Re:Why GEMM is at the heart of deep learning
by simonster on Monday April 20, 2015 @11:34PM
(#9412146)
Either I'm missing something here or the article is correct. Generally, the first dimension of a matrix corresponds to the rows and the second dimension to the columns. So in the original figure, A is m x k, B is k x n, and C is m x n, and this seems right. See http://en.wikipedia.org/wiki/Matrix_%28mathematics%29#Size
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Re:Why GEMM is at the heart of deep learning
by dplarson on Tuesday April 21, 2015 @01:36AM
(#9412437)
I think the author revised the figure(s) between the time of the parent comment (by gcr) and your comment. At least, the A * B = C figure's filename seems to imply a revision [1].
EDIT: yep, the figures were revised. Compare the corrected version [1] vs the original [2].
[1] https://petewarden.files.wordpress.com/2015/04/gemm_correcte...
[2] https://petewarden.files.wordpress.com/2015/04/gemm.png
EDIT 2: I completely missed that the author put a notice (about having revised the figures) at the bottom of the post.
Re:Why GEMM is at the heart of deep learning
by petewarden on Tuesday April 21, 2015 @02:26AM
(#9412542)
Yes, that was my screwup, sorry for any confusion! Despite multiple linear algebra courses, working in 3D graphics for a decade, I haven't internalized the basics of matrix notation. It doesn't help that my usual sandbox (Eigen) is column major by default, which is the wrong in-memory order for my raster-image trained brain to visualize.
Funnily enough, I find tensor notation a bit easier, despite being less familiar.