Comments (1)
The sequence length doesn't have to be the same, you can end each sequence with a special delimiter. The input sequence length is also randomized between 10 to 20 in the copy task and you can even see an example of variable length output in the repeat-copy task where the NTM learns to output a sequence multiple times and end it with a delimiter.
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Related Issues (16)
- register_buffer HOT 1
- Strange fluctuation on curves even after large #seqs have been trained with HOT 6
- Why to create a new tensor? HOT 2
- Convergence is really slow with copy task when sequence length is smaller HOT 2
- Different results between testing in the mid terms of training and at the end of training HOT 3
- It would be nice if you can add the cuda option HOT 1
- What's the meaning of using memory? HOT 1
- Why use vague storage
- error in copy-task-plots.ipynb HOT 2
- Cannot reproduce README.md graphs/results on copy task [Commit: 5c5ce66] HOT 1
- Why it runs so slowly?
- Why did each batch has a memory? HOT 1
- How could u write down beta, gamma in your source code? HOT 1
- How to visual the training process and draw these animation? HOT 1
- why need to initialize read vector and memory? HOT 1
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from pytorch-ntm.