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How to create an attention layer for deep learning networks?

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Can you please let me know how to create an attention layer for deep learning classification networks? I have a simple 1D convolutional neural network and I want to create a layer that focuses on special parts of a signal as an attention mechanism.
I have been working on the wav2vec MATLAB code recently, but the best I found is the multi-head attention manual calculation. Can we make it as a layer to be included for the trainNetwork function?
For example, this is my current network, which is from this example:
numFilters = 128;
filterSize = 5;
dropoutFactor = 0.005;
numBlocks = 4;
layer = sequenceInputLayer(numFeatures,Normalization="zerocenter",Name="input");
lgraph = layerGraph(layer);
outputName = layer.Name;
for i = 1:numBlocks
dilationFactor = 2^(i-1);
layers = [
% Add and connect layers.
lgraph = addLayers(lgraph,layers);
lgraph = connectLayers(lgraph,outputName,"conv1_"+i);
% Skip connection.
if i == 1
% Include convolution in first skip connection.
layer = convolution1dLayer(1,numFilters,Name="convSkip");
lgraph = addLayers(lgraph,layer);
lgraph = connectLayers(lgraph,outputName,"convSkip");
lgraph = connectLayers(lgraph,"convSkip","add_" + i + "/in2");
lgraph = connectLayers(lgraph,outputName,"add_" + i + "/in2");
% Update layer output name.
outputName = "add_" + i;
layers = [
lgraph = addLayers(lgraph,layers);
lgraph = connectLayers(lgraph,outputName,"gapl");
I appreciate your help!
  15 comentarios
mohd akmal masud
mohd akmal masud el 20 de Oct. de 2023
% Define the attention layer
attentionLayer = attentionLayer('AttentionSize', attentionSize);
% Create the rest of your deep learning model
layers = [
convolution2dLayer(3, 64, 'Padding', 'same')
% Create the deep learning network
net = layerGraph(layers);
% Visualize the network
健 李
健 李 el 6 de Nov. de 2023
Dear Mohanad
Thank you very much for sharing your code. I tried running it in Matlab R2023a, but Matlab prompted: The function or variable 'attentionSize' is not recognized I don't know why this error occurred, is it related to my version?

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Respuesta aceptada

Samuel Somuyiwa
Samuel Somuyiwa el 24 de Jun. de 2022
You can create an attention layer as a custom layer, similar to spatialDropoutLayer in the example you are using in your current network, and include it in the network that you are passing to trainNetwork. This doc page explains how to create a custom layer. You can use the Intermediate Layer Template in that doc page to start with.
If you uncomment the nnet.layer.Formattable in that template, you can copy, and modify where necessary, the code from the multihead attention function in wav2vec-2.0 on File Exchange and use it in the predict method of your custom layer. Note that you do not need to implement a backward method in this case. This doc page provides more information on how to create custom layers with formattable inputs.
If you have R2022b prerelease, you can use the (new) attention function instead of the multihead attention function in wav2vec-2.0 on File Exchange to implement the predict method of your layer. Type help attention on the command line to see the help text for the function.
  9 comentarios
jie huang
jie huang el 12 de En. de 2023
Hi, I would like to ask you what to fill in the function layer = initialize(layer,layout) inside the custom layer template if I want to update the learnable parameters of the multi-headed attention mechanism in the layer?
Also, why is the input dimension different from the output dimension in the MATLAB documentation of version 2022b of the multihead self-attention mechanism?
Thank you for your answer.
MAHMOUD EID el 14 de Mzo. de 2023
Hi, can you provide an example of using attention layer in deep network for classifcation tasks using Matlab 2022 ?

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Más respuestas (2)

kollikonda Ashok kumar
kollikonda Ashok kumar el 3 de Mayo de 2023
I too want to know how to use attention layer in deep network for classification tasks..

Ayush Modi
Ayush Modi el 14 de Mzo. de 2024
Refer to the following MathWorks documentation as an example on how to use custom Attention layer for classification task:
Hope this helps you get started!


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