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How to calculate accuracy for neural network algorithms?

Asked by sandhya sandhya on 14 Mar 2019
Latest activity Commented on by sandhya sandhya on 25 Mar 2019
How to calculate accuracy for neural network algorithms?

  1 Comment

I'm pretty sure this is a topic with literally thousands of hits if you google it! Or are you asking specifically about a Matlab coded network, in which case showing some code helps.

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1 Answer

Answer by Greg Heath
on 15 Mar 2019
 Accepted Answer

I normalize the mean-square-error
MSE = mse(error) = mse(output-target)
by the minimum MSE obtained when the output is a constant.
If the output is a constant, the MSE is minimized when that constant is
the average of the target. For a 1-D target
NMSE = mse(output-target) / mse(target-mean(target))
= mse(error) / var(target,1)
This is related to the R-square statistic (AKA as R2) via
Rsquare = R2 = 1 - NMSE
Both NMSE and R2 are contained in [0,1].
I have posted zillions of examples in both the NEWSGROUP and ANSWERS.
Just search using
Greg NMSE
Thank you for formally accepting my answer
Greg

  4 Comments

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Hi sir,i tried below code and i want to calculate the accuracy for this code.please help me if possible
input=load('project1.mat'); %420 x 24
target=load('braineeg.mat'); %1 x 420
hiddenLayerSize = 10;
net = feedforwardnet(hiddenLayerSize );
net.divideFcn = 'divideind';
net.divideParam.trainInd = 1:1792;
net.divideParam.valInd = 1793:2176;
net.divideParam.testInd = 2177:2560;
% [net,tr] = train(net,input,target);
[net,tr] = train(net,input.val,target.val);
view(net)
output = net(input.val);
errors = gsubtract(target.val,output);
performance = perform(net,target.val,output);
figure, plotperform(tr)
figure, plottrainstate(tr)
figure, plotconfusion(target.val,output)
[c,cm] = confusion(target.val,output);
fprintf('Percentage Correct Classification : %f%%\n', 100*(1-c));
fprintf('Percentage Incorrect Classification : %f%%\n', 100*c);
figure, ploterrhist(errors)
trainTargets = target.val .* tr.trainMask{1};
valTargets = target.val .* tr.valMask{1};
testTargets = target.val .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,output);
valPerformance = perform(net,valTargets,output);
testPerformance = perform(net,testTargets,output);
MSE = mse(error) = mse(output-target)
Your numbers make no sense
  1. A 1x420 target requires the input to be transposed
  2. Where does 2560 come from???
  3. Your use of *.val makes no sense
Input signal with dimension 1*420 was my previous signal ,I forgot to modify the dimension.Now,the input signal with dimension 23*2560 and *.val is the inbuilt file of input signal.If you give command as plot(input),it displays the error.But ,if you give command as
plot(input.val), then it displays the output.Can you please add accuracy commands to my code.
input=load('project1.mat'); 23*2560
target=load('braineeg.mat');
hiddenLayerSize = 10;
net = feedforwardnet(hiddenLayerSize );
net.divideFcn = 'divideind';
net.divideParam.trainInd = 1:1792;
net.divideParam.valInd = 1793:2176;
net.divideParam.testInd = 2177:2560;
net = configure(net,input.val,target.val);
[net,tr] = train(net,input.val,target.val);
view(net)
output = net(input.val);
errors = gsubtract(target.val,output);
performance = perform(net,target.val,output);
view(net)
figure, plotperform(tr)
figure, plottrainstate(tr)
figure, plotconfusion(target.val,output)
[c,cm] = confusion(target.val,output);
figure, ploterrhist(errors)
trainTargets = target.val .* tr.trainMask{1};
valTargets = target.val .* tr.valMask{1};
testTargets = target.val .* tr.testMask{1};
trainPerformance = perform(net,trainTargets,output);
valPerformance = perform(net,valTargets,output);
testPerformance = perform(net,testTargets,output);
YPred = predict(net,input.val);
MSE=mse(output-target.val);
NMSE = MSE / mse(target-mean(target.val));

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