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Bahdanau attention Python code


The Bahdanau attention mechanism is a type of attention mechanism used in neural machine translation to address the performance bottleneck of conventional encoder-decoder architectures. It was proposed by Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio in their paper "Neural Machine Translation by Jointly Learning to Align and Translate" in 2015[1][3].

Key Components

1. Encoder-Decoder Architecture: The Bahdanau attention mechanism is part of an encoder-decoder architecture. The encoder generates a set of annotations from the input sentence, and the decoder uses these annotations to generate the translation[3].

2. Attention Mechanism: The attention mechanism is used to weigh the importance of different parts of the input sentence when generating the translation. This is achieved by calculating an attention score for each part of the input sentence and then using these scores to compute a weighted sum of the corresponding parts of the encoder output[3].

3. Weight Normalization: The Bahdanau attention mechanism can be used with or without weight normalization. Weight normalization helps in stabilizing the training process by normalizing the attention weights to ensure they are within a specific range[1].

Implementation

The Bahdanau attention mechanism can be implemented using TensorFlow Addons. The `tfa.seq2seq.BahdanauAttention` class provides the necessary functionality to create a Bahdanau attention object. This object can be used in the `tfa.seq2seq.AttentionWrapper` class to create an attention wrapper that applies the Bahdanau attention mechanism to the output of an RNN encoder[1].

Example Code

Here is an example of how to implement the Bahdanau attention mechanism using TensorFlow and Keras:
python
import tensorflow as tf
from tensorflow.keras.layers import GRU, Dense, AdditiveAttention

class BahdanauAttention(Layer):
    def __init__(self, attnUnits, **kwargs):
        super().__init__(**kwargs)
        self.attnUnits = attnUnits

    def build(self, inputShape):
        self.denseEncoderAnnotation = Dense(
            units=self.attnUnits, use_bias=False
        )
        self.denseDecoderAnnotation = Dense(
            units=self.attnUnits, use_bias=False
        )
        self.attention = AdditiveAttention()

    def call(self, inputs):
        encoderOutput, decoderState = inputs
        encoderAnnotation = self.denseEncoderAnnotation(encoderOutput)
        decoderAnnotation = self.denseDecoderAnnotation(decoderState)
        attentionWeights = self.attention(encoderAnnotation, decoderAnnotation)
        contextVector = tf.reduce_sum(
            encoderOutput * attentionWeights[:, :, None], axis=1
        )
        return contextVector

# Example usage
attnUnits = 128
attention = BahdanauAttention(attnUnits)

Context Vector Shape

The context vector resulting from Bahdanau attention is a weighted average of all the hidden states of the encoder. The shape of the context vector is `(batch_size, hidden_size)`, where `hidden_size` is the number of units in the decoder's hidden state[5].

Applications

The Bahdanau attention mechanism has been used in various applications, including neural machine translation, where it helps in generating more accurate translations by focusing on the most relevant parts of the input sentence[3][4].
Citations:
[1] https://www.tensorflow.org/addons/api_docs/python/tfa/seq2seq/BahdanauAttention
[2] https://www.kaggle.com/code/kmkarakaya/encoder-decoder-with-bahdanau-luong-attention
[3] https://machinelearningmastery.com/the-bahdanau-attention-mechanism/
[4] https://pyimagesearch.com/2022/08/22/neural-machine-translation-with-bahdanaus-attention-using-tensorflow-and-keras/
[5] https://stackoverflow.com/questions/60031693/context-vector-shape-using-bahdanau-attention