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From "Mike Dusenberry (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SYSTEMML-1678) Add new 1D & 2D top_k utility functions
Date Mon, 26 Jun 2017 21:39:00 GMT

    [ https://issues.apache.org/jira/browse/SYSTEMML-1678?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16063839#comment-16063839
] 

Mike Dusenberry commented on SYSTEMML-1678:
-------------------------------------------

[~mboehm7] Can you please look into this engine issue that has been discovered?  You should
be able to grab the PR, and run one DML file to reproduce.

> Add new 1D & 2D top_k utility functions
> ---------------------------------------
>
>                 Key: SYSTEMML-1678
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-1678
>             Project: SystemML
>          Issue Type: Sub-task
>            Reporter: Mike Dusenberry
>            Assignee: Fei Hu
>
> We should add new {{top_k}} and {{top_k2d}} utility functions (in {{nn/util.dml}}) that
accept 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 1D case, {{top_k}} will operate on the columns dimension.  A typical use case is
that in which {{X}} is the output of a {{softmax}} layer (so each row contains a set of normalized
class probabilities), and {{values}} and {{indices}} will contain rows with the top {{k}}
probabilities and class indices as described above.  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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