Problems with neural network training

Question

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I´m trying to train a neural network using nprtool and also manually, calling newpr and train methods. I use samples oriented as rows, instead of default as columns. Using nprtool there is no problem, but when I call to the automatically generated M-file, the output is:

??? Error using ==> network.train at 145
Targets are incorrectly sized for network.
Matrix must have 24 columns.
Error in ==> create_pr_net at 29
[net,tr] = train(net,inputs,targets);

My inputs are 140x24, and my targets are 140x3.

The generated code by Matlab is:

function net = create_pr_net(inputs,targets)
%CREATE_PR_NET Creates and trains a pattern recognition neural network.
%
%  NET = CREATE_PR_NET(INPUTS,TARGETS) takes these arguments:
%    INPUTS - RxQ matrix of Q R-element input samples
%    TARGETS - SxQ matrix of Q S-element associated target samples, where
%      each column contains a single 1, with all other elements set to 0.
%  and returns these results:
%    NET - The trained neural network
%
%  For example, to solve the Iris dataset problem with this function:
%
%    load iris_dataset
%    net = create_pr_net(irisInputs,irisTargets);
%    irisOutputs = sim(net,irisInputs);
%
%  To reproduce the results you obtained in NPRTOOL:
%
%    net = create_pr_net(inputs,targets);
% Create Network
numHiddenNeurons = 2000;  % Adjust as desired
net = newpr(inputs,targets,numHiddenNeurons);
net.divideParam.trainRatio = 90/100;  % Adjust as desired
net.divideParam.valRatio = 5/100;  % Adjust as desired
net.divideParam.testRatio = 5/100;  % Adjust as desired
% Train and Apply Network
[net,tr] = train(net,inputs,targets);
outputs = sim(net,inputs);
% Plot
plotperf(tr)
plotconfusion(targets,outputs)

If I transpose inputs and outputs before calling create_pr_net funcion (inputs=inputs';targets=targets';), the results of the traning are not the same as with nprtool (the results present far worse performance).

I am using Matlab R2010a.

Thanks.