scale range for neural network

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mustafa alnasser
mustafa alnasser on 24 Oct 2014
Answered: Greg Heath on 27 Oct 2014
Dear All;
I have in neural network, : My input range is starting with small value , then it become big ( 1000 times of the small values) which a lot of data accumulate in small range and make conflict between detail in this range , how can we solve it . Also, if I have data range overlap at the boundary which may lead to misinterpretation, what is the best way to overcome this issue.
Regards;

Accepted Answer

Greg Heath
Greg Heath on 27 Oct 2014
I don't have any specific code. I would experiment with the following.
1. Worry about the gating later.
2. First, determine, by trial and error, subsets of effective ranges for inputs of fitnet. This will not be an easy task. It is quite possible that nonlinear input transformations (e.g., logs or powers ) may be be helpful.
3. Since fitnet defaults to mapminmax transformations of inputs and outputs before other calculations, what has to be determined is how to choose the different ranges of inputs that will be transformed to [-1,1].
4. If there are c input range categories the gating net targets should be {0,1} c-dimensional unit vectors. The transformations between the vectors and category indices are
targets = ind2vec(indices)
indices = vec2ind(targets)
Hope this helps.
Thank you for formally accepting my answer
Greg

More Answers (1)

Greg Heath
Greg Heath on 25 Oct 2014
This is easily solved by using a gating net that sends the input to a following net designed for a specific range of inputs.
Hope this helps
Thank you for formally accepting my answer
Greg
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mustafa alnasser
mustafa alnasser on 25 Oct 2014
Thank you for answer but how to use gating in neural network Regards

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