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From "Mike Dusenberry (JIRA)" <>
Subject [jira] [Updated] (SYSTEMML-1736) Add new 2D top_k utility function
Date Wed, 28 Jun 2017 20:02:00 GMT


Mike Dusenberry updated SYSTEMML-1736:
    Issue Type: New Feature  (was: Sub-task)
        Parent:     (was: SYSTEMML-618)

> Add new 2D top_k utility function
> ---------------------------------
>                 Key: SYSTEMML-1736
>                 URL:
>             Project: SystemML
>          Issue Type: New Feature
>            Reporter: Mike Dusenberry
>            Assignee: Fei Hu
>              Labels: SYSTEMML-618
>             Fix For: SystemML 1.0
> 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 |].
 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}}
> 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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