neural network how to orgnize the data

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Lama AlDeen
Lama AlDeen le 26 Avr 2015
Commenté : Lama AlDeen le 29 Avr 2015
I have a problem with nn classification ,I have 19 different classes , each class has 20 features and each feature has 200 samples(it could be more in future), how should I orgnize the input matrix ? is it 20 x (200*19) or (20 *19) x 200 , and the target 1 x (200 * 19) is that right ? or should I use eye() as I read in some answers? another question is how to decide the most appropirate number of hidden layers(middle layers not input and output) and number of nerouns in each layer ? the last thing .. neural network provide different results each run .. is it possible to save the best run net configration and use it later to provide the same or approximated results ??
thanks in advance

Réponse acceptée

Greg Heath
Greg Heath le 26 Avr 2015
[ I N ] = size(input) % [20 200 ]
[ O N ] = size(target) % [ 19 200 ]
If you made a mistake and each class has 200 samples then
[ I N ] = size(input) % [20 1800 ]
[ O N ] = size(target) % [ 19 1800 ]
For classification design posts, search the NEWSGROUP and ANSWERS using
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Hope this helps
Greg
  7 commentaires
Greg Heath
Greg Heath le 28 Avr 2015
CORRECTION:
Each of 19 classes has 200 examples of 20 dimensional column feature vectors.
[ I N ] = size(input) % [ 20 3800 ]
[ O N ] = size(target) % [ 19 3800 ]
The columns of the target are the columns of eye(19). Class indices are 1:19
The transformations between target and true class indices and between output and estimated class indices are
truclassindices = vec2ind(target);
target = ind2vec(truclassindices);
...
output = net(input);
estclassindices = vec2ind(output);
Hope this helps.
Thank you for formally accepting my answer
Greg
Lama AlDeen
Lama AlDeen le 29 Avr 2015
Thank you very very much Prof.Greg Heath ,and special appreciation for your patience

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