Author: Landon Rabern
Date: 12:44:44 07/03/03
Go up one level in this thread
On July 03, 2003 at 03:22:15, Christophe Theron wrote: >On July 02, 2003 at 13:13:43, Landon Rabern wrote: > >>On July 02, 2003 at 02:18:48, Dann Corbit wrote: >> >>>On July 02, 2003 at 02:03:20, Landon Rabern wrote: >>>[snip] >>>>I made an attempt to use a NN for determining extensions and reductions. It was >>>>evolved using a GA, kinda worked, but I ran out of time. to work on it at the >>>>end of school and don't have my computer anymore. The problem is that the NN is >>>>SLOW, even using x/(1+|x|) for activation instead of tanh(x). >>> >>>Precompute a hyperbolic tangent table and store it in an array. Speeds it up a >>>lot. >> >>Well, x/(1+|x|) is as fast or faster than a large table lookup. The slowdown >>was from all the looping necessary for the feedforward. >> >>Landon > > > >A stupid question maybe, but I'm very interested by this stuff: > >Do you really need a lot of accuracy for the "activation function"? Would it be >possible to consider a 256 values output for example? > >Would the lack of accuracy hurt? > >I'm not sure, but it seems to me that biological neurons do not need a lot of >accuracy in their output, and even worse: they are noisy. So I wonder if low >accuracy would be enough. > There are neural net models that work with only binary output. If the total input value exceeds some threshhold then you get a 1 otherwise a 0. The problem is with training them by back prop. But in this case I was using a Genetic Alg, so no back prop at all - so no problem. I might work, but I don't see the benefit - were you thinking for speed? The x/(1+|x|) is pretty fast to calculate, but perhaps the binary (or other discrete) would be faster. Something to try. Landon
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