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From Shadi Mari <shadim...@gmail.com>
Subject Re: Hivemall FFM & Criteo Dataset - LogLess counter
Date Tue, 17 Oct 2017 15:59:09 GMT
Makoto,

I am using the default hyper-parameters in addition to the following
settings:

feature_hashing: 20
classification is enabled
Iterations = 10
K = 2, another test using K = 4
Opt: FTRL (default)

I tried setting the initial learning to 0.2 and optimizer to AdaGrad with
no significant changes on the empirical loss.

Thanks






On Tue, Oct 17, 2017 at 6:51 PM, Makoto Yui <myui@apache.org> wrote:

> The empirical loss (cumulative logloss) is too large.
>
> The simple test in FieldAwareFactorizationMachineUDTFTest shows that
> empirical loss is decreasing properly but it seems optimization is not
> working correctly in your case.
>
> Could you show me the training hyperparameters?
>
> Makoto
>
> 2017-10-17 19:01 GMT+09:00 Shadi Mari <shadimari@gmail.com>:
> > Hello,
> >
> > I am trying to understand the results produced by FFM on each iteration
> > during the training of Criteo 2014 dataset.
> >
> > Basically, I have 10 mappers running concurrently (each has ~4.5M
> records),
> > and follows is an output by one of the mappers:
> >
> > -----------------------------
> >
> > fm.FactorizationMachineUDTF|: Wrote 4479491 records to a temporary file
> for
> > iterative training: hivemall_fm392724107368114556.sgmt (2.02 GiB)
> > Iteration #2 [curLosses=1.5967339372694769E10,
> > prevLosses=4.182558816480771E10, changeRate=0.6182399322209704,
> > #trainingExamples=4479491]
> >
> > -----------------------------
> >
> > Looking at the source code, FFM implementation uses LogLess performance
> > metric when classification is specified, however the curLossess counter
> is
> > very high 1.5967339372694769E10
> >
> >
> > What does this mean?
> >
> > Regards
> >
> >
>
>
>
> --
> Makoto YUI <myui AT apache.org>
> Research Engineer, Treasure Data, Inc.
> http://myui.github.io/
>

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