[MADLIB-1338] DL: Add support for reporting various metrics in fit/evaluate

• Type: New Feature
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Fix Version/s: v1.16
• Component/s: Deep Learning
• Assignee: Unassigned
• Reporter: Nandish Jayaram
• Created: 02/May/19 19:03
• Updated: 29/May/19 23:21
• Resolved: 29/May/19 23:21

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 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 for more details

2. metric param can be optional

3. Maybe we should rename all the related output column as metric instead of metrics