Problematic Ordinal Input Categorical Output Neural Network

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Andreas
Andreas 2016-2-22
编辑： Andreas 2016-2-23
Hello everyone.! I'm fairly new to the ANN, but I am making quite an effort on that. I have a problem with ordinal input (numbers from 1-5 in each column, arrays are the observations (172) ) and categorical output (A-->10000 , B-->010000, C-->00100 etc) I am using patternet with 100 hidden neurons and
net.layers{1}.transferFcn = 'tansig';
net.layers{2}.transferFcn = 'logsig';
although I have given a shot to softmax and other transfer functions. Also I use
net.divideFcn = 'dividerand';
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
net.trainFcn = 'trainscg';
net.trainParam.max_fail= 1000;
net.inputs{1}.processFcns = {'removeconstantrows','fixunknowns','mapminmax'};
net.outputs{2}.processFcns = {'removeconstantrows'};
net.trainParam.epochs=1000;
net.trainParam.min_grad=0;
this block in order to finalize my network. However, I have not achieved any greater accuracy the 52%.. Is there any specific suggestion.? Thank you everyone. I hope I was accurate enough.

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Greg Heath
Greg Heath 2016-2-22
1. It looks like you can delete the above equations because they are defaults.
2. You definitely need to substantially reduce the No. of hidden nodes
[ I N ] = size(input) % = ?
[ O N ] = size(target)% = ?
Ntrn = N -2*round(0.15*N)% = ?
Ntrneq = Ntrn*O % =? No. of training equations
For an I-H-O net, the number of unknown weights is
Nw = (I+1)H+(H+1)*O
No. of unknowns is not larger than number of equations when
H < Hub = (Ntrneq-O)/(I+O+1)
Using the code at
help patternnet
and/or
doc patternnet
where H = 10 (default)
find out how much of the average target variance the net can model
MSE00 = mean((vartarget',1)); MSE reference
NMSE = mse(target-output)/MSE00 %Normalized MSE
Rsq = 1-NMSE ( > 0.99 is my goal)
Loop over i = 1:Ntrials = 10, Then check the 10 results to see if H can be decreased or should be increased.
For examples, search the NEWSGROUP and ANSWERS using
patternnet greg
Hope this helps
Thank you for formally accepting my answer
Greg

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Andreas
Andreas 2016-2-23
编辑：Andreas 2016-2-23
Thank you so much, I really appreciate your answer! I guess you mean
MSE00 = mean(var(target',1))
and I choose the setting with the highest Rsq. However, although my targets are formatted through the 1-out of-C philosophy, the outputs I get is not 0 or 1 vector, but there are also values inbetween (such as 0.012, 0.534 etc.) Is that correct? Should I post-process something?

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