Comments (2)
You can certainly include the others (for instance, take the last 5 outputs and flatten them to a vector). Since y1[:, :, -1] has a receptive field that covers all the input pixels in the sequence, we think it is sufficient to just use this last output (which is what RNNs do on this task).
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Thanks for your reply, I read your paper again, maybe I got it๐
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Related Issues (20)
- ๅฝๆฐ่ฐ็จ้ฎ้ข
- LSTM and RNN used and issues of compatibility HOT 1
- issue about Input of TCN HOT 1
- ModuleNotFoundError: No module named 'tcn' HOT 2
- Clarification on figure 3(a) HOT 4
- Training on variable-length sequences HOT 1
- copy memory questions
- why raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enabled
- seq2seq
- How should I choose correct layers number?
- How to save model?
- Code Question about: input the final conv-layer output to the linear layer
- What is the accuracy supposed to be for the MNIST problem?
- Is TCN suitable for spatio-temporal data? HOT 7
- why?
- Correlate .mat files with songs in Nottingham dataset
- Zero padding - possibly incorrect behavior? HOT 1
- DDP training with TCN Model
- do you have code examples for multivariate time series
- loss=nan
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