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 slightly.
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 be able
to build a more comprehensive test suite to ensure that I don't break anything. This raises a
question for me.

After training the neural network, assuming that I am using the same training data every time (in
the same order), are the results deterministic across operating systems, CPUs, Perl versions, etc?
From reading through the code, I don't see anything that would cause problems here, but I'm not

If the results *are* deterministic then I can go ahead and build the test suite and send this back
to the author. Otherwise, I can only build the tests for me, but I'd prefer to be able let 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

Mark Kvale, neurobiophysicist

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postedSep 12, '03 at 3:44p
activeSep 16, '03 at 10:18p



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