i am trying to use cross validation in order to determine the optimum number of hidden units for neural network. Am getting an error which i am not able to decipher.

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Neeraj N Sajjan 2016-5-6

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https://ww2.mathworks.cn/matlabcentral/answers/282962-i-am-trying-to-use-cross-validation-in-order-to-determine-the-optimum-number-of-hidden-units-for-neu

评论： Greg Heath 2016-5-8

Error using crossval>evalFun (line 480) The function '@(Xtrain,Ytrain,Xtest)model_finder(i,Xtrain,Ytrain,Xtest)' generated the following error: Invalid types for comparison.

Error in crossval>getLossVal (line 517) funResult = evalFun(funorStr,arg(1:end-1));

Error in crossval (line 416) [funResult,outarg] = getLossVal(i, nData, cvp, data, predfun);

Error in nnrealmain (line 7) mcr=crossval('mcr',x,y,'predfun',hid_find,'partition',c);

This is the main code i typed for cross validation.

load('permanentpcadata.mat');

mcrs=[]; y=[ones(18,1);2*ones(13,1);3*ones(18,1);4*ones(16,1);5*ones(21,1);6*ones(9,1)];

for i=6:20

hid_find=@(Xtrain,Ytrain,Xtest)model_finder(hiiden_units,Xtrain,Ytrain,Xtest); c=cvpartition(y,'k',10); mcr=crossval('mcr',x,y,'predfun',hid_find,'partition',c);

mcrs=[mcrs mcr]; end

save('crossvalop.mat','mcrs');

index=6:20; plot(index,mcrs);

could you suggest where i have gone wrong in the implementation of cross validation?