matlab人工智能生成对抗网络代码

以下是一个简单的生成对抗网络（GAN）的 MATLAB 代码：

% 生成器网络
generator = [
    imageInputLayer([100 1 1],'Name','in')
    fullyConnectedLayer(128*7*7)
    reshapeLayer([7 7 128])
    transposedConv2dLayer(4,128,'Stride',2,'Cropping',1,'Name','tconv1')
    batchNormalizationLayer('Name','bn1')
    reluLayer('Name','relu1')
    transposedConv2dLayer(4,64,'Stride',2,'Cropping',1,'Name','tconv2')
    batchNormalizationLayer('Name','bn2')
    reluLayer('Name','relu2')
    transposedConv2dLayer(4,1,'Stride',2,'Cropping',1,'Name','tconv3')
    tanhLayer('Name','tanh')];

% 判别器网络
discriminator = [
    imageInputLayer([28 28 1],'Name','in')
    convolution2dLayer(4,32,'Stride',2,'Padding',1,'Name','conv1')
    leakyReluLayer(0.2,'Name','lrelu1')
    convolution2dLayer(4,64,'Stride',2,'Padding',1,'Name','conv2')
    batchNormalizationLayer('Name','bn2')
    leakyReluLayer(0.2,'Name','lrelu2')
    fullyConnectedLayer(1,'Name','fc')
    sigmoidLayer('Name','sigmoid')];

% GAN 网络
gan = [
    generator
    discriminator];

% 生成器输入和噪声
genInputSize = 100;
genInput = dlarray(randn(genInputSize,1,'single'),'SSCB');

% 判别器输入和标签
discInputSize = [28 28 1];
discInput = dlarray(zeros([discInputSize 128],'single'),'SSCB');
discLabels = dlarray([ones(128,1,'single'); zeros(128,1,'single')],'CB');

% 优化器选项
learnRate = 0.0002;
gradientDecayFactor = 0.5;
squaredGradientDecayFactor = 0.999;
l2RegularizationFactor = 0.0001;
beta1 = 0.5;
beta2 = 0.999;

% 优化器
genOptimizer = adamOpti(learnRate,beta1,beta2,gradientDecayFactor,squaredGradientDecayFactor,l2RegularizationFactor);
discOptimizer = adamOpti(learnRate,beta1,beta2,gradientDecayFactor,squaredGradientDecayFactor,l2RegularizationFactor);

% 训练循环
numEpochs = 50;
miniBatchSize = 128;
numIterations = floor(numel(trainX)/miniBatchSize);
ZValidation = dlarray(randn(genInputSize,1,1,miniBatchSize,'single'),'SSCB');
generatorValidation = dlfeval(@predict,generator,ZValidation);

for epoch = 1:numEpochs
    tic
    shuffle = randperm(numel(trainX));
    trainX = trainX(shuffle);
    for i = 1:numIterations
        idx = (i-1)*miniBatchSize+1:i*miniBatchSize;
        XBatch = trainX(idx);
        % 更新判别器
        dLoss = dlfeval(@discriminatorLoss,generator,discriminator,XBatch,discInput,discLabels);
        [discriminator, discLoss] = dlfeval(@adamUpdate, discriminator, discOptimizer, dLoss);
        % 更新生成器
        gLoss = dlfeval(@generatorLoss,generator,discriminator,genInput,discLabels);
        [generator, genLoss] = dlfeval(@adamUpdate, generator, genOptimizer, gLoss);
    end
    % 打印损失
    disp("Epoch: " + epoch + ", Discriminator Loss: " + discLoss + ", Generator Loss: " + genLoss);
    % 验证生成器
    generatorValidation = dlfeval(@predict,generator,ZValidation);
    figure;
    for img = 1:miniBatchSize
        subplot(8,16,img);
        imshow(generatorValidation(:,:,:,img));
    end
    toc
end

这个代码使用了 MATLAB 的深度学习工具箱中的函数来定义生成器和判别器网络，以及训练和优化 GAN 网络。请注意，这个代码是一个简单的示例，你可能需要根据你的数据集和任务来调整网络结构和超参数。