卷积神经网络中加入5折交叉验证matlab代码

下面是一个简单的 MATLAB 代码示例，用于在卷积神经网络中使用 5 折交叉验证：

% Load data
load('data.mat')

% Set up cross-validation
k = 5;
cv = cvpartition(size(data, 4), 'KFold', k);

% Train and test the network for each fold
for i = 1:k
    % Split data into training and testing sets
    trainIdx = cv.training(i);
    testIdx = cv.test(i);
    trainData = data(:, :, :, trainIdx);
    testData = data(:, :, :, testIdx);
    trainLabels = labels(trainIdx);
    testLabels = labels(testIdx);

    % Create and train the network
    net = alexnet;
    [net, ~] = trainNetwork(trainData, trainLabels, layers);

    % Test the network
    predLabels = classify(net, testData);
    accuracy(i) = mean(predLabels == testLabels);
end

% Calculate overall accuracy
meanAccuracy = mean(accuracy);

在这个示例中，我们假设数据和标签存储在名为“data.mat”的 MATLAB 文件中。我们使用“cvpartition”函数生成 5 折交叉验证分区，然后在每个分区上训练和测试一个卷积神经网络。最后，我们计算每个分区的准确性，并计算平均准确性作为模型的性能指标。