Comments (4)
Hey! Thank you! It means a lot to me. There's an explanation in this thread: #14
I plan to add a good and more exhaustive explanation to the README soon!
from keras-attention.
No idea yet, is it that for permute? permute(1,1)?
from keras-attention.
@felixhao28 maybe you can answer that one.
from keras-attention.
The second argument is the axes to perform dot product. In this case it is equivalent to hidden_states * transpose(attention_weights)
from keras-attention.
Related Issues (20)
- pip install and numpy, keras packages are forced to be uninstalled HOT 1
- Use this repository for CNN HOT 1
- 2D attention HOT 6
- weird attention weights when adding sequence of numbers. HOT 1
- attention when using more than one feature HOT 1
- get_config HOT 14
- Using attention with multivariate timeseries data
- Loading model problems HOT 5
- Interpreting attention weights for more than one input features. HOT 2
- Add guidance to README to use Functional API for saving models that use this layer HOT 4
- Attention Mechanism not working HOT 10
- what do the h_t mean in the Attention model? HOT 1
- Output with multiple time steps HOT 1
- Attention not working for MLP HOT 2
- TypeError: Expected `trainable` argument to be a boolean, but got: 64 HOT 3
- Please update version HOT 1
- TypeError: __call__() takes 2 positional arguments but 3 were given HOT 2
- Number of parameters in Attention layer HOT 2
- Does it support causal mask? HOT 2
- Value Error occurs when I exercise your demo code
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from keras-attention.