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problems with logistic regressions #1

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ninegua opened this issue Mar 20, 2017 · 4 comments
Open

problems with logistic regressions #1

ninegua opened this issue Mar 20, 2017 · 4 comments
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@ninegua
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ninegua commented Mar 20, 2017

I've noticed two problems with logistic_regression:

  1. The second dot operation is not matched and turned into parfor.

  2. Result will be wrong if w -= ... is rewritten as w = w - ... in source code.

@ehsantn
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ehsantn commented Mar 20, 2017

The second dot is essentially a reduction which we don't support yet. Turning it to parfor is easy once we finalize the reduction format.

The second problem is troubling though. I'll take a look.

@ehsantn
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ehsantn commented Mar 27, 2017

The logistic regression issue is resolved.

@ninegua
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ninegua commented Apr 4, 2017

We could turn the general matrix multiply into a parfor over a sequential reduction, or with the recent support on reduction, a parfor over a parfor. Thoughts?

@ehsantn
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ehsantn commented Apr 4, 2017

Currently, the matrix multiply (first dot) is a parfor over a sequential reduction. I don't see any benefit for the parfor over parfor approach for this code since the reduction dimension is small.

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