对抗生成神经网络代码

以下是一个简单的对抗生成神经网络（GAN）的代码示例：

import torch
import torch.nn as nn

# 定义生成器网络
class Generator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        x = self.sigmoid(x)
        return x

# 定义鉴别器网络
class Discriminator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.fc1(x)
        x = self.fc2(x)
        x = self.fc3(x)
        x = self.sigmoid(x)
        return x

# 定义GAN模型
class GAN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(GAN, self).__init__()
        self.generator = Generator(input_size, hidden_size, output_size)
        self.discriminator = Discriminator(output_size, hidden_size, 1)

    def forward(self, x):
        generated_data = self.generator(x)
        output = self.discriminator(generated_data)
        return generated_data, output

# 创建GAN模型实例
input_size = 100
hidden_size = 128
output_size = 784
gan = GAN(input_size, hidden_size, output_size)

# 打印模型结构
print(gan)

这段代码定义了一个简单的GAN模型，包括生成器网络和鉴别器网络。生成器网络将一个随机噪声向量作为输入，并生成一个与数据集相似的样本。鉴别器网络接收生成器生成的样本或真实数据，并输出一个介于0和1之间的标量，表示样本是真实的还是生成的。GAN模型将生成器和鉴别器组合在一起，并通过训练使生成器生成更逼真的样本，同时鉴别器能够更准确地区分真实样本和生成样本。