UNET跳跃连接MATLAB代码

以下是一个简单的UNET跳跃连接的MATLAB代码示例：

% 定义UNET网络结构
unet_layers = [
    imageInputLayer([256 256 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
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(3, 512, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(2, 256, 'Stride', 2)
    concatenationLayer(3)
    convolution2dLayer(3, 256, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(2, 128, 'Stride', 2)
    concatenationLayer(3)
    convolution2dLayer(3, 128, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(2, 64, 'Stride', 2)
    concatenationLayer(3)
    convolution2dLayer(3, 64, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    convolution2dLayer(1, 1)
    regressionLayer
];

% 训练UNET网络
options = trainingOptions('adam', ...
    'InitialLearnRate', 1e-3, ...
    'MaxEpochs', 100, ...
    'MiniBatchSize', 16, ...
    'Shuffle', 'every-epoch', ...
    'ValidationData', {valX, valY}, ...
    'ValidationFrequency', 10, ...
    'Plots', 'training-progress');

unet_net = trainNetwork(trainX, trainY, unet_layers, options);

在UNET网络中，跳跃连接使用`concatenationLayer`层来连接编码器和解码器的对应层。在上述代码中，我们使用了3个跳跃连接来连接编码器中的第2、3、4个卷积层和解码器中对应的转置卷积层之前的卷积层。在训练UNET网络时，我们使用了Adam优化器和交叉熵损失函数。