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https://issues.apache.org/jira/browse/SYSTEMML-1677?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16053451#comment-16053451
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Mike Dusenberry commented on SYSTEMML-1677:
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I thought about this a bit more, and if we can transpose the shape from `(N,
C*H*W)` to `(N*H*W, C)`, then we can apply the regular `softmax` layer and
transpose the outputs back to the original shape afterwards. We could do the
same for the cross entropy loss as well. Ideally, we should add a
`transpose4d(X, C, Hin, Win, new_dim1, new_dim2, new_dim3, new_dim4)`
builtin-in function with the same functionality as NumPy's [transpose
function](https://docs.scipy.org/doc/numpy-1.12.0/reference/generated/numpy.transpose.html).
In the short term, if we could create a couple of efficient utility functions
`util::NCHW_to_NHWC(X, C, H, W)` and `util::NHWC(X, C, H, W)` that convert from
`(N, C*H*W)` to `(N*H*W, C)` (or maybe just to `(N, H*W*C)`, which could then
be trivially reshaped to `(N*H*W, C)`), then we could easily implement 2D
softmax and 2D cross entropy loss (or just let the user take care of the
conversions using the utility functions directly, rather than adding new
layers).
> Add a new 2D cross-entropy layer
> --------------------------------
>
> Key: SYSTEMML-1677
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1677
> Project: SystemML
> Issue Type: Sub-task
> Reporter: Mike Dusenberry
> Assignee: Mike Dusenberry
>
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