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I have ECG data and sleep annotations as target. The data is recorded in sessions. For one case, I want to use each session as an input sample. Therefore, I need to zero-pad the input data to achieve same length/dimension. What do I do with the target data?

Do I also "zero-pad" to achieve the same length/dimension? I could use a new state (e.g. 666 as 0 is already in use) which will then not be considered by using a masking layer on the particular zero-padded input data.

Or do I just leave the target as it is?

Thanks for your help

edit: more info about the data

Florida Man
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  • It it a semantic segmentation target? – marcopah May 15 '18 at 18:45
  • Dear marcopah, sorry I thought that would not matter. The data is actually ECG data and the target sleep annotations. I have different sleep session length and therefore need to zeropad for the case of using one session as a sample. Thanks in advance – Florida Man May 15 '18 at 20:24

1 Answers1

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after some logical thinking, I came to the obvious conclusion, that you have to pad also the target. As the samples are of different length apparently, you need to pad to be able to create a tensor with fixed dimensions.

I would create a new label for the mask_value. For this labels, there is no training as these timesteps are skipped and the weights are set to zero via the masking.

Florida Man
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