UNET MATLAB 池化代码
时间: 2024-03-02 22:42:07 浏览: 29
以下是一个简单的UNet MATLAB池化代码示例:
```matlab
%% 定义 UNet 模型
input = imageInputLayer([256 256 3],'Name','InputLayer');
conv1 = convolution2dLayer(3,64,'Padding','same','Name','Conv1_1');
relu1 = reluLayer('Name','ReLU1_1');
conv2 = convolution2dLayer(3,64,'Padding','same','Name','Conv1_2');
relu2 = reluLayer('Name','ReLU1_2');
pool1 = maxPooling2dLayer(2,'Stride',2,'Name','Pool1');
% 编码器
conv3 = convolution2dLayer(3,128,'Padding','same','Name','Conv2_1');
relu3 = reluLayer('Name','ReLU2_1');
conv4 = convolution2dLayer(3,128,'Padding','same','Name','Conv2_2');
relu4 = reluLayer('Name','ReLU2_2');
pool2 = maxPooling2dLayer(2,'Stride',2,'Name','Pool2');
conv5 = convolution2dLayer(3,256,'Padding','same','Name','Conv3_1');
relu5 = reluLayer('Name','ReLU3_1');
conv6 = convolution2dLayer(3,256,'Padding','same','Name','Conv3_2');
relu6 = reluLayer('Name','ReLU3_2');
pool3 = maxPooling2dLayer(2,'Stride',2,'Name','Pool3');
conv7 = convolution2dLayer(3,512,'Padding','same','Name','Conv4_1');
relu7 = reluLayer('Name','ReLU4_1');
conv8 = convolution2dLayer(3,512,'Padding','same','Name','Conv4_2');
relu8 = reluLayer('Name','ReLU4_2');
% 解码器
trans1 = transposedConv2dLayer(2,512,'Stride',2,'Name','TransConv1');
concat1 = concatenationLayer(3,{'Conv4_2','TransConv1'},'Name','Concat1');
conv9 = convolution2dLayer(3,256,'Padding','same','Name','Conv5_1');
relu9 = reluLayer('Name','ReLU5_1');
conv10 = convolution2dLayer(3,256,'Padding','same','Name','Conv5_2');
relu10 = reluLayer('Name','ReLU5_2');
trans2 = transposedConv2dLayer(2,256,'Stride',2,'Name','TransConv2');
concat2 = concatenationLayer(3,{'Conv3_2','TransConv2'},'Name','Concat2');
conv11 = convolution2dLayer(3,128,'Padding','same','Name','Conv6_1');
relu11 = reluLayer('Name','ReLU6_1');
conv12 = convolution2dLayer(3,128,'Padding','same','Name','Conv6_2');
relu12 = reluLayer('Name','ReLU6_2');
trans3 = transposedConv2dLayer(2,128,'Stride',2,'Name','TransConv3');
concat3 = concatenationLayer(3,{'Conv2_2','TransConv3'},'Name','Concat3');
conv13 = convolution2dLayer(3,64,'Padding','same','Name','Conv7_1');
relu13 = reluLayer('Name','ReLU7_1');
conv14 = convolution2dLayer(3,64,'Padding','same','Name','Conv7_2');
relu14 = reluLayer('Name','ReLU7_2');
output = convolution2dLayer(1,1,'Padding','same','Name','OutputLayer');
softmax = softmaxLayer('Name','Softmax');
class = classificationLayer('Name','ClassLayer');
% 建立网络
layers = [input
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
output
softmax
class];
% 定义训练参数
options = trainingOptions('adam', ...
'MaxEpochs', 25, ...
'InitialLearnRate', 1e-3, ...
'Shuffle', 'every-epoch', ...
'Verbose', true, ...
'Plots', 'training-progress');
% 训练模型
net = trainNetwork(XTrain,YTrain,layers,options);
```
在这个示例中,我们定义了一个包含了池化操作的UNet模型,并使用了`maxPooling2dLayer`来实现池化操作。我们还定义了训练参数和训练模型的代码。需要注意的是,这个示例是一个简单的UNet模型,实际应用中可能需要更复杂的结构和更多的层来实现更好的性能。
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