matlab利用GAN生成图像

GAN（Generative Adversarial Network）是一种生成式模型，能够生成高质量的图像。在MATLAB中，可以使用Deep Learning Toolbox中的GAN函数来实现GAN的图像生成。

以下是一个简单的示例代码，它使用GAN生成手写数字图像：

% 导入手写数字数据集
digitDatasetPath = fullfile(matlabroot,'toolbox','nnet','nndemos', ...
    'nndatasets','DigitDataset');
digitData = imageDatastore(digitDatasetPath, ...
    'IncludeSubfolders',true,'LabelSource','foldernames');

% 定义生成器网络
generator = [
    imageInputLayer([1 1 100],'Normalization','none','Name','in')
    fullyConnectedLayer(7*7*256,'Name','fc')
    reluLayer('Name','relu1')
    transposedConv2dLayer(6,256,'Stride',2,'Cropping',2,'Name','tconv1')
    batchNormalizationLayer('Name','bn1')
    reluLayer('Name','relu2')
    transposedConv2dLayer(6,128,'Stride',2,'Cropping',2,'Name','tconv2')
    batchNormalizationLayer('Name','bn2')
    reluLayer('Name','relu3')
    transposedConv2dLayer(6,64,'Stride',2,'Cropping',2,'Name','tconv3')
    batchNormalizationLayer('Name','bn3')
    reluLayer('Name','relu4')
    transposedConv2dLayer(6,1,'Stride',2,'Cropping',2,'Name','tconv4')
    tanhLayer('Name','tanh')];

% 定义判别器网络
discriminator = [
    imageInputLayer([28 28 1],'Name','in')
    convolution2dLayer(3,64,'Stride',2,'Padding',1,'Name','conv1')
    leakyReluLayer(0.2,'Name','lrelu1')
    convolution2dLayer(3,128,'Stride',2,'Padding',1,'Name','conv2')
    batchNormalizationLayer('Name','bn2')
    leakyReluLayer(0.2,'Name','lrelu2')
    convolution2dLayer(3,256,'Stride',2,'Padding',1,'Name','conv3')
    batchNormalizationLayer('Name','bn3')
    leakyReluLayer(0.2,'Name','lrelu3')
    convolution2dLayer(3,512,'Stride',2,'Padding',1,'Name','conv4')
    batchNormalizationLayer('Name','bn4')
    leakyReluLayer(0.2,'Name','lrelu4')
    fullyConnectedLayer(1,'Name','fc')
    sigmoidLayer('Name','sigmoid')];

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

% 训练GAN
numEpochs = 50;
miniBatchSize = 128;
plots = "training-progress";
learnRate = 0.0002;
gradientDecayFactor = 0.5;
squaredGradientDecayFactor = 0.999;
executionEnvironment = "auto";
options = trainingOptions('adam', ...
    'MaxEpochs',numEpochs, ...
    'MiniBatchSize',miniBatchSize, ...
    'Plots',plots, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropFactor',0.1, ...
    'LearnRateDropPeriod',30, ...
    'InitialLearnRate',learnRate, ...
    'GradientDecayFactor',gradientDecayFactor, ...
    'SquaredGradientDecayFactor',squaredGradientDecayFactor, ...
    'ExecutionEnvironment',executionEnvironment);

% 开始训练
[trainedGAN,trainingInfo] = trainNetwork(digitData,gan,options);

训练完成后，可以使用以下代码生成手写数字图像：

% 生成手写数字图像
numImages = 16;
noiseVector = randn(1,1,100,numImages);
generatedImages = predict(trainedGAN.Generator,noiseVector);
imshow(imtile(generatedImages));

这将生成16张手写数字图像。