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Re: validation in neural network
Posted:
Mar 10, 2013 11:36 AM
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"Anurag Bishnoi" wrote in message <khh89h$n7c$1@newscl01ah.mathworks.com>...
> what is the difference between validation and testing in neural network training?
>
> what is the meaning of line " Best validation performance is 11.23334 at 10 epochs?
>
> plz explain in detail
Training has stopped because the performance on the nontraining validation set has
failed to decrease for
tr.trainParam.max_fail = net.trainParam.max_fail epochs (default = 6).
also see tr.stop.
Assuming the default, training stopped at tr.num_epochs = 16. However, the nontraining validation set is a representative of the parent population of data. Therefore, to achieve a net that performs well on the parent population (good generalization), the previous design at
tr.best_epoch = tr.num_epochs - tr.trainParam.max_fail
is chosen.
Consequently, to obtain a completely unbiased estimate of generalization performance, use
MSEtest = tr.best_tperf
instead of
MSEtest = tr.tperf(end).
Hope this helps.
Greg
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