MATLAB创建RDN网络代码
时间: 2023-10-07 13:12:23 浏览: 44
以下是MATLAB中创建RDN网络的代码示例:
```matlab
% 导入数据
data = imageDatastore('path/to/images');
% 设置超参数
scale = 2; % 放大倍数
numResBlocks = 16; % Residual Dense Block(RDB)中的残差块数量
numFilters = 64; % 每个RDB中的滤波器数量
numChannels = 3; % 输入和输出图像的通道数
% 创建RDN网络
inputLayer = imageInputLayer([NaN NaN numChannels]);
convLayer = convolution2dLayer(3, numFilters, 'Padding', 1);
reluLayer = reluLayer();
rdbLayers = [];
for i = 1:numResBlocks
rdbLayers = [rdbLayers, residualDenseBlock(numFilters)];
end
concatLayer = concatenationLayer(3);
outConvLayer1 = convolution2dLayer(3, numFilters, 'Padding', 1);
outConvLayer2 = convolution2dLayer(3, numFilters*scale^2, 'Padding', 1);
pixelShuffleLayer = pixelShuffleLayer(scale);
outConvLayer3 = convolution2dLayer(3, numChannels, 'Padding', 1);
outputLayer = regressionLayer();
layers = [inputLayer;
convLayer;
reluLayer;
rdbLayers;
concatLayer;
outConvLayer1;
reluLayer;
outConvLayer2;
pixelShuffleLayer;
outConvLayer3;
outputLayer];
% 训练RDN网络
options = trainingOptions('adam', ...
'MaxEpochs', 100, ...
'InitialLearnRate', 1e-4, ...
'MiniBatchSize', 16, ...
'Shuffle', 'every-epoch', ...
'Verbose', true, ...
'Plots', 'training-progress');
net = trainNetwork(data, layers, options);
```
这是一个简单的RDN网络,由输入层、卷积层、Residual Dense Blocks、拼接层、输出层等组成。其中,Residual Dense Blocks是RDN网络的核心部分,由多个残差块和密集连接组成。训练时使用adam优化器,最大迭代次数为100,学习率为1e-4,每次训练使用16张图像。