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");

I appreciate your help!

regards,

Mohanad

[XT]

Hi,Mohanad

I want to add the multi-head attention layer in my model. But I dont't know how to achieve it as a custom layer. Would you mind sharing the code of your attention layer with me? I highly appreciate your support.

[健 李]

Hi,Mohanad

I also need to customize the attention layer code. Can you share your code with me? This is my email [e-mail]. Thank you very much.

[xiao xiao]

Hi,Mohanad

Would you mind sharing the code of your attention layer with me? I highly appreciate your support.

This is my email [e-mail]. Thank you very much.

[Muhammad]

Hello Mohnas,

Can you share me the code of your attention layer? I highly appreciate your support.

Email: [e-mail]. Thank you

[Hansion]

Hello Mohanad,

Could you please share the code of your attention layer with me? I highly appreciate your support.

This is my email: [e-mail]. Thank you very much.

[Lin Sui]

Hi,Mohanad

Would you mind sharing the code of your attention layer with me? I highly appreciate your support.

This is my email [e-mail]. Thank you very much.

[jie huang]

Hello,Mohanad

I'm also having the same problem as you. I'm having trouble creating the multihead attention layer. Would you mind sharing your code of the attention layer with me? I would highly appreciate your help.

This means a lot to me. This is my email [e-mail] you very much.

[tao zhang]

Hello,Mohanad

I've been working on attention mechanics recently,Would you mind sharing your code of the attention layer with me? I would highly appreciate your help.

This is my email [e-mail]. Thank you very much.

[석우]

Hello,Mohanad

Can you share me the code of your attention layer? I highly appreciate your support.

This is my email: [e-mail]

Thank you so much

[lee]

Hello,Mohanad

I'm currently learning about attention mechanisms. Would you mind sharing the code of your attention layer with me? I highly appreciate your support.

This is my email: [e-mail]. Thank you very much.

[kollikonda Ashok kumar]

Hi Mohanad. Can you share the code for Attention network along with pretrained networks for image classification....My mail [e-mail]..

Thanks in Advance for your help

[weikang]

Hello Mohanad

I'm also having the same problem as you.Could you please share the code of your attention layer with me? I highly appreciate your support.

This is my email: [e-mail]. Thank you very much.

[pingwei gu]

Hello Mohanad

I'm also having the same problem as you.Could you please share the code of your attention layer with me? I highly appreciate your support.

This is my email: [e-mail]. Thank you very much.

[mohd akmal masud]

Dearv@Mohanad Alkhodari@Mohanad Alkhodari@XT@XT@健 李@Muhammad

% Define the attention layer
attentionLayer = attentionLayer('AttentionSize', attentionSize);
% Create the rest of your deep learning model
layers = [
    imageInputLayer([inputImageSize])
    convolution2dLayer(3, 64, 'Padding', 'same')
    reluLayer
    attentionLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer
    ];
% Create the deep learning network
net = layerGraph(layers);
% Visualize the network
plot(net);

[健 李]

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?

Answer 1

[Samuel Somuyiwa]

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.

[Mohanad Alkhodari]

Hi Samuel,

I have been working on this for the past week and I managed to create my own positional encoder and transformer encoder based on the wav2vec model.

But I am not sure if the performance I get during training (attached) shows that the layers are built correctly.

My implementation of the multihead attention of wav2vec returned same results as R2022b attention function as well.

Do you mind checking my network and layers design over a Zoom meeting? that would be really helpful!

regards,

Mohanad

[Samuel Somuyiwa]

No, I don't mind checking your network over a Zoom meeting. Any time between 12:00 - 14:00 Mon - Fri next week works for me.

[Mohanad Alkhodari]

Many thanks for this!

I sent you an email!

regards,

Mohanad

[Mohanad Alkhodari]

I have sent you an email on your icl email.

How about Monday at 12:00PM London time?

Please let me know.

regards,

Mohanad

[Mohanad Alkhodari]

Please let me know about this! I appreciate it so much!

regards,

Mohanad

[Samuel Somuyiwa]

Hi Mohanad,

I did not receive your email. Can you email me using my Mathworks email address?

[Mohanad Alkhodari]

Done!

[jie huang]

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]

Hi, can you provide an example of using attention layer in deep network for classifcation tasks using Matlab 2022 ?

Answer 2

[kollikonda Ashok kumar]

I too want to know how to use attention layer in deep network for classification tasks..

Answer 3

[Ayush Modi]

Hi Mohanad Alkhodari, kollikonda Ashok kumar

Refer to the following MathWorks documentation as an example on how to use custom Attention layer for classification task:

1. https://www.mathworks.com/help/deeplearning/ug/image-captioning-using-attention.html
2. https://www.mathworks.com/help/deeplearning/ug/sequence-to-sequence-translation-using-attention.html

Hope this helps you get started!

Answer 4

[Ali El romeh]

To add an attention mechanism to your 1D convolutional neural network in MATLAB, you can create a custom attention layer and integrate it into your existing network architecture

here is an example of how you can implement a simple attention layer and incorporate it into your network

Step 1: define the custom attention Layr

Create a custom attention layer class that will compute the attention weights and apply them to the input signl

classdef AttentionLayer < nnet.layer.Layer

    properties (Learnable)
        Weights
        Bias
    end

    methods
        function layer = AttentionLayer(name)
            % create an attention layerr
            layer.Name = name;
            layer.Description = "Attention Layer";

            % initialize the weights and bias
            layer.Weights = randn([1, 1]);
            layer.Bias = randn([1, 1]);
        end

        function Z = predict(layer, X)
            % forword pass through attention laye
            
            % compute attention scores
            scores = tanh(layer.Weights * X + layer.Bias);
            
            % apply softmax to get attention weights
            attentionWeights = softmax(scores, 2);
            
            % multiply input by attention weights
            Z = attentionWeights .* X;
        end
    end
end

step 2: add thecustom attention layer to ur network

Modify your existing network to include the custom attention layrr

numFilters = 128;
filterSize = 5;
dropoutFactor = 0.005;
numBlocks = 4;
numFeatures = size(X_train, 2);  % assuming X_train is your input data
numClasses = numel(unique(Y_train));  % assuming Y_train is your target data
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 lyerss
    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

% Add the custom attention layer.
attentionLayer = AttentionLayer('attention');
lgraph = addLayers(lgraph, attentionLayer);
lgraph = connectLayers(lgraph, outputName, "attention");

layers = [
    globalMaxPooling1dLayer("Name", 'gapl')
    fullyConnectedLayer(numClasses, Name="fc")
    softmaxLayer
    classificationLayer('Classes', unique(Y_train), 'ClassWeights', weights)];

lgraph = addLayers(lgraph, layers);
lgraph = connectLayers(lgraph, "attention", "gapl");        

step 3: train the network

you can now train the network using the trainNetwork function with your training data

options = trainingOptions('adam', ...
    'MaxEpochs', 30, ...
    'MiniBatchSize', 64, ...
    'InitialLearnRate', 1e-3, ...
    'Verbose', false, ...
    'Plots', 'training-progress');

net = trainNetwork(X_train, Y_train, lgraph, options);

thiss code defins a custom attention layer, integrates it into your existing network architecture, and trains the networkk

the attention layer applies a simple attention mechanism, which can be further customized and improved depending on your specific requirements and data characteristics