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https://issues.apache.org/jira/browse/MADLIB-1452?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan reassigned MADLIB-1452:
---------------------------------------
Assignee: Orhan Kislal
> Add top n to evalute()
> ----------------------
>
> Key: MADLIB-1452
> URL: https://issues.apache.org/jira/browse/MADLIB-1452
> Project: Apache MADlib
> Issue Type: Improvement
> Components: Deep Learning
> Reporter: Frank McQuillan
> Assignee: Orhan Kislal
> Priority: Major
> Fix For: v1.18.0
>
>
> Applies to
> https://madlib.apache.org/docs/latest/group__grp__keras.html#keras_evaluate
> &
> https://madlib.apache.org/docs/latest/group__grp__keras__run__model__selection.html#keras_evaluate
> Add a new parameter to the evaluate interface:
> {code}
> madlib_keras_evaluate(
> model_table,
> test_table,
> output_table,
> use_gpus,
> mst_key,
> top_n -- new parameter
> )
> {code}
> {code}
> top_n (optional)
> INTEGER[], default {1}. Array of top values to compute accuracy percentages
> using the metric from the training set. E.g., {1, 5, 10} means compute the
> top-1, top-5 and top-10 classification accuracies.
> {code}
> Add 2 new columns to the right side of the output table:
> {code}
> output_table
> TEXT. Name of table that validation output will be written to. Table contains:
> loss Loss value on evaluation dataset.
> metric Metric value on evaluation dataset,
> where 'metrics_type' below identifies the type of metric.
> metrics_type Type of metric used that was used in the
> training step.
> top_n_accuracy Array of percentage accuracies as per metric
> _type
> top_n Array defining the top n values used.
> {code}
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