自回归模型MATLAB事例
时间: 2023-05-23 11:04:48 浏览: 72
A:在MATLAB中,可以使用Deep Learning Toolbox中的transformer函数实现自回归模型。下面是一个简单的示例代码:
```matlab
% 导入数据
data = load('data.mat');
inputData = data.inputData;
outputData = data.outputData;
% 定义网络参数
numHeads = 8;
hiddenSize = 256;
numEncoderLayers = 2;
numDecoderLayers = 2;
dropoutProb = 0.1;
sequenceLength = size(inputData, 2);
srcVocabSize = max(inputData(:));
tgtVocabSize = max(outputData(:));
% 定义encoder和decoder
encoder = transformerEncoder(hiddenSize, numHeads, dropoutProb, numEncoderLayers);
decoder = transformerDecoder(hiddenSize, numHeads, dropoutProb, numDecoderLayers);
% 定义自回归模型
model = transformerModel(encoder, decoder, srcVocabSize, tgtVocabSize, hiddenSize, ...
'SequenceLength', sequenceLength);
% 训练模型
miniBatchSize = 128;
numEpochs = 50;
learnRate = 0.001;
gradientThreshold = 1;
validationFrequency = floor(size(inputData, 1) / miniBatchSize);
options = trainingOptions('adam', ...
'ExecutionEnvironment', 'auto', ...
'MiniBatchSize', miniBatchSize, ...
'GradientThreshold', gradientThreshold, ...
'ValidationData', {inputData, outputData}, ...
'ValidationFrequency', validationFrequency, ...
'Plots', 'training-progress', ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.1, ...
'LearnRateDropPeriod', 15, ...
'InitialLearnRate', learnRate, ...
'MaxEpochs', numEpochs);
model = train(model, inputData, outputData, options);
% 使用训练好的模型进行预测
testData = load('testdata.mat');
testInput = testData.testInput;
testOutput = testData.testOutput;
predictedOutput = predict(model, testInput, 'SequenceLength', size(testInput, 2));
```
在这个示例代码中,我们首先导入了训练和测试数据,并定义了网络参数。然后,我们使用transformerEncoder和transformerDecoder定义了encoder和decoder,并将它们传递给了transformerModel函数以定义自回归模型。接着,我们使用trainingOptions函数定义了训练选项,并使用train函数训练了模型。最后,我们使用predict函数对测试数据进行了预测。