Frank McQuillan created MADLIB-1400:
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Summary: 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
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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