基于pytorch写一个gan的损失函数

下面是一个基于PyTorch实现的GAN损失函数的示例代码：

import torch.nn.functional as F

def gan_loss_function(real_output, fake_output):
    # 计算生成器损失
    gen_loss = F.binary_cross_entropy_with_logits(fake_output, torch.ones_like(fake_output))
    
    # 计算判别器损失
    real_loss = F.binary_cross_entropy_with_logits(real_output, torch.ones_like(real_output))
    fake_loss = F.binary_cross_entropy_with_logits(fake_output, torch.zeros_like(fake_output))
    disc_loss = real_loss + fake_loss
    
    return gen_loss, disc_loss

其中，`real_output`是判别器对真实样本的输出，`fake_output`是判别器对生成样本的输出。`binary_cross_entropy_with_logits`函数是PyTorch中用于计算二元交叉熵损失的函数，可以方便地计算生成器损失和判别器损失。最终返回的是生成器损失和判别器损失。

相关问题

用pytorch写一个GAN代码

这里提供一个简单的GAN代码，使用PyTorch实现：```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.utils import save_image# 定义生成器
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.relu = nn.ReLU()
self.tanh = nn.Tanh() def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.tanh(self.fc3(x))
return x# 定义判别器
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.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid() def forward(self, x):
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.sigmoid(self.fc3(x))
return x# 定义超参数
batch_size = 100
input_size = 100
hidden_size = 256
output_size = 784
num_epochs = 200# 加载MNIST数据集
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(.5,), std=(.5,))
])
train_dataset = dset.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)# 初始化生成器和判别器
G = Generator(input_size, hidden_size, output_size)
D = Discriminator(output_size, hidden_size, 1)# 定义损失函数和优化器
criterion = nn.BCELoss()
G_optimizer = optim.Adam(G.parameters(), lr=.0002)
D_optimizer = optim.Adam(D.parameters(), lr=.0002)# 训练GAN模型
for epoch in range(num_epochs):
for i, (images, _) in enumerate(train_loader):
# 训练判别器
real_labels = torch.ones(batch_size, 1)
fake_labels = torch.zeros(batch_size, 1)
real_images = images.view(batch_size, -1)
z = torch.randn(batch_size, input_size)
fake_images = G(z) D_real_loss = criterion(D(real_images), real_labels)
D_fake_loss = criterion(D(fake_images), fake_labels)
D_loss = D_real_loss + D_fake_loss
D_optimizer.zero_grad()
D_loss.backward()
D_optimizer.step() # 训练生成器
z = torch.randn(batch_size, input_size)
fake_images = G(z)
G_loss = criterion(D(fake_images), real_labels)
G_optimizer.zero_grad()
G_loss.backward()
G_optimizer.step() # 打印损失值
if (i+1) % 100 == :
print('Epoch [{}/{}], Step [{}/{}], D_loss: {:.4f}, G_loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, len(train_loader), D_loss.item(), G_loss.item())) # 保存生成的图像
if (epoch+1) % 10 == :
fake_images = fake_images.view(batch_size, 1, 28, 28)
save_image(fake_images, './samples/sample_{}.png'.format(epoch+1))

基于pytorch，编一个GAN的代码

好的，以下是一个简单的GAN代码，用于生成手写数字图像：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
from torch.utils.data import DataLoader

# 定义生成器和判别器
class Generator(nn.Module):
    def __init__(self, ngf=64):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.ConvTranspose2d(100, ngf*8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(ngf*8),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf*4),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf*4, ngf*2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf*2),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf*2, ngf, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ngf),
            nn.ReLU(True),
            nn.ConvTranspose2d(ngf, 1, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)

class Discriminator(nn.Module):
    def __init__(self, ndf=64):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(1, ndf, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf, ndf*2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf*2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf*2, ndf*4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf*4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf*4, ndf*8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(ndf*8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(ndf*8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input).view(-1, 1)

# 定义训练函数
def train(netG, netD, dataloader, device, epochs=5, lr=0.0002, beta1=0.5):
    criterion = nn.BCELoss()
    optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
    optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
    fixed_noise = torch.randn(64, 100, 1, 1, device=device)

    for epoch in range(epochs):
        for i, data in enumerate(dataloader, 0):
            netD.zero_grad()
            real_cpu = data[0].to(device)
            b_size = real_cpu.size(0)
            label = torch.full((b_size,), 1, device=device)
            output = netD(real_cpu)
            errD_real = criterion(output, label)
            errD_real.backward()
            D_x = output.mean().item()

            noise = torch.randn(b_size, 100, 1, 1, device=device)
            fake = netG(noise)
            label.fill_(0)
            output = netD(fake.detach())
            errD_fake = criterion(output, label)
            errD_fake.backward()
            D_G_z1 = output.mean().item()
            errD = errD_real + errD_fake
            optimizerD.step()

            netG.zero_grad()
            label.fill_(1)
            output = netD(fake)
            errG = criterion(output, label)
            errG.backward()
            D_G_z2 = output.mean().item()
            optimizerG.step()

            print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'
                  % (epoch+1, epochs, i+1, len(dataloader), errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

        vutils.save_image(real_cpu, '%s/real_samples.png' % "./results", normalize=True)
        fake = netG(fixed_noise)
        vutils.save_image(fake.detach(), '%s/fake_samples_epoch_%03d.png' % ("./results", epoch+1), normalize=True)

# 加载数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
dataset = dset.MNIST(root="./data", download=True, transform=transform)
dataloader = DataLoader(dataset, batch_size=128, shuffle=True, num_workers=2)

# 训练
device = torch.device("cuda:0" if (torch.cuda.is_available()) else "cpu")
netG = Generator().to(device)
netD = Discriminator().to(device)
train(netG, netD, dataloader, device)

这是一个生成手写数字图像的GAN，使用的数据集是MNIST。在训练过程中，生成器和判别器通过对抗的方式互相提升，最终生成器可以生成逼真的手写数字图像。