problem of using patternnet in Neural network toolbox

I use the pattern recognition network (patternnet) to train a set of data, whose dimension is 7100 and there have 2000 data points. The output is a 2*2000 vector. In other words, there have two classes. I set “hiddenLayerSize” as 50, after running the program, the systems gives the following error message? Is that due to the setup of “hiddenLayerSize” or any other issues?

??? Out of memory. Type HELP MEMORY for your options.

Error in ==> perfs_jejj>calc_Y_trainPerfJeJJ at 89
  JJy = Jwb_y * Jwb_y';

Error in ==> perfs_jejj>singlecalc at 58
  [Y,trainPerfy,trainN,JEy,JJy] = calc_Y_trainPerfJeJJ(net,data,fcns);

Error in ==> perfs_jejj at 9
    [trainPerfy,trainN,valPerfy,~,testPerfy,~,JEy,JJy] = singlecalc(net,data,fcns);

Error in ==> trainlm>train_network at 199
  [perf,vperf,tperf,je,jj,gradient] = nntraining.perfs_jejj(net,data,fcns);

Error in ==> trainlm at 113
  [net,tr] = train_network(net,tr,data,fcns,param);

Error in ==> network.train at 107
[net,tr] = feval(net.trainFcn,net,X,T,Xi,Ai,EW,net.trainParam);

Error in ==> clusteringSOM at 147
[net,tr] = train(net,inputs,targets);

Answer 1

[Greg Heath]

use the pattern recognition network (patternnet) to train a set of data,

whose dimension is 7100 and there have 2000 data points. The output

is a 2*2000 vector. In other words, there have two classes. I set

“hiddenLayerSize” as 50, after running the program, the systems gives

the following error message? Is that due to the setup of

“hiddenLayerSize” or any other issues?

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

For an I-H-O node topology,

size(p) = [ I Ntrn ]            % Input

size(t)  = [ O Ntrn ]          % Output

Neq     = Ntrn*O              % No. of training equations

Nw = (I+1)*H+(H+1)*O    % No. of unknown weights

For accurate and stable weight estimates when training to convergence require that

Neq >> Nw

or

1 <= H << (Neq-O)/(I+O+1) 

1 <= H <<  (2000*2 -2)/(7100+2+1) = 3998/7103 = 0.56

Which is impossible.

Therefore, drastically reduce the input dimension I.

Hope this helps.

Greg