gan pytorch实现
时间: 2023-09-14 08:12:02 浏览: 44
GAN是一种生成对抗网络,可以用于生成逼真的图像、音频等等。下面是一个基于PyTorch框架的GAN实现的代码示例,包含生成器和判别器的定义、损失函数和优化器的设置等等。
首先,我们需要导入PyTorch和其他必要的库:
```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
import numpy as np
import matplotlib.pyplot as plt
```
然后,我们需要定义生成器和判别器的架构。这里我们使用简单的全连接神经网络作为生成器(Generator),使用卷积神经网络作为判别器(Discriminator):
```python
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):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
out = self.relu(out)
out = self.fc3(out)
out = self.tanh(out)
return out
class Discriminator(nn.Module):
def __init__(self, input_size, hidden_size):
super(Discriminator, self).__init__()
self.conv1 = nn.Conv2d(input_size, hidden_size, kernel_size=4, stride=2, padding=1)
self.conv2 = nn.Conv2d(hidden_size, hidden_size * 2, kernel_size=4, stride=2, padding=1)
self.bn2 = nn.BatchNorm2d(hidden_size * 2)
self.conv3 = nn.Conv2d(hidden_size * 2, hidden_size * 4, kernel_size=4, stride=2, padding=1)
self.bn3 = nn.BatchNorm2d(hidden_size * 4)
self.conv4 = nn.Conv2d(hidden_size * 4, hidden_size * 8, kernel_size=4, stride=2, padding=1)
self.bn4 = nn.BatchNorm2d(hidden_size * 8)
self.conv5 = nn.Conv2d(hidden_size * 8, 1, kernel_size=4, stride=1, padding=0)
self.sigmoid = nn.Sigmoid()
self.leaky_relu = nn.LeakyReLU(0.2)
def forward(self, x):
out = self.conv1(x)
out = self.leaky_relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.leaky_relu(out)
out = self.conv3(out)
out = self.bn3(out)
out = self.leaky_relu(out)
out = self.conv4(out)
out = self.bn4(out)
out = self.leaky_relu(out)
out = self.conv5(out)
out = self.sigmoid(out)
return out.view(-1, 1)
```
接着,我们需要定义损失函数和优化器。生成器和判别器的损失函数分别为交叉熵和二元交叉熵,优化器使用Adam:
```python
criterion = nn.BCELoss()
generator = Generator(input_size=100, hidden_size=256, output_size=784)
discriminator = Discriminator(input_size=1, hidden_size=64)
generator_optimizer = optim.Adam(generator.parameters(), lr=0.0002, betas=(0.5, 0.999))
discriminator_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002, betas=(0.5, 0.999))
```
接下来,我们需要定义训练过程。首先,我们需要定义生成器生成的假图像的数量和噪声的维度,以及每一个epoch中生成器和判别器的训练次数:
```python
batch_size = 64
noise_dim = 100
num_epochs = 100
num_g_steps = 1
num_d_steps = 1
```
然后,我们可以开始训练。在每一个epoch中,我们先通过生成器生成一些假图像,然后将假图像和真实图像放入判别器中进行训练。训练过程中,我们需要计算生成器和判别器的损失,并进行反向传播更新模型参数。
```python
for epoch in range(num_epochs):
for i, (real_images, _) in enumerate(train_loader):
real_images = Variable(real_images)
real_labels = Variable(torch.ones(real_images.size(0)))
# Train discriminator
for j in range(num_d_steps):
discriminator.zero_grad()
noise = Variable(torch.randn(real_images.size(0), noise_dim))
fake_images = generator(noise)
fake_labels = Variable(torch.zeros(fake_images.size(0)))
d_loss_real = criterion(discriminator(real_images), real_labels)
d_loss_fake = criterion(discriminator(fake_images), fake_labels)
d_loss = d_loss_real + d_loss_fake
d_loss.backward()
discriminator_optimizer.step()
# Train generator
for j in range(num_g_steps):
generator.zero_grad()
noise = Variable(torch.randn(real_images.size(0), noise_dim))
fake_images = generator(noise)
fake_labels = Variable(torch.ones(fake_images.size(0)))
g_loss = criterion(discriminator(fake_images), fake_labels)
g_loss.backward()
generator_optimizer.step()
# Logging
if i % 100 == 0:
print('Epoch [%d/%d], Step [%d/%d], d_loss: %.4f, g_loss: %.4f'
% (epoch + 1, num_epochs, i + 1, len(train_loader), d_loss.data[0], g_loss.data[0]))
# Generate some sample images
if epoch == 0:
noise = Variable(torch.randn(64, noise_dim))
fake_images = generator(noise)
save_image(fake_images.data, 'gan_samples.png')
# Save the model checkpoints
torch.save(generator.state_dict(), 'generator.pth')
torch.save(discriminator.state_dict(), 'discriminator.pth')
```
最后,我们可以生成一些样本图像并可视化:
```python
# Load the trained model
generator.load_state_dict(torch.load('generator.pth'))
# Generate sample images
noise = Variable(torch.randn(64, noise_dim))
fake_images = generator(noise)
# Visualize the generated images
fake_images = fake_images.view(fake_images.size(0), 1, 28, 28).data.numpy()
fig = plt.figure(figsize=(8, 8))
for i in range(64):
ax = fig.add_subplot(8, 8, i + 1)
ax.imshow(fake_images[i, 0], cmap='gray')
ax.axis('off')
plt.show()
```
这就是一个基于PyTorch框架的GAN实现的示例代码。需要注意的是,GAN的训练过程非常复杂,需要对损失函数、优化器、网络架构等等进行调试。
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