Causality
See original GitHub issueHi,
In which part of the code you make sure the network is causal? Is it the Chomp1d
?
Thanks a lot! Amir
Issue Analytics
- State:
- Created 5 years ago
- Reactions:1
- Comments:8 (3 by maintainers)
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Top GitHub Comments
Please see Figure 1(a) in our paper for reference. We pad by (k-1)*d on the two sides of the input for convolution, and then use Chomp1d to remove the (k-1)*d output elements on the right.
If you draw a figure yourself, you would find that this is equivalent to removing the “future elements”, which ensures causality.
Thanks for the prompt reply.
Do you mean you pad by (k-1)*d zeros on both sides of input? It seems only on left side in Figure 1(a). Besides, padding on left side makes sense to me. I must misunderstand something.
Will there be some leakage if we only pad on left side without Chomp1d? Based on my current understanding, if we only pad (k-1)*d elements on left side, convolutional layer will produce a sequence with the same length of input. Is there something wrong?
Thanks!