生成对抗网络matlab2021a

生成对抗网络（GAN）是一种深度学习模型，用于生成与训练数据类似的新数据。在MATLAB 2021a中，可以使用Deep Learning Toolbox中的函数来实现GAN模型。以下是一个简单的GAN示例：

% 导入数据
load digitTrainSet

% 定义生成器网络
numLatentInputs = 100;
numGeneratorFilters = 64 * 4;
generatorFilterSize = 4;
generator = [
    imageInputLayer([1 1 numLatentInputs],'Normalization','none','Name','in')
    projectAndReshapeLayer(numGeneratorFilters*7*7,'Name','proj')
    transposedConv2dLayer(generatorFilterSize,generatorFilterSize,numGeneratorFilters*2,'Name','tconv1')
    batchNormalizationLayer('Name','bn1')
    reluLayer('Name','relu1')
    transposedConv2dLayer(generatorFilterSize,generatorFilterSize,numGeneratorFilters,'Stride',2,'Cropping',1,'Name','tconv2')
    batchNormalizationLayer('Name','bn2')
    reluLayer('Name','relu2')
    transposedConv2dLayer(generatorFilterSize,generatorFilterSize,1,'Stride',2,'Cropping',1,'Name','tconv3')
    tanhLayer('Name','tanh')];

% 定义判别器网络
numDiscriminatorFilters = 64;
discriminatorFilterSize = 4;
discriminator = [
    imageInputLayer([28 28 1],'Name','in')
    convolution2dLayer(discriminatorFilterSize,discriminatorFilterSize,numDiscriminatorFilters,'Stride',2,'Padding',1,'Name','conv1')
    leakyReluLayer(0.2,'Name','lrelu1')
    convolution2dLayer(discriminatorFilterSize,discriminatorFilterSize,numDiscriminatorFilters*2,'Stride',2,'Padding',1,'Name','conv2')
    batchNormalizationLayer('Name','bn2')
    leakyReluLayer(0.2,'Name','lrelu2')
    convolution2dLayer(discriminatorFilterSize,discriminatorFilterSize,numDiscriminatorFilters*4,'Stride',2,'Padding',1,'Name','conv3')
    batchNormalizationLayer('Name','bn3')
    leakyReluLayer(0.2,'Name','lrelu3')
    convolution2dLayer(discriminatorFilterSize,discriminatorFilterSize,numDiscriminatorFilters*8,'Stride',2,'Padding',1,'Name','conv4')
    batchNormalizationLayer('Name','bn4')
    leakyReluLayer(0.2,'Name','lrelu4')
    fullyConnectedLayer(1,'Name','fc')
    sigmoidLayer('Name','sigmoid')];

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

% 训练GAN网络
options = trainingOptions('adam', ...
    'MaxEpochs',100, ...
    'MiniBatchSize',128, ...
    'Verbose',false, ...
    'Plots','training-progress');
[trainedGan,trainInfo] = trainNetwork(digitTrainSet,gan,options);

% 生成新数据
noise = randn(1,1,numLatentInputs,16);
generatedImages = predict(trainedGan.Generator,noise);
montage(generatedImages)

上述代码中，我们首先导入了一个手写数字数据集，然后定义了一个生成器网络和一个判别器网络。接着，我们使用ganNetwork函数将这两个网络组合成一个GAN网络。最后，我们使用trainNetwork函数训练GAN网络，并使用predict函数生成新数据。