matlab实现GAN

GAN是一种用于训练生成器网络和判别器网络的深度学习算法，可以用来生成新样本。MATLAB提供了深度学习工具箱和图形用户界面（Deep Network Designer），可以帮助您轻松构建、训练和验证GAN模型。

下面是一个简单的GAN模型的MATLAB实现：

首先，导入必要的工具箱：

addpath('工具箱路径');

% 导入必要的工具箱
% 如果您尚未安装工具箱，则可以从此处下载安装
% https://cn.mathworks.com/products/deep-learning.html
% https://cn.mathworks.com/products/deep-learning/designer.html

接下来，构建生成器和判别器网络：

% 构建生成器网络
generator = [
    imageInputLayer([100 1 1],'Normalization','none')

    fullyConnectedLayer(128*7*7)
    reshapeLayer([128 7 7])

    transposedConv2dLayer(4,256,'Stride',2,'Cropping','same')
    batchNormalizationLayer
    reluLayer

    transposedConv2dLayer(4,128,'Stride',2,'Cropping','same')
    batchNormalizationLayer
    reluLayer

    transposedConv2dLayer(4,64,'Stride',2,'Cropping','same')
    batchNormalizationLayer
    reluLayer

    transposedConv2dLayer(4,1,'Stride',2,'Cropping','same')
    tanhLayer
];

% 构建判别器网络
discriminator = [
    imageInputLayer([28 28 1])

    convolution2dLayer(3,64,'Padding','same')
    batchNormalizationLayer
    leakyReluLayer(0.2)

    maxPooling2dLayer(2,'Stride',2)

    convolution2dLayer(3,128,'Padding','same')
    batchNormalizationLayer
    leakyReluLayer(0.2)

    maxPooling2dLayer(2,'Stride',2)

    convolution2dLayer(3,256,'Padding','same')
    batchNormalizationLayer
    leakyReluLayer(0.2)

    maxPooling2dLayer(2,'Stride',2)

    convolution2dLayer(3,512,'Padding','same')
    batchNormalizationLayer
    leakyReluLayer(0.2)

    fullyConnectedLayer(1)
    sigmoidLayer
];

然后，将生成器和判别器网络合并成一个GAN模型：

% 将生成器和判别器网络合并为一个GAN模型
gan = ganModel(generator, discriminator);

最后，进行训练：

% 加载训练数据集
[XTrain, ~] = digitTrain4DArrayData;

% 定义GAN模型参数
opts = trainingOptions('adam', ...
    'MiniBatchSize', 128, ...
    'MaxEpochs', 100, ...
    'Verbose', false, ...
    'Plots', 'training-progress');

% 训练GAN模型
[gan,info] = trainNetwork(XTrain, gan, opts);