On Sat, 13 Sep 2003, Ovid wrote:

>  Hi all,
>  
>  I'm working with AI::NeuralNet::Mesh and I've seen a few areas that it can be improved sli
ghtly. 
>  Mainly, I can make it run clean under warnings and, according to some initial benchmarks, 
I can
>  give it a nice performance boost with a few tweaks.  However, I backed out my changes to b
e able
>  to build a more comprehensive test suite to ensure that I don't break anything.  This rais
es a
>  question for me.
>  
>  After training the neural network, assuming that I am using the same training data every t
ime (in
>  the same order), are the results deterministic across operating systems, CPUs, Perl versio
ns, etc?
>   From reading through the code, I don't see anything that would cause problems here, but I
'm not
>  sure.
>  
>  If the results *are* deterministic then I can go ahead and build the test suite and send t
his back
>  to the author.  Otherwise, I can only build the tests for me, but I'd prefer to be able le
t others
>  take advantage of my work.
>  

Some speedups would certainly be welcome. I don't know the module
code, but some general considerations on perfect repeatability across
OSes and CPUs would include the fact that some OS/CPU combinations
compute with doubles and some with long doubles. Also, special
function libraries (for the logistic and tanh activation functions)
will vary across the C libraries.

Theoretically, the error landscape for an NN optimization is full of
local minima, wich implies the existence of separatrices that can
magnify even small numerical discrepanies.  I have no idea, however,
if this is a practical problem with NN testing; if one tested just a
single batch learing step, I can't see how the divergence would grow
large.

--
Mark Kvale, neurobiophysicist
http://www.keck.ucsf.edu/~kvale/