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Mike Dusenberry resolved SYSTEMML-1686. --------------------------------------- Resolution: Fixed Assignee: Matthias Boehm (was: Mike Dusenberry) Fix Version/s: SystemML 1.0 Fixed in [commit 7602af9 | https://github.com/apache/systemml/commit/7602af94fbd9554f097a573bee87d1b51e709ccb]. > Transpose Conv2d has incorrect filter shape and incorrect input size argument > ----------------------------------------------------------------------------- > > Key: SYSTEMML-1686 > URL: https://issues.apache.org/jira/browse/SYSTEMML-1686 > Project: SystemML > Issue Type: Bug > Affects Versions: SystemML 1.0 > Reporter: Mike Dusenberry > Assignee: Matthias Boehm > Fix For: SystemML 1.0 > > > Currently, the transpose conv2d layer ([{{nn/layers/conv2d_tranpose.dml}} | > https://github.com/apache/systemml/blob/master/scripts/nn/layers/conv2d_transpose.dml] > has a bug in which the filters tensor {{W}} has an incorrect shape, and the > {{conv2d_backward_data}} op has an incorrect input shape argument. This > results in an exception when the number of input channels {{C}} is not equal > to the number of filters {{F}} (i.e. number of output channels). Since the > transpose conv2d op is the gradient of the conv2d op, the filter tensor needs > to have the shape {{C, F, Hf, Wf}} for {{F}} filters, rather than {{F, C, Hf, > Wf}}, in order to map from an input with {{C}} channels to an output with > {{F}} channels during the input data gradient function > ({{conv2d_backward_data}}) that is used in the forward pass. Additionally, > the {{input_shape}} argument for {{conv2d_backward_data}} needs to be {{N, F, > Hout, Wout}}, rather than {{N, C, Hout, Wout}} in order to map from an input > with {{C}} channels to an output with {{F}} channels. Our current test cases > did not catch this issue because the tests used {{C = F = 1}}. -- This message was sent by Atlassian JIRA (v6.4.14#64029)