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