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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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Nandish Jayaram updated MADLIB-1338:
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    Description: 
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 loss/accuracy computation from `fit_transition`.

  was:
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 report all of these metrics in the output table.
Other requirements:

Output summary table must have the metrics' labels (instead of just accuracy)
Remove loss/accuracy computation from fit_transition.




> 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 loss/accuracy computation from `fit_transition`.



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