dropoutProb = 0.5; numFilters = 64; scale = 0.2; inputSize = [64 64 3]; filterSize = 5; layersDiscriminator = [ imageInputLayer(inputSize,'Normalization','none','Name','in') dropoutLayer(0.5,'Name','dropout') convolution2dLayer(filterSize,numFilters,'Stride',2,'Padding','same','Name','conv1') leakyReluLayer(scale,'Name','lrelu1') convolution2dLayer(filterSize,2*numFilters,'Stride',2,'Padding','same','Name','conv2') batchNormalizationLayer('Name','bn2') leakyReluLayer(scale,'Name','lrelu2') convolution2dLayer(filterSize,4*numFilters,'Stride',2,'Padding','same','Name','conv3') batchNormalizationLayer('Name','bn3') leakyReluLayer(scale,'Name','lrelu3') convolution2dLayer(filterSize,8*numFilters,'Stride',2,'Padding','same','Name','conv4') batchNormalizationLayer('Name','bn4') leakyReluLayer(scale,'Name','lrelu4') convolution2dLayer(4,1,'Name','conv5')]; lgraphDiscriminator = layerGraph(layersDiscriminator);对每行代码进行注释

以下是对每行代码的注释：

dropoutProb = 0.5; % 设置dropout的丢弃概率为0.5
numFilters = 64; % 设置卷积层中的滤波器数量为64
scale = 0.2; % 设置leakyRelu层的缩放因子为0.2

inputSize = [64 64 3]; % 设置输入图像的大小为64x64x3
filterSize = 5; % 设置卷积层的滤波器大小为5x5

layersDiscriminator = [
    imageInputLayer(inputSize,'Normalization','none','Name','in') % 创建输入图像层，指定其大小和名称
    dropoutLayer(0.5,'Name','dropout') % 创建dropout层，设置丢弃概率为0.5
    convolution2dLayer(filterSize,numFilters,'Stride',2,'Padding','same','Name','conv1') % 创建卷积层，指定滤波器大小、数量、步长和填充方式
    leakyReluLayer(scale,'Name','lrelu1') % 创建leakyRelu层，指定缩放因子
    convolution2dLayer(filterSize,2*numFilters,'Stride',2,'Padding','same','Name','conv2') % 创建卷积层，指定滤波器大小、数量、步长和填充方式
    batchNormalizationLayer('Name','bn2') % 创建批归一化层
    leakyReluLayer(scale,'Name','lrelu2') % 创建leakyRelu层，指定缩放因子
    convolution2dLayer(filterSize,4*numFilters,'Stride',2,'Padding','same','Name','conv3') % 创建卷积层，指定滤波器大小、数量、步长和填充方式
    batchNormalizationLayer('Name','bn3') % 创建批归一化层
    leakyReluLayer(scale,'Name','lrelu3') % 创建leakyRelu层，指定缩放因子
    convolution2dLayer(filterSize,8*numFilters,'Stride',2,'Padding','same','Name','conv4') % 创建卷积层，指定滤波器大小、数量、步长和填充方式
    batchNormalizationLayer('Name','bn4') % 创建批归一化层
    leakyReluLayer(scale,'Name','lrelu4') % 创建leakyRelu层，指定缩放因子
    convolution2dLayer(4,1,'Name','conv5')]; % 创建卷积层，指定滤波器大小和数量

lgraphDiscriminator = layerGraph(layersDiscriminator); % 将网络层组合成网络图

以上代码是一个鉴别器网络的构建过程，其中包含了输入图像层、dropout层、卷积层、leakyRelu层、批归一化层等。可以根据需要调整参数或添加其他层来构建自定义的鉴别器网络。