生成对抗网络具体实现代码

由于生成对抗网络结构复杂，实现过程中需要涉及深度学习框架的基础知识，以下是使用Python和PyTorch实现GAN的基本步骤：

1. 定义生成器和判别器的网络结构
2. 定义损失函数（对比度损失）
3. 定义优化器（Adam优化器、SGD优化器等）
4. 训练模型

下面是一个简单的GAN的代码实现：

# 导入依赖库
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torchvision
import numpy as np
import matplotlib.pyplot as plt

# 定义随机种子
torch.manual_seed(123)

# 定义超参数
num_epochs = 100
batch_size = 16
learning_rate = 0.0002

# 加载数据集
transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor(), 
                                torchvision.transforms.Normalize((0.5,), (0.5,))])
train_dataset = torchvision.datasets.MNIST(root='.', train=True, 
                                           transform=transform, download=True)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# 定义生成器的网络结构
class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.fc1 = nn.Linear(100, 256)
        self.fc2 = nn.Linear(256, 512)
        self.fc3 = nn.Linear(512, 784)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        x = self.relu(x)
        x = self.fc3(x)
        x = torch.tanh(x)
        return x

# 定义判别器的网络结构
class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()
        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.fc1(x)
        x = self.sigmoid(x)
        x = self.fc2(x)
        x = self.sigmoid(x)
        x = self.fc3(x)
        x = self.sigmoid(x)
        return x

# 创建生成器和判别器实例
generator = Generator()
discriminator = Discriminator()

# 定义损失函数
criterion = nn.BCELoss()

# 定义优化器
generator_optimizer = optim.Adam(generator.parameters(), lr=learning_rate)
discriminator_optimizer = optim.Adam(discriminator.parameters(), lr=learning_rate)

# 存储损失函数值
generator_loss = []
discriminator_loss = []

# 训练GAN模型
for epoch in range(num_epochs):
    for i, (images, _) in enumerate(train_loader):
        batch_size = images.size(0)
        
        # 获取真实图片
        real_images = Variable(images.view(batch_size, -1))
        real_labels = Variable(torch.ones(batch_size, 1))
        
        # 获取生成器的噪声输入
        noise = Variable(torch.randn(batch_size, 100))
        
        # 生成假图片
        fake_images = generator(noise)
        fake_labels = Variable(torch.zeros(batch_size, 1))
        
        # 训练判别器
        discriminator_optimizer.zero_grad()
        real_outputs = discriminator(real_images)
        real_loss = criterion(real_outputs, real_labels)
        fake_outputs = discriminator(fake_images)
        fake_loss = criterion(fake_outputs, fake_labels)
        d_loss = real_loss + fake_loss
        d_loss.backward()
        discriminator_optimizer.step()
        discriminator_loss.append(d_loss.item())
        
        # 训练生成器
        generator_optimizer.zero_grad()
        noise = Variable(torch.randn(batch_size, 100))
        fake_images = generator(noise)
        outputs = discriminator(fake_images)
        g_loss = criterion(outputs, real_labels)
        g_loss.backward()
        generator_optimizer.step()
        generator_loss.append(g_loss.item())
        
        
    if (epoch+1) % 10 == 0:
        print("Epoch [{}/{}], Step [{}/{}], Discriminator Loss: {:.4f}, Generator Loss: {:.4f}"
              .format(epoch+1, num_epochs, i+1, len(train_loader), d_loss.item(), g_loss.item()))

# 生成100张图片并展示
noise = Variable(torch.randn(100, 100))
generated_images = generator(noise).data.numpy()
generated_images = generated_images.reshape(100, 28, 28)
fig, ax = plt.subplots(10, 10, figsize=(10, 10))
for i in range(10):
    for j in range(10):
        ax[i][j].imshow(generated_images[i*10+j], cmap='gray')
        ax[i][j].axis('off')
plt.show()

以上代码实现了基本的GAN，其中指定了数据集为MNIST手写数字数据集，生成器的输入噪声为100维的向量，输出为784维的图片像素值；判别器的输入是784维的图片像素值，输出为一个二元分类结果。在GAN的训练过程中，首先训练判别器来区分真实图片和假图片，再根据判别器的结果训练生成器生成更逼真的假图片，两者交替进行。最后使用生成器生成100张假图片并展示。