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https://issues.apache.org/jira/browse/MADLIB-1338?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan resolved MADLIB-1338.
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Resolution: Fixed
> DL: Add support for reporting various metrics in fit/evaluate
> -------------------------------------------------------------
>
> Key: MADLIB-1338
> URL: https://issues.apache.org/jira/browse/MADLIB-1338
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Deep Learning
> Reporter: Nandish Jayaram
> Priority: Major
> Fix For: v1.16
>
>
> The current `madlib_keras.fit()` code reports accuracy as the only metric,
> along with loss value. But we could ask for different metrics in compile
> params (`mae`, `binary_accuracy ` etc.), then `Keras.evaluate()` would return
> back `loss` (by default) and `mean_absolute_error` or `binary_accuracy`
> (metrics).
> This JIRA requests support to be able to report any one of these metrics in
> the output table.
> Other requirements:
> 1. Remove training loss/accuracy computation from `fit_transition` and
> instead use the evaluate function to calculate the training loss/metric. See
> PR [https://github.com/apache/madlib/pull/388
> |https://github.com/apache/madlib/pull/388/files]for more details
> 2. metric param can be optional
> 3. Maybe we should rename all the related output column as metric instead of
> metrics
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