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Spencer

Strange back propagation behaviour (picure inside)

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Hi! The picture below shows the output from my program trying to learn a neural network the XOR function. EDIT: what it shows is the error rate (sqrt(output - expected)**2) for the 4 different inputs of the XOR func. the strange thing is that it only "learns" for one of the outputs...all the others dont adapt. I know this is quite i shot in the dark, but does anybody have any idea about what might be going on? I have checked and double checkde my code and i dont find anything wrong.....any other suggestions about what i can try to test my ANN on? thank you... --Spencer --Spencer "Relax, this dragon is sleeping..." [edited by - spencer on November 11, 2003 8:47:28 AM]

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I am using one hidden layer with 2 neurons and of course one output neuron and 2 inputs...



--Spencer

"Relax, this dragon is sleeping..."

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Are you using an extra input set to 1 in all neurons (I think this is known as "bias")? If not, try that.

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yes i am....doing that...and this is what really confuses me since it _should_ work..:/



--Spencer

"Relax, this dragon is sleeping..."

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Guest Anonymous Poster
You should try to find a Java applet that performs BP and compare your weights with the applet. I remember that on Generation5, there was one step of BP algorithm with weights and outputs, could be worth a look.

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Guest Anonymous Poster
btw. what software did you use to generate the graphs ? Matlab ?

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i will look at generation5 thanx

the software i use is gnuplot very good



--Spencer

"Relax, this dragon is sleeping..."

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Guest Anonymous Poster
Gnuplot, have to try this.

http://www.generation5.org/content/2001/xornet.asp

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can it make some difference that i use
tanh() as my activation function?
I think it shouldnt but you might know better....



--Spencer

"Relax, this dragon is sleeping..."

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