matlab搭建残差快

在MATLAB中搭建残差网络可以通过使用深度学习工具箱中的函数来实现。以下是一个简单的示例代码，演示了如何在MATLAB中搭建一个残差块：

layers = [
    imageInputLayer([32 32 3])
    convolution2dLayer(3, 64, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3, 64, 'Padding', 'same')
    batchNormalizationLayer
    additionLayer
    reluLayer
    convolution2dLayer(3, 64, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3, 64, 'Padding', 'same')
    batchNormalizationLayer
    additionLayer
    reluLayer
    convolution2dLayer(3, 128, 'Padding', 'same', 'Stride', 2)
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3, 128, 'Padding', 'same')
    batchNormalizationLayer
    convolution2dLayer(1, 128, 'Padding', 'same', 'Stride', 2)
    batchNormalizationLayer
    additionLayer
    reluLayer
    convolution2dLayer(3, 128, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(3, 128, 'Padding', 'same')
    batchNormalizationLayer
    additionLayer
    reluLayer
    fullyConnectedLayer(10)
    softmaxLayer
    classificationLayer];

options = trainingOptions('sgdm', 'MaxEpochs', 10, 'InitialLearnRate', 0.001);

net = trainNetwork(trainImages, trainLabels, layers, options);

这个示例代码搭建了一个简单的残差网络，用于图像分类任务。你可以根据自己的需求修改网络结构和参数。