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I am making simple leaf recognizing prorgam. I have 10 plant leaf data and total sample size about 660.

My input size 3, output layer 10. Hidden layers is changeable.(2 between 30)

First input data: compactness = (2*pi*Leaf Area) / (perimeter*perimeter)

Second input data: aspect Ratio = leaf windth / leaf height

Third input data: fullness = leaf Area / ( width * height )

And I am normalizing [0,1] all of data.

Output data is [1,0,0,0,0,0,0,0,0,0] for first plant species

[0,1,0,0,0,0,0,0,0,0] for second plant species

[0,0,1,0,0,0,0,0,0,0] for third plant species



[0,0,0,0,0,0,0,0,0,1] for tenth plant species.

But my error is about 32 and no recognize plant species.

My transfer function BipolarSigmoid and alfa value is 2. Learning Rate : 0.5 Momentum : 0.0

network = new ActivationNetwork(
            new BipolarSigmoidFunction(2), //activation func.
            3,                             //input count
            trackBar2.Value,        //hidden layer count
            10 );                          //output count     

        //Learning Network
        BackPropagationLearning backprob = new BackPropagationLearning( network );

What is my wrong? Thanks in advance. (sorry my english)
Closed because the post is not clear, or is incomplete and has not been phrased in a way that allows it to be fully understood.. Reported by Sergey Alexandrovich Kryukov, Kornfeld Eliyahu Peter, Peter Leow on Saturday, June 21, 2014 8:43pm.
Posted 20-Jun-14 12:26pm
Edited 21-Jun-14 20:43pm
(no name)118.5K

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