matlab对抗网络代码

以下是一个简单的MATLAB实现对抗网络（Adversarial Network）的代码示例：

% 加载数据
[trainImages, trainLabels] = mnist.loadTrainSet();
[testImages, testLabels] = mnist.loadTestSet();

% 定义生成器网络
generator = [
    imageInputLayer([100 1 1], 'Normalization', 'none')
    fullyConnectedLayer(7*7*128)
    reshapeLayer([7 7 128])
    transposedConv2dLayer(4, 128, 'Stride', 2, 'Cropping', 1)
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(4, 64, 'Stride', 2, 'Cropping', 1)
    batchNormalizationLayer
    reluLayer
    transposedConv2dLayer(4, 1, 'Stride', 2, 'Cropping', 1)
    tanhLayer
];

% 定义判别器网络
discriminator = [
    imageInputLayer([28 28 1])
    convolution2dLayer(5, 32, 'Padding', 2)
    batchNormalizationLayer
    leakyReluLayer(0.2)
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(5, 64, 'Padding', 2)
    batchNormalizationLayer
    leakyReluLayer(0.2)
    maxPooling2dLayer(2, 'Stride', 2)
    fullyConnectedLayer(1024)
    leakyReluLayer(0.2)
    fullyConnectedLayer(1)
    sigmoidLayer
];

% 定义整个对抗网络
gan = ganNetwork(generator, discriminator);

% 设置训练选项
options = trainingOptions('sgdm', ...
    'MaxEpochs', 100, ...
    'MiniBatchSize', 128, ...
    'Plots', 'training-progress');

% 训练生成器和判别器
[gan, generatorLoss, discriminatorLoss] = trainNetwork(trainImages, gan, options);

% 生成一些假图像
noise = randn(100, 1, 1, 'single');
fakeImages = predict(generator, noise);

% 显示生成的图像
montage(fakeImages, 'Size', [10 10]);

这个示例中，我们使用了MNIST数据集，定义了一个简单的生成器和判别器网络，并且训练了整个对抗网络。在训练结束后，我们使用生成器生成了一些假图像，并通过montage函数将它们显示出来。