neural network tool box

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I have used training data set (P) is of size 280*5 and target (T) as 5*5 of identity matrix. Test data (Ptest) as 280*20 and I can able to construct the confusion matrix using statement

figure,plotconfusion(T,y);

My question is if training data set P became 280*35 and target T as 35*35, the confusion matrix is not clear after testing because of 35 symbols.

Answer 1

[Greg Heath]

Your data is woefully inadequate. Let

 [ I N ] = size(input)
 [ O N ] = size(target)
 Ntrn    = N-2*round(0.15*N) % default training set size
 Ntrneq  = N*O               % default number of training equations

For an adequate definition of the input and output spaces, it is desirable that

 Ntrn >> 1+max(I,O)

For a net with I-H-O node topology, the number of unknown weights is

 Nw = (I+1)*H+(H+1)*O

For an adequate estimate of weights, it is desirable that

 Ntrneq >> Nw

 You need a drastic reduction in the input dimension I and/or a corresponding increase in training examples.

 When Ntrn is not large and/or the number of classification categories is large,pay more attention to the function confusion than the plot function plotconfusion.