I am freshman in NN. Now, I am working Back propagation. I have the
analogue input and binary output. I dont know what activation functions
I use for backpropagetion with the single hidden layer.
Please, help me.
I program the backpropgation based on errors. If I used sigmoid function
then result is not good, and step function I cant.
Thanks for help.
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What do you mean by “result is not good”? Sigmoid and similar functions
are the standard choice for multilayer feed-forward networks.
You can use the sigmoid function… But you should remember that it’s
output is asymptotic… The sigmoid function “goes to 0” at minus
infinity, and “goes to 1” at infinity, but never actually reaches those
values. Training your network to reach them can result in very large
weights, which does not help convergence.
If you use the sigmoid function, you should probably train your network
to output values in a restricted range like [0.25, 0.75], the lowest
representing 0, and then interpret outputs under 0.5 as being 0, and
over 0.5 as 1.
Thank Nyx and Reedbeta very much!
I used the sigmoid functions for my problem. But the output signals are
only in range (0.49 - 0.50). I feel they are not good. Can I interpret
outputs < 0.5 like 0, > 0.5 like 1? If I do that, the error of problem
Can You help me!