UNET MATLAB代码

下面是一个简单的UNET MATLAB代码示例，用于图像分割任务：

% 加载训练数据和标签
load('trainData.mat');
load('trainLabels.mat');

% 定义UNET模型
inputLayer = imageInputLayer([256,256,3]);
conv1 = convolution2dLayer(3,64,'Padding', 'same', 'Name', 'conv1');
relu1 = reluLayer('Name', 'relu1');
conv2 = convolution2dLayer(3,64,'Padding', 'same', 'Name', 'conv2');
relu2 = reluLayer('Name', 'relu2');
pool1 = maxPooling2dLayer(2,'Stride',2,'Name', 'pool1');

conv3 = convolution2dLayer(3,128,'Padding', 'same', 'Name', 'conv3');
relu3 = reluLayer('Name', 'relu3');
conv4 = convolution2dLayer(3,128,'Padding', 'same', 'Name', 'conv4');
relu4 = reluLayer('Name', 'relu4');
pool2 = maxPooling2dLayer(2,'Stride',2,'Name', 'pool2');

conv5 = convolution2dLayer(3,256,'Padding', 'same', 'Name', 'conv5');
relu5 = reluLayer('Name', 'relu5');
conv6 = convolution2dLayer(3,256,'Padding', 'same', 'Name', 'conv6');
relu6 = reluLayer('Name', 'relu6');
pool3 = maxPooling2dLayer(2,'Stride',2,'Name', 'pool3');

conv7 = convolution2dLayer(3,512,'Padding', 'same', 'Name', 'conv7');
relu7 = reluLayer('Name', 'relu7');
conv8 = convolution2dLayer(3,512,'Padding', 'same', 'Name', 'conv8');
relu8 = reluLayer('Name', 'relu8');

% 上采样
trans1 = transposedConv2dLayer(2,512,'Stride',2,'Name', 'trans1');
concat1 = concatenationLayer(4, 'Name', 'concat1');
conv9 = convolution2dLayer(3,256,'Padding', 'same', 'Name', 'conv9');
relu9 = reluLayer('Name', 'relu9');
conv10 = convolution2dLayer(3,256,'Padding', 'same', 'Name', 'conv10');
relu10 = reluLayer('Name', 'relu10');

trans2 = transposedConv2dLayer(2,256,'Stride',2,'Name', 'trans2');
concat2 = concatenationLayer(4, 'Name', 'concat2');
conv11 = convolution2dLayer(3,128,'Padding', 'same', 'Name', 'conv11');
relu11 = reluLayer('Name', 'relu11');
conv12 = convolution2dLayer(3,128,'Padding', 'same', 'Name', 'conv12');
relu12 = reluLayer('Name', 'relu12');

trans3 = transposedConv2dLayer(2,128,'Stride',2,'Name', 'trans3');
concat3 = concatenationLayer(4, 'Name', 'concat3');
conv13 = convolution2dLayer(3,64,'Padding', 'same', 'Name', 'conv13');
relu13 = reluLayer('Name', 'relu13');
conv14 = convolution2dLayer(3,64,'Padding', 'same', 'Name', 'conv14');
relu14 = reluLayer('Name', 'relu14');

conv15 = convolution2dLayer(1,2,'Name', 'conv15');
softmax1 = softmaxLayer('Name', 'softmax1');
outputLayer = pixelClassificationLayer('Name', 'outputLayer');

layers = [inputLayer, conv1, relu1, conv2, relu2, pool1, conv3, relu3, conv4, relu4, pool2, conv5, relu5, conv6, relu6, pool3, conv7, relu7, conv8, relu8, trans1, concat1, conv9, relu9, conv10, relu10, trans2, concat2, conv11, relu11, conv12, relu12, trans3, concat3, conv13, relu13, conv14, relu14, conv15, softmax1, outputLayer];

% 定义训练选项
options = trainingOptions('adam', ...
    'MaxEpochs', 50, ...
    'MiniBatchSize', 16, ...
    'Shuffle', 'every-epoch', ...
    'Verbose', true, ...
    'Plots', 'training-progress');

% 训练UNET模型
unet = trainNetwork(trainData, categorical(trainLabels), layers, options);

% 加载测试数据
load('testData.mat');

% 对测试数据进行分割
predictedLabels = semanticseg(testData, unet);

% 显示分割结果
imshow(predictedLabels);

请注意，这只是一个简单示例，可能需要根据您的具体任务进行修改和调整。