To customize Bahdanau attention in TensorFlow, you can use the `tfa.seq2seq.BahdanauAttention` class from TensorFlow Addons. Here are the key parameters you can customize:
1. `units`: The depth of the query mechanism. This is the number of units in the attention mechanism's output.
2. `memory`: The memory to query; usually the output of an RNN encoder. This tensor should be shaped `[batch_size, max_time, ...]`.
3. **`memory_sequence_length``: (Optional) Sequence lengths for the batch entries in memory. If provided, the memory tensor rows are masked with zeros for values past the respective sequence lengths.
4. `normalize`: Python boolean. Whether to normalize the energy term.
5. `probability_fn`: (Optional) String, the name of function to convert the attention score to probabilities. The default is `softmax` which is `tf.nn.softmax`. Other options include `hardmax`, which is `tf.nn.softmax` within this module. Any other value will result in a validation error.
6. `kernel_initializer`: (Optional) The name of the initializer for the attention kernel.
7. `dtype`: The data type for the query and memory layers of the attention mechanism.
8. `name`: Name to use when creating ops.
9. `kwargs`**: Dictionary that contains other common arguments for layer creation.
Here is an example of how to use these parameters:
python
attnUnits = 128
attention = BahdanauAttention(
units=attnUnits,
memory=encoderOutput,
memory_sequence_length=encoderSequenceLength,
normalize=True,
probability_fn='softmax',
kernel_initializer='glorot_uniform',
dtype=tf.float32,
name='BahdanauAttention'
)
These parameters allow you to customize the Bahdanau attention mechanism to suit your specific neural machine translation model requirements[1][2][5].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://d2l.ai/chapter_attention-mechanisms-and-transformers/bahdanau-attention.html
[5] https://pyimagesearch.com/2022/08/22/neural-machine-translation-with-bahdanaus-attention-using-tensorflow-and-keras/