VAE带有数据包的代码
时间: 2024-04-14 18:03:33 浏览: 18
以下是一个简单的实现变分自动编码器(Variational Autoencoder,VAE)的代码示例,用于处理带有数据包(batch)的输入数据。
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
# 定义变分自动编码器的模型类
class VAE(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(VAE, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.fc21 = nn.Linear(hidden_dim, latent_dim)
self.fc22 = nn.Linear(hidden_dim, latent_dim)
self.fc3 = nn.Linear(latent_dim, hidden_dim)
self.fc4 = nn.Linear(hidden_dim, input_dim)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def encode(self, x):
h = self.relu(self.fc1(x))
return self.fc21(h), self.fc22(h)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5*logvar)
eps = torch.randn_like(std)
return mu + eps*std
def decode(self, z):
h = self.relu(self.fc3(z))
return self.sigmoid(self.fc4(h))
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
return self.decode(z), mu, logvar
# 定义训练函数
def train_vae(model, train_loader, num_epochs):
model.train()
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(num_epochs):
total_loss = 0
for batch_idx, data in enumerate(train_loader):
inputs, _ = data
inputs = inputs.view(-1, input_dim)
optimizer.zero_grad()
recon_batch, mu, logvar = model(inputs)
loss = criterion(recon_batch, inputs) + 0.5 * torch.sum(logvar.exp() - logvar - 1 + mu.pow(2))
loss.backward()
total_loss += loss.item()
optimizer.step()
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, total_loss))
# 示例使用
input_dim = 784 # 输入数据的维度
hidden_dim = 256 # 隐层维度
latent_dim = 64 # 潜在空间维度
# 创建变分自动编码器模型
model = VAE(input_dim, hidden_dim, latent_dim)
# 创建训练数据加载器
train_loader = torch.utils.data.DataLoader(dataset, batch_size=128, shuffle=True)
# 训练模型
num_epochs = 10
train_vae(model, train_loader, num_epochs)
```
请注意,此代码仅为示例,实际应用中可能需要根据具体需求进行修改和优化。