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Posted to issues@systemml.apache.org by "Mike Dusenberry (JIRA)" <ji...@apache.org> on 2017/07/05 17:24:00 UTC

[jira] [Updated] (SYSTEMML-1681) Add a unit test for 2D convolution comparing against another system

     [ https://issues.apache.org/jira/browse/SYSTEMML-1681?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Mike Dusenberry updated SYSTEMML-1681:
--------------------------------------
    Description: 
We should add a comparison against TensorFlow in {{nn/test/test.dml}}, like we have for other layers, and check that the same values are produced on CPU & GPU for both TensorFlow and SystemML.  There is already a {{conv2d}} function in {{nn/test/test.dml}} that compares the output of three different 2D convolution layers that we have (built-in, DML im2col, DML basic).  We should add a new section to this {{conv2d}} function (similar to how the {{max_pool2d}} test function has multiple test sections) to compare the built-in conv2d layer against the output from TensorFlow.  We should use the same {{X}}, {{W}}, and {{b}} initialization values as used in {{conv2d_transpose}}.


> Add a unit test for 2D convolution comparing against another system
> -------------------------------------------------------------------
>
>                 Key: SYSTEMML-1681
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-1681
>             Project: SystemML
>          Issue Type: New Feature
>            Reporter: Mike Dusenberry
>            Assignee: Mike Dusenberry
>
> We should add a comparison against TensorFlow in {{nn/test/test.dml}}, like we have for other layers, and check that the same values are produced on CPU & GPU for both TensorFlow and SystemML.  There is already a {{conv2d}} function in {{nn/test/test.dml}} that compares the output of three different 2D convolution layers that we have (built-in, DML im2col, DML basic).  We should add a new section to this {{conv2d}} function (similar to how the {{max_pool2d}} test function has multiple test sections) to compare the built-in conv2d layer against the output from TensorFlow.  We should use the same {{X}}, {{W}}, and {{b}} initialization values as used in {{conv2d_transpose}}.



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