Comments (4)
Hi, PyTorch is telling you here that the mask (here represented by the variable attention_mask
) should be of type Bool
, but is Byte
. I encourage you to get deeper into this error to understand why the mask is of type Byte
, I can't help you with just this traceback. If you have more info, you can always post it here.
from transformer.
thanks.
I have found out the reason for the error, which has something to do with version of python
from transformer.
btw, I wonder what is the reason for changing softmax to sigmoid? looking forward to your reply :)
from transformer.
I'm currently working on a regression problem, softmax
is usually applied to classification, so it's not a good fit here. I'm using sigmoid
, but it could be replaced with any other activation function, such as ReLU
or its variations. Feel free to play around with the ones implemented in pytorch, it should be easy to plug !
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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