MATLAB: How i can to choose a good structure of neural networks( number of nodes in hidden layer)

Deep Learning Toolboxlmer

I’am new to neural networks (for prediction) and I'm not sure how to go about trying to achieve better test error on my dataset. i have 1200*7 inputs matrix ( 1200 lines and 7 columns)and targets (1200*7) for training my neural network. I tested my neural network by differents datasets (not using in trianing). i got bad MSE about 0,10084 with 19 nodes in hidden layer!!!!. Always My program displays the following error message :
Error using .* Matrix dimensions must agree.
Error in neuralNetworktest (line 95) traintargetsTesting = targetsTesting .*tr.trainMask{1};
please advise my about this problem. thanks .
  • * * this is my program : * * *
clear all 
A = load('C:\Users\Omar\Desktop\les données\inputs.txt');
B = load('C:\Users\Omar\Desktop\les données\targets.txt');
C=  load('C:\Users\Omar\Desktop\les données\inputs_Testing.txt');
D= load('C:\Users\Omar\Desktop\les données\targets_Testing.txt');
inputs = A';
targets = B';
inputs_Testing=C';
targets_Testing=D';
% Create a Fitting Network
% Choose Input and Output Pre/Post-Processing Functions
% For a list of all processing functions type: help nnprocess
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};
% Setup Division of Data for Training, Validation, Testing
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'sample';  % Divide up every sample
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% For help on training function 'trainlm' type: help trainlm
% For a list of all training functions type: help nntrain
net.trainF = 'trainlm';  % Levenberg-Marquardt
% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
net.performFcn = 'mse'  % Mean squared error
n=10;% number of iteration 
fileoutmse=fopen('mse.xls','w');% open XLS file  to save MSE and number of nodes  
fprintf(fileoutmse,'%s\t%s\n','numnodes','MSE');
          for NNHL=1:20 % NNHL number of nodes 
        fprintf('number of nodes in hidden layer :%d\n',NNHL);
          net = fitnet(NNHL);
      for j=1:n % n= number of iteration 
                   [net,tr] = train(net,inputs,targets);
                   outputs = net(inputs_Testing);
perf = mse(net,targets_Testing,outputs);
fprintf(fileoutmse,'%d\t %.5f\r\n',NNHL,perf);
end 
   end    
     fclose(fileoutmse);    
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ...
  'plotregression', 'plotfit'};
% Train the Network
% Test the Network
errors = gsubtract(targets_Testing,outputs);
performance = perform(net,targets_Testing,outputs)
% Recalculate Training, Validation and Test Performance
traintargets_Testing = targets_Testing .*tr.trainMask{1};
valtargets_Testing = targets_Testing .*tr.valMask{1};
testtargets_Testing = targets_Testing .*tr.testMask{1};
trainPerformance = perform(net,targets_Testing,outputs)
valPerformance = perform(net,valtargets_Testing,outputs);
testPerformance = perform(net,testtargets_Testing,outputs);
% View the Network
view(net)
fileout=fopen('outputs.xls','w'); % open Excel file to save outputs
fprintf(fileout,'%.2f\r\n',outputs);
fclose(fileout);
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotfit(net,inputs,targets)
%figure, plotregression(targets,outputs)
%figure, ploterrhist(errors)
                                -----------------------------

Best Answer

 [ I N ]  = size(input)    % [7 1200]
 [ O N ]  = size(target)   % [ 7 1200 ]
 Ntrn     = N -2*(0.15*N)  % 840
 Ntrneq   = Ntrn*O         % 5880
 Nw = (I+1)*H+(H+1)*O      % 8*19+20*7=152+140 = 292
Since Ntrneq >> Nw, choose
 MSEgoal << mean(var(target',1))
MSE ~ 0.1 is not bad if the RHS >~ 10
For details see my posts
 greg fitnet MSEgoal
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