MATLAB基于贝叶斯优化的LSTM多输入预测代码

以下是基于贝叶斯优化的LSTM多输入预测的MATLAB代码示例：

% Load data
load multivariate_time_series_data.mat

% Define input and output variables
inputs = data(:,1:3);
output = data(:,4);

% Define LSTM network architecture
numFeatures = size(inputs,2);
numResponses = size(output,2);
numHiddenUnits = 200;
layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits,'OutputMode','sequence')
    fullyConnectedLayer(numResponses)
    regressionLayer];

% Define hyperparameters for Bayesian optimization
vars = [
    optimizableVariable('MiniBatchSize',[10 100],'Type','integer')
    optimizableVariable('LearnRate',[1e-5 1e-2],'Transform','log')
    optimizableVariable('GradientThreshold',[1e-5 1],'Transform','log')
    optimizableVariable('L2Regularization',[1e-10 1e-3],'Transform','log')
    optimizableVariable('SequenceLength',[24 168],'Type','integer')];

% Define objective function for Bayesian optimization
minfun = @(hyperparams)lstm_multivariate_predict(inputs,output,hyperparams,layers);

% Perform Bayesian optimization
results = bayesopt(minfun,vars,'MaxObj',10,'IsObjectiveDeterministic',true,'UseParallel',true);

% Print optimal hyperparameters
results.XAtMinObjective

% Train LSTM network with optimal hyperparameters
opts = trainingOptions('adam', ...
    'MiniBatchSize',results.XAtMinObjective.MiniBatchSize, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRate',results.XAtMinObjective.LearnRate, ...
    'GradientThreshold',results.XAtMinObjective.GradientThreshold, ...
    'L2Regularization',results.XAtMinObjective.L2Regularization, ...
    'MaxEpochs',200, ...
    'Shuffle','never', ...
    'Verbose',0);

net = trainNetwork(inputs',output',layers,opts);

% Make predictions on test data
testInputs = testData(:,1:3)';
testOutput = testData(:,4)';
testPredictions = predict(net,testInputs);

其中，`lstm_multivariate_predict` 函数的代码如下：

function rmse = lstm_multivariate_predict(inputs,output,hyperparams,layers)

% Split data into training and validation sets
numTimeStepsTrain = floor(0.9*size(inputs,1));
XTrain = inputs(1:numTimeStepsTrain,:);
YTrain = output(1:numTimeStepsTrain,:);
XValidation = inputs(numTimeStepsTrain+1:end,:);
YValidation = output(numTimeStepsTrain+1:end,:);

% Define LSTM network
net = network;
net.numLayers = numel(layers);
for i=1:numel(layers)
    net.layers{i} = layers(i);
end
net = configure(net,XTrain',YTrain');

% Train LSTM network
opts = trainingOptions('adam', ...
    'MiniBatchSize',hyperparams.MiniBatchSize, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRate',hyperparams.LearnRate, ...
    'GradientThreshold',hyperparams.GradientThreshold, ...
    'L2Regularization',hyperparams.L2Regularization, ...
    'MaxEpochs',200, ...
    'Shuffle','never', ...
    'Verbose',0);
[net,tr] = trainNetwork(XTrain',YTrain',net.Layers,opts);

% Make predictions on validation set
YPred = predict(net,XValidation')';

% Calculate RMSE on validation set
rmse = sqrt(mean((YPred - YValidation).^2));
end

在上面的代码中，我们首先加载多变量时间序列数据，然后定义输入和输出变量。接下来，我们定义LSTM网络架构，并指定超参数以进行贝叶斯优化。然后，我们定义一个目标函数，该函数使用输入、输出数据和超参数训练LSTM网络，并返回在验证集上的RMSE。然后，我们使用 `bayesopt` 函数执行贝叶斯优化，并训练LSTM网络使用最优超参数。最后，我们使用训练好的LSTM网络对测试数据进行预测并计算RMSE。