Pinkesh Badjatiya pinkeshbadjatiya
wassname
/ keras_attention_wrapper.py
Created
November 1, 2016 08:06
A keras attention layer that wraps RNN layers.
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| """ | |
| A keras attention layer that wraps RNN layers. | |
| Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) | |
| and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449). | |
| date: 20161101 | |
| author: wassname | |
| url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a | |
| """ |
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: |
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
/ class_weights.py
Created
January 23, 2017 11:55
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| def get_class_weights(y, smooth_factor=0): | |
| """ | |
| Returns the weights for each class based on the frequencies of the samples | |
| :param smooth_factor: factor that smooths extremely uneven weights | |
| :param y: list of true labels (the labels must be hashable) | |
| :return: dictionary with the weight for each class | |
| """ | |
| counter = Counter(y) | |
| if smooth_factor > 0: |