CNN回归预测的MATLAB代码
时间: 2023-08-12 08:07:47 浏览: 41
以下是一个简单的CNN回归预测的MATLAB代码示例:
```
% 加载数据
load('data.mat', 'inputs', 'targets');
% 分割数据
[trainInd, valInd, testInd] = dividerand(size(inputs,2), 0.6, 0.2, 0.2);
trainInputs = inputs(:, trainInd);
trainTargets = targets(:, trainInd);
valInputs = inputs(:, valInd);
valTargets = targets(:, valInd);
testInputs = inputs(:, testInd);
testTargets = targets(:, testInd);
% 设置CNN参数
inputSize = size(inputs,1);
numHiddenUnits = 100;
filterSize = [5 5];
numFilters = 20;
outputSize = size(targets,1);
layers = [ ...
imageInputLayer([inputSize 1 1])
convolution2dLayer(filterSize, numFilters, 'Padding', 2)
reluLayer
maxPooling2dLayer(2, 'Stride', 2)
fullyConnectedLayer(numHiddenUnits)
reluLayer
fullyConnectedLayer(outputSize)
regressionLayer];
% 设置训练选项
options = trainingOptions('sgdm', ...
'MaxEpochs', 20, ...
'MiniBatchSize', 64, ...
'ValidationData', {valInputs, valTargets}, ...
'ValidationFrequency', 10, ...
'Verbose', false, ...
'Plots', 'training-progress');
% 训练CNN
net = trainNetwork(trainInputs, trainTargets, layers, options);
% 测试CNN
predictedTargets = predict(net, testInputs);
% 计算误差
mse = mean((testTargets - predictedTargets).^2);
fprintf('测试集均方误差: %f\n', mse);
```
请注意,此代码仅为示例代码,应根据具体数据和问题进行修改和优化。