[jira] [Resolved] (MADLIB-1400) Modify warm start logic for DL to handle case of missing weight

["Orhan Kislal (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MADLIB-1400?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Orhan Kislal resolved MADLIB-1400.
----------------------------------
      Assignee: Orhan Kislal
    Resolution: Fixed

> Modify warm start logic for DL to handle case of missing weight
> ---------------------------------------------------------------
>
>                 Key: MADLIB-1400
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1400
>             Project: Apache MADlib
>          Issue Type: Improvement
>          Components: Deep Learning
>            Reporter: Frank McQuillan
>            Assignee: Orhan Kislal
>            Priority: Major
>             Fix For: v1.17
>
>         Attachments: 20191219_163748.jpg
>
>
> I was trying to implement an autoML algorithm on top of the new multi-model fit and ran into an issue with warm start.  I would suggest a slight change in logic:
> Currently if there are not existing models+weights in the model table for every single MST key in the MST table, we error out when warm start = TRUE.
> I suggest if there is not an entry in the model table+weights for an MST key in the MST table, then randomly initialize the weights (or whatever the default is in Keras) and do not error out.  Of course, if there are existing models+weights in the model table for an MST key in the MST table, then use them (which is currently what we do). 
> Again this all applies to warm start = TRUE only.
> Oh, if warm start = TRUE but the model table does not exist at all, we should error out like we do today.
> Logic for warm start = FALSE as implemented seems OK to me.



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