帮我用matlab编写CNN-LSTM-Attention模型

好的，下面是一个使用Matlab实现CNN-LSTM-Attention模型的示例代码。在这个示例中，我们将使用MNIST手写数字识别数据集来演示模型的实现。

首先，我们需要加载并预处理数据集：

[trainImages, trainLabels, testImages, testLabels] = helperMNISTData.load();

% 将图像调整为相同的大小
imageSize = [28 28 1];
trainImages = imresize(trainImages, imageSize);
testImages = imresize(testImages, imageSize);

% 将标签转换为分类向量
numClasses = numel(unique(trainLabels));
trainLabels = categorical(trainLabels);
testLabels = categorical(testLabels);

% 创建ImageDatastore对象
trainData = imageDatastore(trainImages, trainLabels);
testData = imageDatastore(testImages, testLabels);

然后，我们定义CNN模型：

% 创建卷积神经网络模型
layers = [
    imageInputLayer(imageSize)

    convolution2dLayer(3,16,'Padding','same')
    batchNormalizationLayer
    reluLayer

    maxPooling2dLayer(2,'Stride',2)

    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)
];

% 连接卷积神经网络图层
lgraph = layerGraph(layers);

接下来，我们定义LSTM模型：

% 创建长短期记忆网络模型
numHiddenUnits = 64;
lstm = lstmLayer(numHiddenUnits,'OutputMode','last');

% 添加LSTM层到图层图中
lstmLayer = sequenceInputLayer(numHiddenUnits);
lstmLayer.Name = 'lstmLayer';
lgraph = addLayers(lgraph,lstmLayer);
lgraph = connectLayers(lgraph,'pooling2dLayer','lstmLayer');

lgraph = addLayers(lgraph,lstm);
lgraph = connectLayers(lgraph,'lstmLayer','lstm');

最后，我们定义Attention模型：

% 创建注意力模型
attention = attentionLayer('AttentionWeightLearnRateFactor',0.2);

% 添加注意力层到图层图中
attentionLayer = sequenceInputLayer(numHiddenUnits);
attentionLayer.Name = 'attentionLayer';
lgraph = addLayers(lgraph,attentionLayer);
lgraph = connectLayers(lgraph,'lstm', 'attentionLayer');

lgraph = addLayers(lgraph,attention);
lgraph = connectLayers(lgraph,'attentionLayer','attention');
lgraph = connectLayers(lgraph,'lstm','attention','SequenceOutput',true);

现在，我们可以训练模型并进行测试：

% 设置训练参数
miniBatchSize = 128;
numEpochs = 10;
learnRate = 1e-3;
gradientDecayFactor = 0.9;
squaredGradientDecayFactor = 0.999;
l2RegularizationFactor = 1e-5;

options = trainingOptions('adam', ...
    'MiniBatchSize',miniBatchSize, ...
    'MaxEpochs',numEpochs, ...
    'InitialLearnRate',learnRate, ...
    'GradientDecayFactor',gradientDecayFactor, ...
    'SquaredGradientDecayFactor',squaredGradientDecayFactor, ...
    'L2Regularization',l2RegularizationFactor, ...
    'Shuffle','every-epoch', ...
    'ValidationData',testData, ...
    'ValidationFrequency',30, ...
    'Plots','training-progress', ...
    'Verbose',false);

% 训练模型
net = trainNetwork(trainData,lgraph,options);

% 测试模型
predictions = classify(net,testData);
accuracy = sum(predictions == testLabels)/numel(testLabels);
fprintf("Accuracy: %.2f%%\n", accuracy*100);

这就是一个使用Matlab实现CNN-LSTM-Attention模型的简单示例。您可以根据您的需求进行修改和扩展，例如更改模型架构、调整训练参数等。请注意，实现复杂的深度学习模型需要大量的计算资源和时间。