使用pytorch实现wdcgan的代码
时间: 2023-10-10 09:11:34 浏览: 36
以下是使用PyTorch实现WDcGAN的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
# 定义生成器
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(100, 64 * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(64 * 8),
nn.ReLU(True),
nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(64 * 4),
nn.ReLU(True),
nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(64 * 2),
nn.ReLU(True),
nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(True),
nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
output = self.main(input)
return output
# 定义判别器
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv2d(3, 64, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, 64 * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(64 * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64 * 2, 64 * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(64 * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64 * 4, 64 * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(64 * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64 * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
output = self.main(input)
return output.view(-1, 1).squeeze(1)
# 初始化网络
generator = Generator()
discriminator = Discriminator()
# 定义损失函数
criterion = nn.BCELoss()
# 定义优化器
optimizer_g = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))
# 加载数据集
transform = transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_dataset = datasets.ImageFolder('path/to/your/dataset', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4)
# 训练模型
for epoch in range(100):
for i, (images, _) in enumerate(train_loader):
# 训练判别器
discriminator.zero_grad()
real_images = Variable(images)
real_labels = Variable(torch.ones(images.size(0)))
fake_labels = Variable(torch.zeros(images.size(0)))
# 计算真实图片的判别结果并计算损失
real_outputs = discriminator(real_images)
d_loss_real = criterion(real_outputs, real_labels)
d_loss_real.backward()
# 生成假的图片并计算判别结果和损失
z = Variable(torch.randn(images.size(0), 100, 1, 1))
fake_images = generator(z).detach()
fake_outputs = discriminator(fake_images)
d_loss_fake = criterion(fake_outputs, fake_labels)
d_loss_fake.backward()
# 更新判别器
d_loss = d_loss_real + d_loss_fake
optimizer_d.step()
# 训练生成器
generator.zero_grad()
z = Variable(torch.randn(images.size(0), 100, 1, 1))
fake_images = generator(z)
fake_outputs = discriminator(fake_images)
g_loss = criterion(fake_outputs, real_labels)
g_loss.backward()
optimizer_g.step()
# 输出训练结果
print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f' % (epoch, 100, i, len(train_loader), d_loss.data[0], g_loss.data[0]))
```
请注意,此代码可能需要根据您的数据集和计算资源进行修改。
相关推荐
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)