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https://issues.apache.org/jira/browse/MADLIB-1389?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan closed MADLIB-1389.
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Resolution: Fixed
https://github.com/apache/madlib/pull/457
> Transfer learning for multi-model
> ---------------------------------
>
> Key: MADLIB-1389
> URL: https://issues.apache.org/jira/browse/MADLIB-1389
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Neural Networks
> Reporter: Frank McQuillan
> Priority: Major
> Fix For: v1.17
>
>
> Context
> The transfer learning workflow for 1.17 will be the same as 1.16. It means
> user needs to update the model architecture table with the weights to be used
> for initialization. If they are NULL, then Keras default initialization will
> be used (random, or perhaps what is specified in the model architecture which
> I think there might be options for initialization in model architecture).
> Story
> I think the only bit that is missing currently is to check the model table to
> see if there are any weights there, and if there are, to use them for
> initialization.
> Acceptance
> 1) Train a model with 4 MSTs and plot the loss/accuracy curves. Use 2 MSTs
> for one model architecture and 2 MSTs for a second model architecture.
> Perhaps use CIFAR-10 dataset.
> 2) Copy model weights over to the model architecture table for 2 of the
> models from step #1 (not all 4).
> 3) Train the same 4 models as #1 and plot the loss/accuracy curves. Check
> that the 2 transfer learning cases pick up from where they left off, and that
> the other 2 start from scratch (due to random initialization).
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