cgan pytorch
时间: 2024-04-19 08:22:26 浏览: 17
CGAN(Conditional Generative Adversarial Network)是一种基于生成对抗网络(GAN)的条件生成模型。它通过在生成器和判别器中引入条件信息,可以实现对特定条件下的生成任务。
在PyTorch中,可以使用以下步骤来实现CGAN:
1. 定义生成器(Generator)和判别器(Discriminator)的网络结构。
2. 定义生成器和判别器的损失函数,通常使用交叉熵损失函数。
3. 定义优化器,如Adam优化器。
4. 在训练过程中,首先从数据集中随机选择一个条件和一个真实样本。
5. 将条件和真实样本输入给判别器,计算判别器的损失并进行反向传播优化判别器的参数。
6. 生成一个与条件匹配的噪声向量,并将其与条件输入给生成器,生成一个假样本。
7. 将假样本和条件输入给判别器,计算生成器的损失并进行反向传播优化生成器的参数。
8. 重复步骤4-7直到达到预定的训练次数或损失收敛。
相关问题
pytorch使用cgan生成图片
好的,很高兴为您解答。GAN(Generative Adversarial Networks)是一种深度学习模型,可以生成与训练数据相似的新数据。CGAN(Conditional GAN)是一种带条件的GAN,它能够在生成新数据时考虑输入的条件,使生成的数据更加精确和准确。在使用PyTorch实现CGAN生成图片时,一般需要先定义生成器和判别器模型,然后设置优化器、损失函数等参数。接着,将带有条件的真实图像和噪声输入到生成器中,然后将生成器生成的假图像和真实图像一起输入到判别器中进行判别,最后通过反向传播调整生成器和判别器的参数,不断优化模型,使得生成的图片更加真实和准确。
CGAN自动生成动漫头像代码PyTorch
下面是一个使用PyTorch实现的CGAN自动生成动漫头像的代码示例:
```python
import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
from torch.autograd import Variable
# Hyper Parameters
num_epochs = 200
batch_size = 100
learning_rate = 0.0002
# Image Preprocessing
transform = transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# MNIST Dataset
train_dataset = dsets.ImageFolder(root='./data', transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
# Discriminator Model
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 4, 2, 1)
self.conv2 = nn.Conv2d(64, 128, 4, 2, 1)
self.bn2 = nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 256, 4, 2, 1)
self.bn3 = nn.BatchNorm2d(256)
self.conv4 = nn.Conv2d(256, 512, 4, 2, 1)
self.bn4 = nn.BatchNorm2d(512)
self.conv5 = nn.Conv2d(512, 1, 4, 1, 0)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = nn.functional.leaky_relu(self.conv1(x), 0.2, inplace=True)
x = nn.functional.leaky_relu(self.bn2(self.conv2(x)), 0.2, inplace=True)
x = nn.functional.leaky_relu(self.bn3(self.conv3(x)), 0.2, inplace=True)
x = nn.functional.leaky_relu(self.bn4(self.conv4(x)), 0.2, inplace=True)
x = self.sigmoid(self.conv5(x))
return x
# Generator Model
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.linear = nn.Linear(100, 512 * 4 * 4)
self.bn1 = nn.BatchNorm2d(512)
self.deconv1 = nn.ConvTranspose2d(512, 256, 4, 2, 1)
self.bn2 = nn.BatchNorm2d(256)
self.deconv2 = nn.ConvTranspose2d(256, 128, 4, 2, 1)
self.bn3 = nn.BatchNorm2d(128)
self.deconv3 = nn.ConvTranspose2d(128, 64, 4, 2, 1)
self.bn4 = nn.BatchNorm2d(64)
self.deconv4 = nn.ConvTranspose2d(64, 3, 4, 2, 1)
self.tanh = nn.Tanh()
def forward(self, x):
x = nn.functional.relu(self.bn1(self.linear(x).view(-1, 512, 4, 4)))
x = nn.functional.relu(self.bn2(self.deconv1(x)))
x = nn.functional.relu(self.bn3(self.deconv2(x)))
x = nn.functional.relu(self.bn4(self.deconv3(x)))
x = self.tanh(self.deconv4(x))
return x
# Discriminator and Generator
D = Discriminator()
G = Generator()
# Loss Functions
criterion = nn.BCELoss()
# Optimizers
D_optimizer = torch.optim.Adam(D.parameters(), lr=learning_rate)
G_optimizer = torch.optim.Adam(G.parameters(), lr=learning_rate)
# Training
for epoch in range(num_epochs):
for i, (images, _) in enumerate(train_loader):
batch_size = images.size(0)
# Real Images
real_images = Variable(images)
real_labels = Variable(torch.ones(batch_size))
# Fake Images
z = Variable(torch.randn(batch_size, 100))
fake_images = G(z)
fake_labels = Variable(torch.zeros(batch_size))
# Train Discriminator
D_optimizer.zero_grad()
real_outputs = D(real_images)
real_loss = criterion(real_outputs, real_labels)
fake_outputs = D(fake_images)
fake_loss = criterion(fake_outputs, fake_labels)
D_loss = real_loss + fake_loss
D_loss.backward()
D_optimizer.step()
# Train Generator
G_optimizer.zero_grad()
z = Variable(torch.randn(batch_size, 100))
fake_images = G(z)
outputs = D(fake_images)
G_loss = criterion(outputs, real_labels)
G_loss.backward()
G_optimizer.step()
# Print Losses
if (i + 1) % 10 == 0:
print("Epoch [{}/{}], Step [{}/{}], D Loss: {:.4f}, G Loss: {:.4f}"
.format(epoch + 1, num_epochs, i + 1, len(train_dataset) // batch_size, D_loss.data.item(), G_loss.data.item()))
# Save Generated Images
fake_images = G(z)
torchvision.utils.save_image(fake_images.data, './generated_images/{}.png'.format(epoch + 1), nrow=10)
```
在这个代码中,我们使用了PyTorch框架来实现CGAN模型。我们首先定义了一个Discriminator模型和一个Generator模型,并且使用BCELoss作为损失函数,使用Adam优化器来进行模型训练。在训练过程中,我们首先训练Discriminator模型,然后训练Generator模型,并且每个epoch保存生成的图片。