMask{1};

testTargets = target .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,output)

testPerformance = perform(net,testTargets,output)

test(k)=testPerformance;

% Plot confusion

save net

figure, plotconfusion(target,output)

end

accuracy=mean(test);

% View the Network

view(net)

ples.

Answer 1

[Anshika Chaurasia]

It is my understanding that you are seeing 5 confusion matrices in the output after running the code. The reason for this is the 5-fold cross validation which is being performed. In your code, plotconfusion function is within the for loop with k=5:

for i = 1:k
....
figure, plotconfusion(target,output)
end

This is why the output has 5 confusion matrices. Each confusion matrix corresponds to one of the 5-fold cross validations.

Please refer to the Multilayer Shallow Neural Networks and Backpropagation Training documentation for better understanding of code.