[jira] [Updated] (MADLIB-1338) DL: Add support for reporting various metrics in fit/evaluate
[ https://issues.apache.org/jira/browse/MADLIB-1338?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Nikhil updated MADLIB-1338: --- 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 |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 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 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 al the related output column as metric instead of metrics > 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 -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (MADLIB-1338) DL: Add support for reporting various metrics in fit/evaluate
[ https://issues.apache.org/jira/browse/MADLIB-1338?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Nikhil updated MADLIB-1338: --- 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 |https://github.com/apache/madlib/pull/388/files]for more details 2. metric param can be optional 3. Maybe we should rename al the related output column as metric instead of metrics 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 be able to report any one of these metrics in the output table. Other requirements: 1. 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 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 al the related output column as metric instead of > metrics -- This message was sent by Atlassian JIRA (v7.6.3#76005)
[jira] [Updated] (MADLIB-1338) DL: Add support for reporting various metrics in fit/evaluate
[
https://issues.apache.org/jira/browse/MADLIB-1338?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Nandish Jayaram updated MADLIB-1338:
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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[jira] [Updated] (MADLIB-1338) DL: Add support for reporting various metrics in fit/evaluate
[
https://issues.apache.org/jira/browse/MADLIB-1338?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Orhan Kislal updated MADLIB-1338:
-
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 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.
was:
The current {{madlib_keras.fit()}} code reports accuracy as the only metric,
along with loss value. But we could ask for multiple metrics in compile params
(for eg., {{metrics=['mae','accuracy']}}), then {{Keras.evaluate()}} would
return back {{loss}} (by default), {{mean_absolute_error}} and {{accuracy}}
(metrics).
This JIRA requests support to report all of these metrics in the output table.
Other requirements:
1. Output summary table must have a 2-D array to report {{metrics}}. The inner
dimension corresponds to all metrics values for the iteration at which it is
computed.
1. Output summary table must have the metrics' labels (eg.,
[mean_absolute_error, accuracy])
Summary: DL: Add support for reporting various metrics in fit/evaluate
(was: DL: Add support for reporting multiple metrics in fit/evaluate)
> 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 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.
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