Back propagation training problems



Hi,

I've written a simple neural network learning with back propagation
algorithm.
My network has 6 inputs - these are large numbers (first 5 are between 0 and
540 and the last is between -180 and 180). It is a 3 layer network - 20
neurons in the input layer ,10 in hidden layer and 1 in output layer. I want
my network to return values from (0,1) than I wrote a function f: <0,1> ->
<-5,5> anf f^-1 because these are the values I need in the end.
The input layer has activation function like that: 1 / (1 + e^(-s/100)
hidden layer function is 1 / (1 + e^(-s)
output layer: 1 / (1 + e^(-s/10)
I'm training the network with a correct value which is always 0 (for test
purposes) but as the network back-propagates the output is going towards 1
(which is 5 in the end).
Can somebody figure out what may be the problem? If you need any more
information about the network just tell me.

Thank you very much and best regards,
Max


.



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