Allow for Transformer layers operating on densely connected graph#25
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Allow for Transformer layers operating on densely connected graph#25
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pashminacameron
approved these changes
Jul 12, 2020
| D ~ vertex representation _d_imension | ||
| H ~ attention _h_ead size | ||
| N ~ _n_um attention heads | ||
| P ~ _n_number of node pairs in total, where P = sum(graph_size**2 for graph_size in batch) |
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P ~ number of node _p_airs in total
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| # Compute attention scores between queries and keys by doing an inner product, | ||
| # normalised by the square root of the representation size. | ||
| scores = tf.einsum( |
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I find it useful to keep information about which kind of attention is being implemented when there is an einsum involved in a comment, for example: Attention(Q, K) = softmax(Q K^T/sqrt(D)) ?
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Eh, I included the comment above the einsum to clarify the attention kind, but I think what you mean is you would prefer to have it as a math-y formula as well?
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Yes, I meant the formula, but it's not a biggy. Sorry I forgot about this one - It's easy to forget these PRs exist in amongst all the VSTS noise..
pashminacameron
approved these changes
Jul 12, 2020
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