neural network how to orgnize the data
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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
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Greg Heath
2015년 4월 26일
[ 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
greg patternnet
Hope this helps
Greg
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Greg Heath
2015년 4월 28일
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
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