matlab unet代码
时间: 2023-07-02 15:03:52 浏览: 71
以下是一个简单的 MATLAB UNet 代码示例:
```matlab
% 加载数据
load('images.mat');
load('labels.mat');
% 定义输入图像大小
inputSize = size(images{1});
% 定义网络结构
inputLayer = imageInputLayer(inputSize);
conv1 = convolution2dLayer(3, 64, 'Padding', 'same');
bn1 = batchNormalizationLayer;
relu1 = reluLayer;
conv2 = convolution2dLayer(3, 64, 'Padding', 'same');
bn2 = batchNormalizationLayer;
relu2 = reluLayer;
pool1 = maxPooling2dLayer(2, 'Stride', 2, 'Padding', 'same');
conv3 = convolution2dLayer(3, 128, 'Padding', 'same');
bn3 = batchNormalizationLayer;
relu3 = reluLayer;
conv4 = convolution2dLayer(3, 128, 'Padding', 'same');
bn4 = batchNormalizationLayer;
relu4 = reluLayer;
pool2 = maxPooling2dLayer(2, 'Stride', 2, 'Padding', 'same');
conv5 = convolution2dLayer(3, 256, 'Padding', 'same');
bn5 = batchNormalizationLayer;
relu5 = reluLayer;
conv6 = convolution2dLayer(3, 256, 'Padding', 'same');
bn6 = batchNormalizationLayer;
relu6 = reluLayer;
pool3 = maxPooling2dLayer(2, 'Stride', 2, 'Padding', 'same');
conv7 = convolution2dLayer(3, 512, 'Padding', 'same');
bn7 = batchNormalizationLayer;
relu7 = reluLayer;
conv8 = convolution2dLayer(3, 512, 'Padding', 'same');
bn8 = batchNormalizationLayer;
relu8 = reluLayer;
up1 = transposedConv2dLayer(2, 512, 'Stride', 2, 'Cropping', 'same');
concat1 = concatenationLayer(3);
conv9 = convolution2dLayer(3, 256, 'Padding', 'same');
bn9 = batchNormalizationLayer;
relu9 = reluLayer;
conv10 = convolution2dLayer(3, 256, 'Padding', 'same');
bn10 = batchNormalizationLayer;
relu10 = reluLayer;
up2 = transposedConv2dLayer(2, 256, 'Stride', 2, 'Cropping', 'same');
concat2 = concatenationLayer(3);
conv11 = convolution2dLayer(3, 128, 'Padding', 'same');
bn11 = batchNormalizationLayer;
relu11 = reluLayer;
conv12 = convolution2dLayer(3, 128, 'Padding', 'same');
bn12 = batchNormalizationLayer;
relu12 = reluLayer;
up3 = transposedConv2dLayer(2, 128, 'Stride', 2, 'Cropping', 'same');
concat3 = concatenationLayer(3);
conv13 = convolution2dLayer(3, 64, 'Padding', 'same');
bn13 = batchNormalizationLayer;
relu13 = reluLayer;
conv14 = convolution2dLayer(3, 64, 'Padding', 'same');
bn14 = batchNormalizationLayer;
relu14 = reluLayer;
conv15 = convolution2dLayer(1, 2, 'Padding', 'same');
softmax = softmaxLayer;
outputLayer = pixelClassificationLayer;
% 组合网络层
layers = [
inputLayer
conv1
bn1
relu1
conv2
bn2
relu2
pool1
conv3
bn3
relu3
conv4
bn4
relu4
pool2
conv5
bn5
relu5
conv6
bn6
relu6
pool3
conv7
bn7
relu7
conv8
bn8
relu8
up1
concat1
conv9
bn9
relu9
conv10
bn10
relu10
up2
concat2
conv11
bn11
relu11
conv12
bn12
relu12
up3
concat3
conv13
bn13
relu13
conv14
bn14
relu14
conv15
softmax
outputLayer];
% 定义训练选项
options = trainingOptions('adam', ...
'MaxEpochs', 50, ...
'MiniBatchSize', 20, ...
'InitialLearnRate', 1e-3, ...
'Shuffle', 'every-epoch', ...
'Verbose', true);
% 训练网络
net = trainNetwork(images, categorical(labels), layers, options);
% 保存模型
save('unet.mat', 'net');
```
这个代码示例定义了一个 UNet 网络结构,并使用 MATLAB 的 `trainNetwork` 函数进行训练。你需要准备好你自己的输入图像和标签数据集,并根据情况更改输入图像大小和训练选项。