Keras Attention Layer
Dead-simple Attention layer implementation in Keras based on the work of Yang et al. "Hierarchical Attention Networks for Document Classification"
Notice: the initial version of this repository was based on the implementation by Christos Baziotis. However, recently this repository was rewritten from scratch with the following features:
- Compatibility with Keras 2.2 (tested with TensorFlow 1.8.0);
- Annotations showing dimension transformations and equations;
- Numerically stable softmax using the exp-normalize trick; (New!)
- Easy way to recover the attention weights applied to each sample to make nice visualizations (see neat-vision); (Updated!)
- Example showing differences between vanilla, attention model and attention with masking;
- Example on the sum toy task showing how attention weights can be distributed across timesteps in a sample; (New!)
- Example on sentiment analysis of movie reviews (but GitHub does not render notebook markup, you may want to download the notebook to see word highlights, as in the example below); (New!)
- Allows customizing the attention activation function, since removing it might be beneficial for some tasks, as shown in "A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task" by Chen et al. (New!)
Example of attention on words for sentiment classification in a movie review in the Keras IMDb dataset. Darker colors mean larger weights and, consequently, more importance is given to those term.
Example of attention weights across timesteps during the classification of a sequential sample.