Example of using attention layer in deep learning

I am wondering how the attention layer can be implemented on the deep network.

Can you share an example using R2022B?

I have looked to this link : https://se.mathworks.com/matlabcentral/answers/1743390-how-to-create-an-attention-layer-for-deep-learning-networks?s_tid=ta_ans_results

but could not implement the layer.

[MAHMOUD EID]

@Varun Sai Alaparthi

@Samuel Somuyiwa

Answer 1

[Rohit]

Hi MAHMOUD,

I understand that you want to add an attention layer in your deep learning model.

As an example, you can look up this MathWorks documentation link: https://in.mathworks.com/help/deeplearning/ug/image-captioning-using-attention.html

In this example, the attention layer is created and used in the custom “modelDecoder” function, which decodes the output from the encoder network and generate the caption for the image.

The “modelDecoder” function defines a custom GRU cell that incorporates attention. Inside the function, the attention layer is created using the custom “attention” function, which calculates the context vector and the attention weights using “Bahdanau” attention. The output of the attention layer is then concatenated with the input to the GRU cell, which allows the cell to focus on different parts of the image while generating the caption.

Similar approach can be found in this example - https://in.mathworks.com/help/deeplearning/ug/sequence-to-sequence-translation-using-attention.html

I hope that above examples helps for you.