matlab定义gan网络

在 MATLAB 中，您可以使用深度学习工具箱中的函数来定义 GAN 网络。以下是一个简单的示例：

首先，导入必要的函数和数据集：

% 导入深度学习工具箱
addpath('C:\Program Files\MATLAB\R2021a\toolbox\deeplearning');

% 加载 MNIST 数据集
[XTrain, YTrain] = digitTrain4DArrayData;

接下来，定义生成器和判别器网络：

% 定义生成器网络
generator = [
    imageInputLayer([1 1 100],'Normalization','none','Name','in')
    fullyConnectedLayer(7*7*256,'Name','fc')
    batchNormalizationLayer('Name','bn')
    reluLayer('Name','relu')
    transposedConv2dLayer(4,256,'Stride',2,'Padding','same','Name','tconv1')
    batchNormalizationLayer('Name','bn1')
    reluLayer('Name','relu1')
    transposedConv2dLayer(4,128,'Stride',2,'Padding','same','Name','tconv2')
    batchNormalizationLayer('Name','bn2')
    reluLayer('Name','relu2')
    transposedConv2dLayer(4,1,'Stride',2,'Padding','same','Name','tconv3')
    tanhLayer('Name','tanh')];

% 定义判别器网络
discriminator = [
    imageInputLayer([28 28 1],'Name','in')
    convolution2dLayer(4,64,'Stride',2,'Padding','same','Name','conv1')
    leakyReluLayer(0.2,'Name','lrelu1')
    convolution2dLayer(4,128,'Stride',2,'Padding','same','Name','conv2')
    batchNormalizationLayer('Name','bn')
    leakyReluLayer(0.2,'Name','lrelu2')
    convolution2dLayer(4,256,'Stride',2,'Padding','same','Name','conv3')
    batchNormalizationLayer('Name','bn1')
    leakyReluLayer(0.2,'Name','lrelu3')
    fullyConnectedLayer(1,'Name','fc')
    sigmoidLayer('Name','sigmoid')];

然后，使用 `gan` 函数将生成器和判别器组合成 GAN 网络：

% 定义 GAN 网络
gan = gan(generator,discriminator);

最后，使用 `trainNetwork` 函数训练 GAN 网络：

% 训练 GAN 网络
options = trainingOptions('adam','MaxEpochs',100,'MiniBatchSize',128,'Plots','training-progress');
[trainedGAN,stats] = trainNetwork(XTrain, gan, options);