変分オートエンコーダ(VAE)

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

transform = transforms.Compose([
    transforms.ToTensor(),
])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)

# ==== VAE model ====
class VAE(nn.Module):
    def __init__(self, input_dim=784, hidden_dim=400, latent_dim=20):
        super(VAE, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc_mu = nn.Linear(hidden_dim, latent_dim)
        self.fc_logvar = nn.Linear(hidden_dim, latent_dim)

        self.fc2 = nn.Linear(latent_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, input_dim)

    def encode(self, x):
        h = torch.relu(self.fc1(x))
        mu = self.fc_mu(h)
        logvar = self.fc_logvar(h)
        return mu, logvar

    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 = torch.relu(self.fc2(z))
        return torch.sigmoid(self.fc3(h))

    def forward(self, x):
        mu, logvar = self.encode(x)
        z = self.reparameterize(mu, logvar)
        return self.decode(z), mu, logvar

# ===== model training =====
model = VAE()
optimizer = optim.Adam(model.parameters(), lr=1e-3)

def loss_function(recon_x, x, mu, logvar):
    BCE = nn.functional.binary_cross_entropy(recon_x, x, reduction='sum')
    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
    return BCE + KLD

# ===== training loop =====
epochs = 5
for epoch in range(epochs):
    model.train()
    train_loss = 0
    for batch_idx, (data, _) in enumerate(train_loader):
        data = data.view(-1, 784)  # Flatten the input
        optimizer.zero_grad()
        recon_batch, mu, logvar = model(data)
        loss = loss_function(recon_batch, data, mu, logvar)
        loss.backward()
        train_loss += loss.item()
        optimizer.step()
    
    print(f'Epoch {epoch + 1}, Loss: {train_loss / len(train_loader.dataset):.4f}')

# ===== new sample creating =====
model.eval()
with torch.no_grad():
    z = torch.randn(16, 20)  # Generate random latent vectors
    samples = model.decode(z).view(-1, 1, 28, 28)  # Decode to images

$ python3 vae.py
Epoch 1, Loss: 164.1793
Epoch 2, Loss: 121.6226
Epoch 3, Loss: 114.7562
Epoch 4, Loss: 111.7122
Epoch 5, Loss: 109.9405

Loss がエポックごとに下がっている → 学習が進んでいる証拠
VAE の Loss は「再構築誤差 (BCE) ＋ KLダイバージェンス」なので、単純な分類モデルの Accuracy とは違って「小さくなるほど良い」という見方