VAE
2024-12-12
julyfunnotes大四上机器学习关于 VAE 理解的教程： https://spaces.ac.cn/archives/5253假定 $p(Z|X)$ 为一正态分布.（Z 为隐变量，X 为目标分布）注意不是假设 $p(Z)$为正态分布，不同 $X$ 显然必须有不同的隐藏分布，否则解码器无法区分它们，训练时 $X_i$ 和 $X^"hat"_i$ 就无法对应上.训练编码器使得样本对应的（编码器输出的）$mu$ 和 $log sigma^2$ 既要接近正态分布，又要对不同样本产生一些区别使得解码器能够将其还原到对应图像.接近正态又要有些微区分，这是一个权衡问题.为了防止正态分布采样以后，不同样本直接混在一起，其实不同类图像还是独占某一隐变量空间的区域的（这个我主成分分析绘制过）.Hint:正态分布的参数为 runtime 参数（中间层输出结果），不是 traintime 权重.大致代码（见 speit-ml-tp 仓库） :from torch.nn.functional import binary_cross_entropy
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
class Sampling(nn.Module):
def forward(self, mu, logvar):
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
class Encoder(nn.Module):
def __init__(self, latent_dim):
super(Encoder, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=2, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2, padding=1)
self.fc = nn.Linear(64 * 7 * 7, 16)
self.fc_mu = nn.Linear(16, latent_dim)
self.fc_logvar = nn.Linear(16, latent_dim)
def forward(self, x):
x = torch.relu(self.conv1(x))
x = torch.relu(self.conv2(x))
x = x.view(x.size(0), -1)
x = torch.relu(self.fc(x))
mu = self.fc_mu(x)
logvar = self.fc_logvar(x)
return mu, logvar
class Decoder(nn.Module):
def __init__(self, latent_dim):
super(Decoder, self).__init__()
self.fc = nn.Linear(latent_dim, 64 * 7 * 7)
self.deconv1 = nn.ConvTranspose2d(64, 64, kernel_size=3, stride=2, padding=1, output_padding=1)
self.deconv2 = nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1)
self.deconv3 = nn.ConvTranspose2d(32, 1, kernel_size=3, padding=1)
def forward(self, z):
x = torch.relu(self.fc(z))
x = x.view(-1, 64, 7, 7)
x = torch.relu(self.deconv1(x))
x = torch.relu(self.deconv2(x))
x = torch.sigmoid(self.deconv3(x))
return x
class VAE(nn.Module):
def __init__(self, latent_dim):
super(VAE, self).__init__()
self.encoder = Encoder(latent_dim)
self.decoder = Decoder(latent_dim)
self.sampling = Sampling()
def forward(self, x):
mu, logvar = self.encoder(x)
z = self.sampling(mu, logvar)
return self.decoder(z), mu, logvar
def loss_function(recon_x, x, mu, logvar): # reconstruction
BCE = binary_cross_entropy(recon_x, x, reduction='sum')
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
return BCE + KL