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https://bugzilla.redhat.com/show_bug.cgi?id=497001





--- Comment #7 from Fabian Deutsch <[email protected]>  2009-07-29 16:34:25 
EDT ---
To explain those tests: The neural net itself is detemrinistic afaik. But the
test uses random numbers and the learning algorithm just finds local maxima, so
it can happen that the test fails. That one big disadvanteg of neural networks:
you never know if the solution you find is the best ...
Concluding: It's is correct that we pass even if the test fails, because the
test is just checking the funcionality of the net and as even bad networks are
neural networks the test actually is ok.
Normally you stop training a network by looking at the mean square error or
after a certain time.

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