attentionLayer函数，matlab代码

以下是一个简单的attentionLayer函数的Matlab代码示例：

function [weightedContext, attentionWeights] = attentionLayer(context, query, key, value)
% context: 输入上下文，大小为 [batchSize, contextLength, hiddenSize]
% query: 查询向量，大小为 [batchSize, queryLength, hiddenSize]
% key: 键向量，大小为 [batchSize, keyLength, hiddenSize]
% value: 值向量，大小为 [batchSize, valueLength, hiddenSize]

% 计算注意力分数
scores = query * permute(key, [1, 3, 2]); % 大小为 [batchSize, queryLength, keyLength]
attentionWeights = softmax(scores, 3); % 在最后一个维度上进行softmax，大小为 [batchSize, queryLength, keyLength]

% 对值向量进行加权求和
weightedContext = attentionWeights * value; % 大小为 [batchSize, queryLength, hiddenSize]
end

注意，这只是一个简单的实现示例，具体实现可能会因应用场景和需求而有所不同。

相关问题

matlab self attention layer

在 MATLAB 中实现 Self-Attention Layer，可以按照以下步骤进行：

1. 定义一个自注意力层的类，该类应该继承自 matlab.mixin.CustomDisplay 类，以便在命令窗口中显示自定义信息。

2. 在类的构造函数中设置自注意力层的参数，包括输入向量的维度、自注意力头的数量、隐藏层的大小等。

3. 实现自注意力层的前向传播函数，该函数应该接受输入向量并返回自注意力层的输出。在前向传播函数中，需要实现自注意力机制，包括计算注意力权重、加权求和等操作。

4. 实现自注意力层的反向传播函数，该函数应该接收反向传播的梯度并返回输入向量的梯度。在反向传播函数中，需要计算注意力权重的导数、加权求和的导数等。

下面是一个简单的 MATLAB 自注意力层的示例代码：

classdef SelfAttentionLayer < matlab.mixin.CustomDisplay
    properties
        input_dim
        num_heads
        hidden_dim
        dropout_rate
        query_weights
        key_weights
        value_weights
    end
    
    methods
        function obj = SelfAttentionLayer(input_dim, num_heads, hidden_dim, dropout_rate)
            obj.input_dim = input_dim;
            obj.num_heads = num_heads;
            obj.hidden_dim = hidden_dim;
            obj.dropout_rate = dropout_rate;
            obj.query_weights = randn(hidden_dim, input_dim);
            obj.key_weights = randn(hidden_dim, input_dim);
            obj.value_weights = randn(hidden_dim, input_dim);
        end
        
        function output = forward(obj, input)
            batch_size = size(input, 1);
            query = input * obj.query_weights';
            key = input * obj.key_weights';
            value = input * obj.value_weights';
            query = reshape(query, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
            key = reshape(key, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
            value = reshape(value, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
            attention_weights = softmax(query * permute(key, [1, 3, 2]) / sqrt(obj.hidden_dim/obj.num_heads), 3);
            attention_weights = dropout(attention_weights, obj.dropout_rate);
            output = reshape(attention_weights * value, [batch_size, obj.hidden_dim]);
        end
        
        function input_gradient = backward(obj, output_gradient, input)
            batch_size = size(input, 1);
            query = input * obj.query_weights';
            key = input * obj.key_weights';
            value = input * obj.value_weights';
            query = reshape(query, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
            key = reshape(key, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
            value = reshape(value, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
            attention_weights = softmax(query * permute(key, [1, 3, 2]) / sqrt(obj.hidden_dim/obj.num_heads), 3);
            attention_weights = dropout(attention_weights, obj.dropout_rate);
            output_gradient = reshape(output_gradient, [batch_size, obj.num_heads, obj.hidden_dim/obj.num_heads]);
            value_gradient = attention_weights' * output_gradient;
            attention_weights_gradient = output_gradient * permute(value, [1, 3, 2]);
            attention_weights_gradient = attention_weights_gradient .* (attention_weights .* (1-attention_weights));
            attention_weights_gradient = dropout(attention_weights_gradient, obj.dropout_rate);
            query_gradient = attention_weights_gradient * key;
            key_gradient = permute(query, [1, 3, 2]) * attention_weights_gradient;
            input_gradient = (query_gradient + key_gradient + value_gradient) * obj.query_weights;
        end
        
        function displayScalarObject(obj)
            disp(['SelfAttentionLayer with input_dim = ', num2str(obj.input_dim), ', num_heads = ', num2str(obj.num_heads), ', hidden_dim = ', num2str(obj.hidden_dim), ', dropout_rate = ', num2str(obj.dropout_rate)]);
        end
    end
end

该代码使用了随机初始化的权重矩阵，实现了自注意力层的前向传播和反向传播函数，并添加了自定义信息的显示功能。

cnn-lstm-attention序列预测的matlab代码

以下是使用CNN-LSTM-Attention模型进行序列预测的MATLAB代码示例：

% 加载数据
load data.mat

% 数据预处理
XTrain = permute(XTrain,[2 3 4 1]);
XTest = permute(XTest,[2 3 4 1]);
YTrain = categorical(YTrain);
YTest = categorical(YTest);

% 定义CNN层
layers = [
    imageInputLayer([32 32 3])
    convolution2dLayer(3,32,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,64,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    convolution2dLayer(3,128,'Padding','same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2,'Stride',2)
    ];

% 定义LSTM层
inputSize = 128;
numHiddenUnits = 64;
numClasses = 10;
lstmLayers = [
    sequenceInputLayer(inputSize)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer
    ];

% 定义Attention层
attention = attentionLayer(numHiddenUnits);

% 将CNN和LSTM层连接起来
layers = [
    layers
    sequenceFoldingLayer('Name','fold')
    lstmLayers
    sequenceUnfoldingLayer('Name','unfold')
    attention
    ];

% 定义训练选项
options = trainingOptions('adam', ...
    'MaxEpochs',30, ...
    'MiniBatchSize',64, ...
    'Plots','training-progress');

% 训练模型
net = trainNetwork(XTrain,YTrain,layers,options);

% 测试模型
YPred = classify(net,XTest);
accuracy = sum(YPred == YTest)/numel(YTest);
disp(['Test accuracy: ' num2str(accuracy)])

需要注意的是，上述代码中用到的`attentionLayer`函数需要自行实现。你可以参考以下代码：

classdef attentionLayer < nnet.layer.Layer
    properties
        HiddenSize
        AttentionWeights
    end
    methods
        function layer = attentionLayer(hiddenSize,name)
            layer.HiddenSize = hiddenSize;
            layer.Name = name;
            layer.AttentionWeights = layer.initWeights(hiddenSize);
        end
        function weights = initWeights(~,hiddenSize)
            weights = randn(hiddenSize,1);
        end
        function Z = predict(layer,X)
            W = layer.AttentionWeights;
            Z = tanh(W'*X);
        end
        function [dLdX,dLdW] = backward(layer,X,~,dLdZ,~)
            W = layer.AttentionWeights;
            Y = layer.predict(X);
            dLdY = dLdZ.*(1-Y.^2);
            dLdW = dLdY*X';
            dLdX = W*dLdY;
        end
    end
end

这个实现只是一个简单的示例，你可以根据自己的需求进行修改和扩展。