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。