在pytorch上构建卷积神经网络来实现VAE的代码
时间: 2023-03-20 19:01:35 浏览: 128
以下是在 PyTorch 上构建卷积神经网络实现变分自编码器(VAE)的代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Encoder(nn.Module):
def __init__(self, in_channels, latent_dim):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 32, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
self.fc1 = nn.Linear(4 * 4 * 256, latent_dim)
self.fc2 = nn.Linear(4 * 4 * 256, latent_dim)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
x = x.view(x.size(0), -1)
mu = self.fc1(x)
logvar = self.fc2(x)
return mu, logvar
class Decoder(nn.Module):
def __init__(self, latent_dim, out_channels):
super(Decoder, self).__init__()
self.fc = nn.Linear(latent_dim, 4 * 4 * 256)
self.deconv1 = nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1)
self.deconv2 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
self.deconv3 = nn.ConvTranspose2d(64, 32, kernel_size=4, stride=2, padding=1)
self.deconv4 = nn.ConvTranspose2d(32, out_channels, kernel_size=4, stride=2, padding=1)
def forward(self, z):
x = self.fc(z)
x = x.view(x.size(0), 256, 4, 4)
x = F.relu(self.deconv1(x))
x = F.relu(self.deconv2(x))
x = F.relu(self.deconv3(x))
x = torch.sigmoid(self.deconv4(x))
return x
class VAE(nn.Module):
def __init__(self, in_channels, latent_dim):
super(VAE, self).__init__()
self.encoder = Encoder(in_channels, latent_dim)
self.decoder = Decoder(latent_dim, in_channels)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
z = mu + eps * std
return z
def forward(self, x):
mu, logvar = self.encoder(x)
z = self.reparameterize(mu, logvar)
x_recon = self.decoder(z)
return x_recon, mu, logvar
```
上述代码定义了三个模块:编码器、解码器和 VAE。编码器使用卷积神经网络对输入图像进行编码,将其压缩