生成对抗网络pytorch

生成对抗网络（GAN）在PyTorch中的实现相对简单。以下是一个简单的示例GAN代码：

首先，我们需要导入所需的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd.variable import Variable

接下来，我们定义生成器和判别器的网络结构：

class Generator(nn.Module):
  def __init__(self):
    super(Generator, self).__init__()
    self.fc1 = nn.Linear(100, 128)
    self.fc2 = nn.Linear(128, 784)

  def forward(self, x):
    x = nn.LeakyReLU(0.2)(self.fc1(x))
    x = nn.Tanh()(self.fc2(x))
    return x

class Discriminator(nn.Module):
  def __init__(self):
    super(Discriminator, self).__init__()
    self.fc1 = nn.Linear(784, 128)
    self.fc2 = nn.Linear(128, 1)

  def forward(self, x):
    x = nn.LeakyReLU(0.2)(self.fc1(x))
    x = nn.Sigmoid()(self.fc2(x))
    return x

然后，我们定义数据加载器和优化器：

batch_size = 100

transform = transforms.Compose([
  transforms.ToTensor(),
  transforms.Normalize((0.5,), (0.5,))
])

train_data = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True)

generator = Generator()
discriminator = Discriminator()

criterion = nn.BCELoss()
gen_optimizer = optim.Adam(generator.parameters(), lr=0.0002)
dis_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002)

接下来，我们定义训练循环：

num_epochs = 200

for epoch in range(num_epochs):
  for i, (images, _) in enumerate(train_loader):
    batch_size = images.size(0)
    real_images = Variable(images.view(batch_size, -1))

    # Train discriminator
    dis_optimizer.zero_grad()
    dis_output_real = discriminator(real_images)
    dis_loss_real = criterion(dis_output_real, Variable(torch.ones(batch_size, 1)))
    dis_loss_real.backward()

    z = Variable(torch.randn(batch_size, 100))
    fake_images = generator(z)
    dis_output_fake = discriminator(fake_images.detach())
    dis_loss_fake = criterion(dis_output_fake, Variable(torch.zeros(batch_size, 1)))
    dis_loss_fake.backward()

    dis_loss = dis_loss_real + dis_loss_fake
    dis_optimizer.step()

    # Train generator
    gen_optimizer.zero_grad()
    z = Variable(torch.randn(batch_size, 100))
    fake_images = generator(z)
    dis_output_fake = discriminator(fake_images)
    gen_loss = criterion(dis_output_fake, Variable(torch.ones(batch_size, 1)))
    gen_loss.backward()
    gen_optimizer.step()

    if (i+1) % 100 == 0:
      print('Epoch [%d/%d], Step [%d/%d], Discriminator Loss: %.4f, Generator Loss: %.4f' % (epoch+1, num_epochs, i+1, len(train_data)//batch_size, dis_loss.data[0], gen_loss.data[0]))

torch.save(generator.state_dict(), 'generator.pkl')
torch.save(discriminator.state_dict(), 'discriminator.pkl')

在这个训练循环中，我们首先训练鉴别器，然后训练生成器。最后，我们保存生成器和鉴别器的权重。

这是一个简单的GAN实现示例。但是，GAN的调试和微调可能需要更多的工作。