Comments (1)
Hi, the Transformer implemented in this repo is very similar to the original model described in Attention Is All You Need, I would suggest heading there for more information. To answer your questions,
- The Transformer is a coherent many to many model, i.e. we predict one single output for each input. Using the current architecture, it is not suited for forecasting
- We implemented this Transformer with regression in mind, but you should be able to apply it to classification, see for instance #18 (pinned for more visibility)
If you want to add some modules, or modify the model itself (for forcasting or classification for example), don't hesitate to fork and PR. And thanks for the links, I'll be sure to check them out !
from transformer.
Related Issues (20)
- Some questions about the prediction HOT 3
- can you explain more on dimension arguments to transformer class ? HOT 1
- Runtime error: mat1 dim 1 must match mat2 dim 0
- Citation Bibtex HOT 3
- Why the sigmoid in the transformer? HOT 6
- Issue while training the model HOT 1
- Get Error/Applying Univariate Time Series Dataset HOT 3
- Possibility for time series anomaly detection? HOT 3
- Can time series A be used to predict time series Bīŧ HOT 4
- Hello, thanks for your great works, I'm confused with the dataset. HOT 10
- Question about input of the decoder HOT 1
- How do I set d_model, q, v, h, N, dropout, attention_size value? HOT 1
- The input dimension??? HOT 10
- A question HOT 3
- How to set Positional encoding HOT 4
- How to change the program to a classification model ? HOT 1
- RuntimeError: Given groups=1, weight of size [48, 37, 11], expected input[8, 691, 18] to have 37 channels, but got 691 channels instead HOT 6
- Position Encoding HOT 1
- cannot import name 'Transformer' from 'tst' HOT 1
- Questions about the paper. HOT 3
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from transformer.