i am using backpropagation with 30 inputs, 2 hidden layers and 4
outputs and i need to know how to calculate the contribution of each
Please log in or register to post a reply.
The contribution of each input is the weight you assign to it. These
weights are what you change when you train your network.
If you mean what contribution each beginning input makes on the final
output then it is a bit more tricky. If this is the case I assume you
are asking how much you should change the weights on the lowest level
when all you know is what the output of the final level should be.
There are several algorithms for figuring the weight change and they all
revolve around the same idea. If your output is too high you lower the
weights from the nodes that are high and raise the weights of the nodes
that are low. you follow the same logic back to the next level. for
those nodes that are too high you not only lower their weight, but you
also adjust the weights to them just as if they were an output that was
too high. This process can be continued all the way to the origional
If this also is not what you meant, then im afraid you will have to try
to explain again what your trouble is.
thank you for your answer,
actually , i have developed my ANN model and testing it, i need to know
which input has the most effect in the output, also the effect of other
input in the output, the result is expected as a percentage of each
input, this is called contibution, and is shown in the commercial ANN
I believe what you are looking for is referred to as Example Influence.
Mark White at NCSU has some slides available online, where he covers
this. He has some videos of his lectures (scroll down to his lectures
from 2003), where you can listen to him give more information on the
slides, as well as some entertaining political digressions.
His pdf notes on example influence can be found here:
(see slide 33)
Lecture videos can be found here:
Its been a while for me, and I’d have to jog my memory and look at some
old code. If you are still having trouble, I can take a deeper look into