matlab中cnn图片裂缝发育规律计算出图代码

以下是使用MATLAB实现CNN图片裂缝发育规律计算的示例代码：

% 加载数据集
imds = imageDatastore('dataset_path', 'IncludeSubfolders', true, 'LabelSource', 'foldernames');

% 划分训练集和测试集
[trainImgs, testImgs] = splitEachLabel(imds, 0.8, 'randomized');

% 定义卷积神经网络结构
layers = [
    imageInputLayer([224 224 3])
    
    convolution2dLayer(3, 64, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 128, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 256, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    convolution2dLayer(3, 256, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 512, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    convolution2dLayer(3, 512, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    convolution2dLayer(3, 512, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    convolution2dLayer(3, 512, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    
    maxPooling2dLayer(2, 'Stride', 2)
    
    fullyConnectedLayer(4096)
    reluLayer
    
    dropoutLayer(0.5)
    
    fullyConnectedLayer(4096)
    reluLayer
    
    dropoutLayer(0.5)
    
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer
];

% 定义训练参数
options = trainingOptions('sgdm', ...
    'InitialLearnRate', 0.001, ...
    'MaxEpochs', 20, ...
    'MiniBatchSize', 32, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', testImgs, ...
    'ValidationFrequency', 10, ...
    'Verbose', false, ...
    'Plots', 'training-progress');

% 训练卷积神经网络
net = trainNetwork(trainImgs, layers, options);

% 对测试集进行分类预测
predictedLabels = classify(net, testImgs);

% 计算分类准确率
accuracy = mean(predictedLabels == testImgs.Labels);
disp(['Classification accuracy: ', num2str(accuracy)]);

这里使用了一个基本的卷积神经网络结构，并使用了SGD优化器进行训练。你可以根据自己的需求，对网络结构和训练参数进行调整和优化。