Yes, TensorFlow provides pre-built functions for implementing Bahdanau attention. The `tfa.seq2seq.BahdanauAttention` class in TensorFlow Addons implements Bahdanau-style (additive) attention. This class can be used to create a Bahdanau attention object, which can then be used in the `tfa.seq2seq.AttentionWrapper` class to apply the Bahdanau attention mechanism to the output of an RNN encoder[1].
Additionally, TensorFlow also includes the `tf.keras.layers.AdditiveAttention` layer, which is another implementation of Bahdanau-style attention. This layer can be used to calculate attention scores and then apply them to the value tensor to produce the output[4].
Here is an example of how to use the `tfa.seq2seq.BahdanauAttention` class:
python
import tensorflow as tf
from tensorflow_addons.seq2seq import BahdanauAttention
attnUnits = 128
attention = BahdanauAttention(attnUnits)
And here is an example of how to use the `tf.keras.layers.AdditiveAttention` layer:
python
import tensorflow as tf
from tensorflow.keras.layers import AdditiveAttention
query = tf.keras.Input(shape=(None, 10))
values = tf.keras.Input(shape=(None, 10))
attention = AdditiveAttention()
output = attention([query, values])
model = tf.keras.Model(inputs=[query, values], outputs=output)
These pre-built functions simplify the process of implementing Bahdanau attention in TensorFlow and Keras, making it easier to integrate into your neural machine translation models.Citations:
[1] https://www.tensorflow.org/addons/api_docs/python/tfa/seq2seq/BahdanauAttention
[2] https://stackoverflow.com/questions/64072600/calculating-attention-scores-in-bahdanau-attention-in-tensorflow-using-decoder-h
[3] https://github.com/topics/bahdanau-attention
[4] https://www.tensorflow.org/api_docs/python/tf/keras/layers/AdditiveAttention
[5] https://pyimagesearch.com/2022/08/22/neural-machine-translation-with-bahdanaus-attention-using-tensorflow-and-keras/