How to fix error "Model parameter must be a string" in Run a Neural network sample Code

Question

seyed hosein alhoseiny 2017-9-1

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/354897-how-to-fix-error-model-parameter-must-be-a-string-in-run-a-neural-network-sample-code

评论： Rafik 2020-2-22

hello in this sample code this error " Model parameter must be a string" occurs when run section for plot regression graphs for 3 data type (train, validation,test)

codes :

data=xlsread('sarvak_normalized.xlsx');  % Input File
corrplot(data)
input=[1:9]; % Input Layer
p=data(:,input);
output=[10:11];                        % Output Layer
t=data(:,output);
p=p'; t=t';                         % Transposing Matrices
% t=log(t);
% Defining Validation Dataset
trainRatio1=.6;
valRatio1=.2;
testRatio1=.2;
%%Network Definition
nnn1=5;                             % First Number of Neurons in Hidden Layer
nnnj=5;                             % Jump in Number of Neurons in Hidden Layer
nnnf=100;                           % Last Number of Neurons in Hidden Layer
% net1.trainparam.lr=0.1;
% net1.trainParam.epochs=500;
% Training Network
it=20;                                             % Max Number of Iteration
ii=0;
netopt{:}=1:nnnf;
for nnn=nnn1:nnnj:nnnf
    ii=ii+1; nnn
    net1=newff(p,t,[nnn nnn],{'purelin','purelin'},'traingd'); % For more functions see: 'Function Reference' in 'Neural Network Toolbox' of Matlab help
      evalopt(ii)=1000;
      for i=1:it
          [net1,tr,y,et]=train(net1,p,t);             % Training
          net1.divideParam.trainRatio=trainRatio1;
          net1.divideParam.valRatio=valRatio1;
          net1.divideParam.testRatio=testRatio1;
          estval=sim(net1,p(:,tr.valInd)); 
          eval=mse(estval-t(:,tr.valInd));
          if  eval<evalopt(ii)
              netopt{(ii)}=net1; tropt(ii)=tr; evalopt(ii)=eval;
          end
      end
  end
plot(nnn1:nnnj:nnnf,evalopt)
%%Output
%clear
%load('run1');
nn=17
ptrain=p(:,tropt(nn).trainInd);     ttrain=t(:,tropt(nn).trainInd);     esttrain=sim(netopt{nn},ptrain);
ptest=p(:,tropt(nn).testInd);       ttest=t(:,tropt(nn).testInd);       esttest=sim(netopt{nn},ptest);
pval=p(:,tropt(nn).valInd);         tval=t(:,tropt(nn).valInd);         estval=sim(netopt{nn},pval);
estwhole=sim(netopt{nn},p);
% ttrain=exp(ttrain); ttest=exp(ttest); tval=exp(tval); t=exp(t);
% esttrain=exp(esttrain); esttest=exp(esttest); estval=exp(estval); estwhole=exp(estwhole);
plotregression(ttrain,esttrain,'Train',tval,estval,'Validation',...
    ttest,esttest,'Test',t,estwhole,'Whole Data');