class Discriminator(nn.Module): # 定义判别器
def __init__(self, img_size=(28, 28), num_classes=2): # 初始化方法
super(Discriminator, self).__init__() # 继承初始化方法
self.img_size = img_size # 图片尺寸，默认单通道灰度图
self.num_classes = num_classes # 类别数
self.linear1 = nn.Linear(self.img_size[0] * self.img_size[1], 512) # linear映射
self.linear2 = nn.Linear(512, 512) # linear映射
self.linear3 = nn.Linear(512, 512) # linear映射
self.linear4 = nn.Linear(512, 1) # linear映射
self.linear5 = nn.Linear(512, self.num_classes) # linear映射
self.dropout = nn.Dropout(0.4) # dropout操作
self.leakyrelu = nn.LeakyReLU(0.2, inplace=True) # leakyrelu激活函数
self.sigmoid = nn.Sigmoid() # sigmoid激活函数
self.softmax = nn.Softmax(dim=1) # softmax激活函数
def forward(self, x): # 前传函数
x = torch.flatten(x, 1) # 输入图片从三维压缩至一维特征向量，(n,1,28,28)-->(n,784)
x = self.linear1(x) # linear映射，(n,784)-->(n,512)
x = self.leakyrelu(x) # leakyrelu激活函数
x = self.linear2(x) # linear映射,(n,512)-->(n,512)
x = self.leakyrelu(x) # leakyrelu激活函数
x = self.dropout(x) # dropout操作
x = self.linear3(x) # linear映射,(n,512)-->(n,512)
x = self.leakyrelu(x) # leakyrelu激活函数
x = self.dropout(x) # dropout操作
# 根据特征向量x，计算图片真假的得分
validity = self.linear4(x) # linear映射,(n,512)-->(n,1)
validity = self.sigmoid(validity) # sigmoid激活函数，将输出压缩至（0，1）
# 根据特征向量x，计算图片分类的标签
label = self.linear5(x) # linear映射,(n,512)-->(n,2)
label = self.softmax(label) # softmax激活函数，将输出压缩至（0，1）
return (validity, label) # 返回(图片真假的得分，图片分类的标签)
class Generator(nn.Module): # 定义生成器
def __init__(self, img_size=(28, 28), num_classes=2, latent_dim=100): # 初始化方法
super(Generator, self).__init__()
self.img_size = img_size # 图片尺寸，默认单通道灰度图
self.num_classes = num_classes # 类别数
self.latent_dim = latent_dim # 噪声z的长度
self.linear1 = nn.Linear(self.latent_dim, 256) # linear映射
self.bn1 = nn.BatchNorm1d(256, 0.8) # bn操作
self.linear2 = nn.Linear(256, 512) # linear映射
self.bn2 = nn.BatchNorm1d(512, 0.8) # bn操作
self.linear3 = nn.Linear(512, 1024) # linear映射
self.bn3 = nn.BatchNorm1d(1024, 0.8) # bn操作
self.linear4 = nn.Linear(1024, self.img_size[0] * self.img_size[1]) # linear映射
self.leakyrelu = nn.LeakyReLU(0.2, inplace=True) # leakyrelu激活函数
self.tanh = nn.Tanh() # tanh激活函数
self.embedding = nn.Embedding(self.num_classes, self.latent_dim) # embedding操作
def forward(self, input: tuple): # 前传函数
noise, label = input # 从输入的元组中获取噪声向量和标签信息
label = self.embedding(label) # 标签信息经过embedding操作，变成与噪声向量尺寸相同的稠密向量
z = torch.multiply(noise, label) # 噪声向量与标签稠密向量相乘，得到带有标签信息的噪声向量
z = self.linear1(z) # linear映射,(n,100)-->(n,256)
z = self.leakyrelu(z) # leakyrelu激活函数
z = self.linear2(z) # linear映射,(n,256)-->(n,512)
z = self.bn2(z) # 一维bn操作
z = self.leakyrelu(z) # leakyrelu激活函数
z = self.linear3(z) # linear映射,(n,512)-->(n,1024)
z = self.bn3(z) # 一维bn操作
z = self.leakyrelu(z) # leakyrelu激活函数
z = self.linear4(z) # linear映射,(n,1024)-->(n,784)
z = self.tanh(z) # tanh激活函数，将输出压缩至（-1.1）
z = z.view(-1, 1, self.img_size[0], self.img_size[1]) # 从一维特征向量扩展至三维图片，(n,784)-->(n,1,28,28)
return z # 返回生成的图片
