MATLABunetlayers函数代码

unetlayers函数是一个自定义的MATLAB函数，用于构建U-Net模型的卷积层。以下是一个可能的实现：

function layers = unetlayers(numClasses, imageSize)
% Create the layers for a U-Net convolutional neural network.
%   numClasses - number of output classes
%   imageSize - size of input images in the format [height, width, channels]

% Define the encoder portion of the network
encoder = [
    imageInputLayer(imageSize, 'Name', 'input')
    convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1_1')
    reluLayer('Name', 'relu1_1')
    convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv1_2')
    reluLayer('Name', 'relu1_2')
    maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool1')
    
    convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv2_1')
    reluLayer('Name', 'relu2_1')
    convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv2_2')
    reluLayer('Name', 'relu2_2')
    maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool2')
    
    convolution2dLayer(3, 256, 'Padding', 'same', 'Name', 'conv3_1')
    reluLayer('Name', 'relu3_1')
    convolution2dLayer(3, 256, 'Padding', 'same', 'Name', 'conv3_2')
    reluLayer('Name', 'relu3_2')
    maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool3')
    
    convolution2dLayer(3, 512, 'Padding', 'same', 'Name', 'conv4_1')
    reluLayer('Name', 'relu4_1')
    convolution2dLayer(3, 512, 'Padding', 'same', 'Name', 'conv4_2')
    reluLayer('Name', 'relu4_2')
    maxPooling2dLayer(2, 'Stride', 2, 'Name', 'pool4')
    
    convolution2dLayer(3, 1024, 'Padding', 'same', 'Name', 'conv5_1')
    reluLayer('Name', 'relu5_1')
    convolution2dLayer(3, 1024, 'Padding', 'same', 'Name', 'conv5_2')
    reluLayer('Name', 'relu5_2')
];

% Define the decoder portion of the network
decoder = [
    transposedConv2dLayer(2, 512, 'Stride', 2, 'Name', 'transconv6')
    concatenationLayer(3, 'Name', 'concat6')
    convolution2dLayer(3, 512, 'Padding', 'same', 'Name', 'conv6_1')
    reluLayer('Name', 'relu6_1')
    convolution2dLayer(3, 512, 'Padding', 'same', 'Name', 'conv6_2')
    reluLayer('Name', 'relu6_2')
    
    transposedConv2dLayer(2, 256, 'Stride', 2, 'Name', 'transconv7')
    concatenationLayer(3, 'Name', 'concat7')
    convolution2dLayer(3, 256, 'Padding', 'same', 'Name', 'conv7_1')
    reluLayer('Name', 'relu7_1')
    convolution2dLayer(3, 256, 'Padding', 'same', 'Name', 'conv7_2')
    reluLayer('Name', 'relu7_2')
    
    transposedConv2dLayer(2, 128, 'Stride', 2, 'Name', 'transconv8')
    concatenationLayer(3, 'Name', 'concat8')
    convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv8_1')
    reluLayer('Name', 'relu8_1')
    convolution2dLayer(3, 128, 'Padding', 'same', 'Name', 'conv8_2')
    reluLayer('Name', 'relu8_2')
    
    transposedConv2dLayer(2, 64, 'Stride', 2, 'Name', 'transconv9')
    concatenationLayer(3, 'Name', 'concat9')
    convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv9_1')
    reluLayer('Name', 'relu9_1')
    convolution2dLayer(3, 64, 'Padding', 'same', 'Name', 'conv9_2')
    reluLayer('Name', 'relu9_2')
    
    convolution2dLayer(1, numClasses, 'Name', 'conv10')
    softmaxLayer('Name', 'softmax')
    pixelClassificationLayer('Classes', categorical({'background', 'object'}), 'Name', 'output')
];

% Combine the encoder and decoder into a single network
layers = [
    encoder
    decoder
];

该函数定义了一个包含卷积、池化、反卷积和连接层的U-Net模型。函数参数`numClasses`和`imageSize`分别指定输出类别数和输入图像的尺寸。其中，`categorical({'background', 'object'})`指定了两个分类标签，用于语义分割任务。