How to create an attention layer for deep learning networks?

 Hello,

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 = [
        convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal",Name="conv1_"+i)
        layerNormalizationLayer
        spatialDropoutLayer(dropoutFactor)
        convolution1dLayer(filterSize,numFilters,DilationFactor=dilationFactor,Padding="causal")
        layerNormalizationLayer
        reluLayer
        spatialDropoutLayer(dropoutFactor)
        additionLayer(2,Name="add_"+i)];
    % 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");
    else
        lgraph = connectLayers(lgraph,outputName,"add_" + i + "/in2");
    end
    
    % Update layer output name.
    outputName = "add_" + i;
end
layers = [
    globalMaxPooling1dLayer("Name",'gapl')
    fullyConnectedLayer(numClasses,Name="fc")
    softmaxLayer
    classificationLayer('Classes',unique(Y_train),'ClassWeights',weights)];
lgraph = addLayers(lgraph,layers);
lgraph = connectLayers(lgraph,outputName,"gapl");

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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.

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https://www.matlabsolutions.com/resources/how-to-create-an-attention-layer-for-deep-learning-networks-.php