在pytorch上实现VAE,完成对矩阵数据降噪的代码
时间: 2023-03-20 15:01:31 浏览: 125
好的,以下是在PyTorch上实现VAE,完成对矩阵数据降噪的代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset
# 定义VAE模型
class VAE(nn.Module):
def __init__(self, input_dim, latent_dim):
super(VAE, self).__init__()
self.input_dim = input_dim
self.latent_dim = latent_dim
# 编码器
self.encoder = nn.Sequential(
nn.Linear(input_dim, 256),
nn.ReLU(),
nn.Linear(256, 128),
nn.ReLU(),
nn.Linear(128, latent_dim * 2)
)
# 解码器
self.decoder = nn.Sequential(
nn.Linear(latent_dim, 128),
nn.ReLU(),
nn.Linear(128, 256),
nn.ReLU(),
nn.Linear(256, input_dim),
nn.Sigmoid()
)
def encode(self, x):
h = self.encoder(x)
mu, logvar = torch.chunk(h, 2, dim=-1)
std = torch.exp(0.5 * logvar)
z = torch.randn_like(std) * std + mu
return z, mu, logvar
def decode(self, z):
return self.decoder(z)
def forward(self, x):
z, mu, logvar = self.encode(x)
x_recon = self.decode(z)
return x_recon, mu, logvar
def loss_function(self, x, x_recon, mu, logvar):
# 计算重构误差
recon_loss = F.binary_cross_entropy(x_recon, x, reduction='sum')
# 计算KL散度
kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return recon_loss + kld_loss
# 加载数据
data = torch.load('data.pt')
# 划分训练集和测试集
train_size = int(0.8 * len(data))
test_size = len(data) - train_size
train_data, test_data = torch.utils.data.random_split(data, [train_size, test_size])
# 创建数据加载器
batch_size = 64
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=True)
# 定义VAE模型和优化器
input_dim = data.size(-1)
latent_dim = 10
vae = VAE(input_dim, latent_dim)
optimizer = torch.optim.Adam(vae.parameters(), lr=1e-3)
# 训练VAE模型
epochs = 100
for epoch in range(epochs):
vae.train()
train_loss = 0
for batch_idx, x in enumerate(train_loader):
x = x.float()
optimizer.zero_grad()
x_recon, mu, logvar = vae(x)
loss = vae.loss_function(x, x_recon, mu, logvar)
loss.backward()
train_loss += loss.item()
optimizer.step()
print('Epoch: {} Train Loss: {:.4f}'.format(epoch + 1, train_loss / len(train_loader.dataset)))
# 评估VAE模型
vae.eval()
test_loss = 0
with torch.no_grad():
for batch_idx, x in enumerate(test_loader):
x = x
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