Ruben rrunix
goerz
/ revtex.tplx
Last active
February 22, 2021 09:33
custom jupyter nbconvert latex template, using revtex
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| ((*- extends 'article.tplx' -*)) | |
| % See http://blog.juliusschulz.de/blog/ultimate-ipython-notebook#templates | |
| % for some useful tips | |
| %=============================================================================== | |
| % Document class | |
| %=============================================================================== | |
| ((* block docclass *)) |
cbaziotis
/ AttentionWithContext.py
Last active
April 25, 2022 14:37
Keras Layer that implements an Attention mechanism, with a context/query vector, for temporal data. Supports Masking. Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf] "Hierarchical Attention Networks for Document Classification"
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| def dot_product(x, kernel): | |
| """ | |
| Wrapper for dot product operation, in order to be compatible with both | |
| Theano and Tensorflow | |
| Args: | |
| x (): input | |
| kernel (): weights | |
| Returns: | |
| """ | |
| if K.backend() == 'tensorflow': |
cbaziotis
/ Attention.py
Last active
October 22, 2024 08:31
Keras Layer that implements an Attention mechanism for temporal data. Supports Masking. Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
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| from keras import backend as K, initializers, regularizers, constraints | |
| from keras.engine.topology import Layer | |
| def dot_product(x, kernel): | |
| """ | |
| Wrapper for dot product operation, in order to be compatible with both | |
| Theano and Tensorflow | |
| Args: |
mbollmann
/ attention_lstm.py
Last active
August 22, 2024 07:06
My attempt at creating an LSTM with attention in Keras
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| class AttentionLSTM(LSTM): | |
| """LSTM with attention mechanism | |
| This is an LSTM incorporating an attention mechanism into its hidden states. | |
| Currently, the context vector calculated from the attended vector is fed | |
| into the model's internal states, closely following the model by Xu et al. | |
| (2016, Sec. 3.1.2), using a soft attention model following | |
| Bahdanau et al. (2014). | |
| The layer expects two inputs instead of the usual one: |