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https://issues.apache.org/jira/browse/SYSTEMML-1736?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Mike Dusenberry reassigned SYSTEMML-1736:
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Assignee: Mike Dusenberry (was: Fei Hu)
> Add new 2D top_k utility function
> ---------------------------------
>
> Key: SYSTEMML-1736
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1736
> Project: SystemML
> Issue Type: Sub-task
> Reporter: Mike Dusenberry
> Assignee: Mike Dusenberry
>
> We should add a new {{top_k2d}} utility function (in {{nn/util.dml}}) that
> accepts a matrix {{X}} and return matrices {{values}} and {{indices}} with
> the top {{k}} values (i.e. probabilities) and associated indices (i.e.
> classes) along a certain dimension. This will be modeled after the
> [{{top_k}} function in TensorFlow |
> https://www.tensorflow.org/api_docs/python/tf/nn/top_k]. For the 2D case,
> {{top_k}} will operate on the channels dimension. A typical use case here is
> that in which {{X}} is the output of a {{softmax2d}} layer (so each channel
> contains a set of normalized class probabilities), and {{values}} and
> {{indices}} will contain the top {{k}} probabilities and indices along the
> channel axis. This scenario would be common in an image segmentation
> problem, in which every pixel of the output image will have a set of class
> probabilities along the channel axis.
> Having these {{top-k}} functions will allow us to extract either predict a
> single class for each item, or the top {{k}} classes, and therefore may be
> more useful that a {{predict_class}} function.
> Although we will use {{values}} and {{indices}} as the names of the returned
> matrices within the functions, in practice, one is likely to name the results
> {{probs}} and {{classes}} in the calling environment.
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