Hi Jörn, I don’t see a problem with it. Make sure the default is set to the current value. Are you making the fix? I could get to it later tonight. Daniel
> On Aug 29, 2017, at 10:32 AM, Joern Kottmann <[email protected]> wrote: > > Hi Daniel, > > do you see any issue if we expose LLThreshold and allow the user to > change it via training parameters? > > Jörn > > On Sat, Aug 26, 2017 at 1:07 AM, Daniel Russ <[email protected]> wrote: >> Jörn, >> >> Currently, GISTrainer has a private static final variable LLThreshold, >> which controls if the change in the log likelihood between two iterations is >> too small. We could make this parameter. I am concerned about using the >> accuracy to train the model. If we use accuracy, the weight space may be >> flat. >> >> Saurabh, you use the term “early stopping”. In deep learning, early >> stopping is used to prevent overtraining and improve generalization to >> unseen data. I am not sure early stopping serves the same purpose with GIS >> training. Does anyone know if early stopping improves generalization for a >> maxent problem? >> >> Daniel >> >>> On Aug 24, 2017, at 4:48 AM, Joern Kottmann <[email protected]> wrote: >>> >>> You are the first one who ever asked this question. I think we have this as >>> an option already on the gis trainer but it is not exposed all the way >>> through. >>> >>> Please open a jira and I can look at it next week. >>> >>> Jörn >>> >>> On Aug 21, 2017 5:11 PM, "Saurabh Jain" <[email protected]> wrote: >>> >>>> Hi All >>>> >>>> How can we use early stopping while training/crossvalidating custom data >>>> with NameFinder ? What I want if change in likelihood value or accuracy of >>>> model is less than 0.05 between two steps (differ by 5 i.e compare x+5 step >>>> output with x step) then training should stop. I could not find anything >>>> regarding this in documentation. Can some one please help ? >>>> >>>> -- >>>> *Thanks & Regards* >>>> >>>> >>>> *Saurabh Jain * >>>> *AI Developer* >>>> >>>> *Active Intelligence * >>>> >>>> *"* >>>> *To do a thing yesterday was the best time . Second best time is today .” * >>>> >>
