Linear3D #2508
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Linear3D #2508
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lozhnikov
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Jul 10, 2020
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@lozhnikov Memory errors fixed! (I don't know how :D) |
lozhnikov
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Aug 6, 2020
If I remember right, we changed the ordering in |
Right. I will update the description :) |
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![mlpack-bot[bot]](https://avatars.githubusercontent.com/in/23934?s=60&v=4){kind=small}
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@mlpack-jenkins test this please |
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I checked the Linear3D tests and the Lookup tests with valgrind. It didn't show any memory errors. So, I assume these errors are in other tests/methods. If no one objects, I'll merge the PR tomorrow. |
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I merged the PR. Thanks for the contribution! |
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The current Linear layer works well for the 2D input, like if a data point has 'n' features. Then the shape of input is (n, batchSize).
The size of the weight for the 2D Linear layer is
(inSize, outSize).But when we have 3D input where every batch contains multiple data points and each data points has 'n' features. Like in the case of word embeddings, where each batch contains multiple sequences/sentences and each sequence contains embedding vector for each word. So, effectively the shape of such input will be
(sequenceLength, embeddingSize, batchSize), and the number of features isembeddingSize. Now when we try to use the existing Linear layer, we need to vectorize each slice so that shape becomes(sequenceLength * embeddingSize, batchSize). And the number of features is taken assequenceLength * embeddingSizewhich is not true. Even if we try to do such a thing, the number of parameters (the shape of weight will be(sequenceLength * embeddingSize, outSize)) will be much higher than what it should be.In this pull request, I have tried to solve this issue. Let me know your thoughts.
(I asked about this on the IRC but when I am searching those messages now, I'm not getting. Looks like sometimes my messages go to Bermuda triangle :D)