Mike Dusenberry created SYSTEMML-1736:
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Summary: 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: Fei Hu
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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