Cutting off effective history when evaluating char_cnn model. about tcn HOT 4 CLOSED

If you are referring to the language modeling tasks, then no, it wouldn't be a problem, because we enumerate the sequences by validseqlen length as well (see https://github.com/locuslab/TCN/blob/master/TCN/char_cnn/char_cnn_test.py#L92), which means we do evaluate the entire dataset. We basically are using a "shifted window" scheme:

Iteration 1: [---{------ L ---------}]
Iteration 2: .................................[---{------ L ---------}]
Iteration 3: ..................................................................[---{------ L ---------}]

where "{...}" contains the validseqlen elements that absorb enough history information and are used to compute the loss, and "[...]" contains the sequence fed into TCN. L is the sequence length. Not sure if the "illustration" above helps; let me know if you want some further clarifications :-)

from tcn.

Hi thanks for the quick answer, thanks for the illustration, I understand clearly now :D
So like you showed in the example above, only the very first (seqlen - validseqlen) of the dataset are not evaluated ?

from tcn.

That is correct. You can also evaluate them (i.e., evaluate the entire first sequence, and use the validseqlen for the rest of the sequences), but it probably wouldn't affect the performance (perplexity or bpc) because it's only a very small portion of the dataset.

from tcn.

Great thank you very much for the clarification, i will do that adjustment !

from tcn.