matlab lstm分类模型搭建代码

下面是一个使用LSTM网络进行分类的Matlab代码示例：

% 加载数据
load fisheriris;
X = meas.';
Y = ind2vec(grp.');

% 分割数据集
[trainInd,~,testInd] = dividerand(size(X,2),0.6,0,0.4);
XTrain = X(:,trainInd);
YTrain = Y(:,trainInd);
XTest = X(:,testInd);
YTest = Y(:,testInd);

% 构建LSTM网络
numFeatures = size(XTrain,1);
numClasses = size(YTrain,1);
numHiddenUnits = 100;
layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,'OutputMode','last')
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];

% 定义训练选项
maxEpochs = 100;
miniBatchSize = 16;
validationFrequency = floor(numel(YTrain)/miniBatchSize);
options = trainingOptions('adam', ...
    'ExecutionEnvironment','auto', ...
    'GradientThreshold',1, ...
    'MaxEpochs',maxEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'SequenceLength','longest', ...
    'Shuffle','every-epoch', ...
    'ValidationData',{XTest,YTest}, ...
    'ValidationFrequency',validationFrequency, ...
    'Verbose',false, ...
    'Plots','training-progress');

% 训练网络
net = trainNetwork(XTrain,YTrain,layers,options);

% 使用网络进行预测
YPred = classify(net,XTest);

% 计算分类准确率
accuracy = sum(YPred == categorical(vec2ind(YTest)))/numel(YTest);
fprintf('Test accuracy: %0.2f%%\n',accuracy*100);

这个示例使用Fisheriris数据集进行分类，构建了一个LSTM网络，然后使用trainNetwork函数进行训练，最后计算了分类准确率。你可以根据自己的数据集和问题修改代码中的参数和网络结构。