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
