Metadata-Version: 2.1
Name: attention
Version: 3.0
Summary: Keras Simple Attention
Home-page: UNKNOWN
Author: Philippe Remy
License: Apache 2.0
Description: # Keras Attention Mechanism
        [![license](https://img.shields.io/badge/License-Apache_2.0-brightgreen.svg)](https://github.com/philipperemy/keras-attention-mechanism/blob/master/LICENSE) [![dep1](https://img.shields.io/badge/Tensorflow-2.0+-brightgreen.svg)](https://www.tensorflow.org/) [![dep2](https://img.shields.io/badge/Keras-2.0+-brightgreen.svg)](https://keras.io/) 
        ![Simple Keras Attention CI](https://github.com/philipperemy/keras-attention-mechanism/workflows/Simple%20Keras%20Attention%20CI/badge.svg)
        
        Many-to-one attention mechanism for Keras.
        
        <p align="center">
          <img src="examples/equations.png" width="600">
        </p>
        
        
        Installation via pip
        
        ```bash
        pip install attention
        ```
        
        Import in the source code
        
        ```python
        from attention import Attention
        
        # [...]
        
        m = Sequential([
              LSTM(128, input_shape=(seq_length, 1), return_sequences=True),
              Attention(name='attention_weight'), # <--------- here.
              Dense(1, activation='linear')
        ])
        ```
        
        ## Examples
        
        Install the requirements before running the examples: `pip install -r requirements.txt`.
        
        ### IMDB Dataset
        
        In this experiment, we demonstrate that using attention yields a higher accuracy on the IMDB dataset. We consider two
        LSTM networks: one with this attention layer and the other one with a fully connected layer. Both have the same number
        of parameters for a fair comparison (250K).
        
        Here are the results on 10 runs. For every run, we record the max accuracy on the test set for 10 epochs.
        
        
        | Measure  | No Attention (250K params) | Attention (250K params) |
        | ------------- | ------------- | ------------- |
        | MAX Accuracy | 88.22 | 88.76 |
        | AVG Accuracy | 87.02 | 87.62 |
        | STDDEV Accuracy | 0.18 | 0.14 |
        
        As expected, there is a boost in accuracy for the model with attention. It also reduces the variability between the runs, which is something nice to have.
        
        
        ### Adding two numbers
        
        Let's consider the task of adding two numbers that come right after some delimiters (0 in this case):
        
        `x = [1, 2, 3, 0, 4, 5, 6, 0, 7, 8]`. Result is `y = 4 + 7 = 11`.
        
        The attention is expected to be the highest after the delimiters. An overview of the training is shown below, where the
        top represents the attention map and the bottom the ground truth. As the training  progresses, the model learns the 
        task and the attention map converges to the ground truth.
        
        <p align="center">
          <img src="examples/attention.gif" width="320">
        </p>
        
        ### Finding max of a sequence
        
        We consider many 1D sequences of the same length. The task is to find the maximum of each sequence. 
        
        We give the full sequence processed by the RNN layer to the attention layer. We expect the attention layer to focus on the maximum of each sequence.
        
        After a few epochs, the attention layer converges perfectly to what we expected.
        
        <p align="center">
          <img src="examples/readme/example.png" width="320">
        </p>
        
        ## References
        
        - https://www.cs.cmu.edu/~./hovy/papers/16HLT-hierarchical-attention-networks.pdf
        - https://arxiv.org/abs/1508.04025
        - https://arxiv.org/abs/1409.0473
        - https://github.com/philipperemy/keras-attention-mechanism/issues/14
        
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