MATLAB UNET代码
时间: 2023-12-20 15:57:34 浏览: 24
以下是一个简单的 MATLAB UNet 代码示例,用于图像分割:
```matlab
% 导入数据
imds = imageDatastore('path/to/images');
% 定义 UNet 网络结构
inputSize = [256 256 3];
numClasses = 2;
layers = [
imageInputLayer(inputSize)
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, 512, 'Stride', 2)
concatenationLayer(3)
convolution2dLayer(3, 256, 'Padding', 'same')
batchNormalizationLayer
reluLayer
transposedConv2dLayer(2, 256, 'Stride', 2)
concatenationLayer(3)
convolution2dLayer(3, 128, 'Padding', 'same')
batchNormalizationLayer
reluLayer
transposedConv2dLayer(2, 128, 'Stride', 2)
concatenationLayer(3)
convolution2dLayer(3, 64, 'Padding', 'same')
batchNormalizationLayer
reluLayer
convolution2dLayer(1, numClasses)
softmaxLayer
pixelClassificationLayer
];
% 设置训练选项
options = trainingOptions('adam', ...
'MaxEpochs', 20, ...
'MiniBatchSize', 8, ...
'InitialLearnRate', 1e-4, ...
'Shuffle', 'every-epoch', ...
'Verbose', true, ...
'Plots', 'training-progress');
% 训练网络
net = trainNetwork(imds, layers, options);
```
请注意,此代码仅用于演示目的。您需要根据自己的数据集和任务进行修改和调整。