pytorch怎么搭建gan网络

搭建 GAN 网络需要分别定义 Generator 和 Discriminator 两个网络，使用 PyTorch 可以按照以下步骤进行：

1.导入必要的包：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms
from torchvision.datasets import MNIST
from torchvision.utils import make_grid
import matplotlib.pyplot as plt

2.定义 Generator 网络：

class Generator(nn.Module):
    def __init__(self, latent_dim=100, img_shape=(1, 28, 28)):
        super(Generator, self).__init__()
        self.img_shape = img_shape

        self.model = nn.Sequential(
            nn.Linear(latent_dim, 128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 512),
            nn.BatchNorm1d(512, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024, 0.8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(1024, int(torch.prod(torch.tensor(img_shape)))),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), *self.img_shape)
        return img

3.定义 Discriminator 网络：

class Discriminator(nn.Module):
    def __init__(self, img_shape=(1, 28, 28)):
        super(Discriminator, self).__init__()
        self.img_shape = img_shape

        self.model = nn.Sequential(
            nn.Linear(int(torch.prod(torch.tensor(img_shape))), 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1),
            nn.Sigmoid(),
        )

    def forward(self, img):
        img_flat = img.view(img.size(0), -1)
        validity = self.model(img_flat)
        return validity

4.定义训练过程：

# 定义超参数
lr = 0.0002
b1 = 0.5
b2 = 0.999
n_epochs = 200
batch_size = 64
latent_dim = 100
img_shape = (1, 28, 28)

# 定义数据集
dataset = MNIST(root='data/', train=True, transform=transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,)),
]))
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 定义网络和优化器
generator = Generator(latent_dim=latent_dim, img_shape=img_shape)
discriminator = Discriminator(img_shape=img_shape)
adversarial_loss = nn.BCELoss()
optimizer_G = optim.Adam(generator.parameters(), lr=lr, betas=(b1, b2))
optimizer_D = optim.Adam(discriminator.parameters(), lr=lr, betas=(b1, b2))

# 训练过程
for epoch in range(n_epochs):
    for i, (imgs, _) in enumerate(dataloader):

        # 定义输入数据和真实标签
        batch_size = imgs.shape[0]
        real_imgs = imgs.cuda()
        valid = torch.ones((batch_size, 1)).cuda()
        fake = torch.zeros((batch_size, 1)).cuda()

        # -----------------
        # 训练生成器
        # -----------------

        optimizer_G.zero_grad()

        # 生成随机噪声并生成图像
        z = torch.randn((batch_size, latent_dim)).cuda()
        gen_imgs = generator(z)

        # 计算生成图像的损失并进行反向传播
        g_loss = adversarial_loss(discriminator(gen_imgs), valid)
        g_loss.backward()
        optimizer_G.step()

        # ---------------------
        # 训练判别器
        # ---------------------

        optimizer_D.zero_grad()

        # 计算真实图像的损失并进行反向传播
        real_loss = adversarial_loss(discriminator(real_imgs), valid)

        # 计算生成图像的损失并进行反向传播
        fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)

        # 计算总损失并进行反向传播
        d_loss = (real_loss + fake_loss) / 2
        d_loss.backward()
        optimizer_D.step()

    # 打印损失和生成图像
    print(
        "[Epoch %d/%d] [D loss: %f] [G loss: %f]"
        % (epoch + 1, n_epochs, d_loss.item(), g_loss.item())
    )
    if epoch % 10 == 0:
        with torch.no_grad():
            z = torch.randn((batch_size, latent_dim)).cuda()
            gen_imgs = generator(z)
            img_grid = make_grid(gen_imgs[:25], nrow=5, normalize=True)
            plt.imshow(img_grid.permute(1, 2, 0).cpu().numpy())
            plt.show()

以上就是 PyTorch 搭建 GAN 网络的基本步骤，如有需要可以根据具体情况进行修改。