Comments (4)
tessavdheiden
commented on July 24, 2024
Hi, you can use TimeDistributed. Please let me know if you want more info/sample code!
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joyjitchatterjee
commented on July 24, 2024
@tessavdheiden it would be wonderful if you can give a sample code to use LSTM with attention for many to many sequence generation. Right now, my input is of dimensions (17113,1972) and output of (17113,26). The input sequence is a having 17113 time steps, with 1972 features, and the output is a bag of 26 words for 17113 time steps. Thanks.
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luoshao23
commented on July 24, 2024
@tessavdheiden It would be nice if you post a sample code. Thanks.
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philipperemy
commented on July 24, 2024
I advise you to use this one for seq2seq attention: https://github.com/farizrahman4u/seq2seq
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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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