README.md

# clock-vae-mono-100x-v1
원하는 시간의 아날로그 시계 데이터를 생성하기 위해 만들어진 VAE 모델.

---

## name definition
- clock-vae : model name<br>
- mono : color type(color or mono)<br>
- 100x : image size(100x100)<br>
- v1 : version<br>


---
## model define code
```py
class ConditionalVAE(nn.Module):
    def __init__(self, input_dim, condition_dim, latent_dim):
        super(MonoClockVAEHandler.ConditionalVAE, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(input_dim + condition_dim, 400),
            nn.ReLU(),
            nn.Linear(400, 200),
            nn.ReLU(),
        )
        self.fc_mu = nn.Linear(200, latent_dim)
        self.fc_logvar = nn.Linear(200, latent_dim)
    
        self.decoder = nn.Sequential(
            nn.Linear(latent_dim + condition_dim, 200),
            nn.ReLU(),
            nn.Linear(200, 400),
            nn.ReLU(),
            nn.Linear(400, input_dim),
            nn.Sigmoid()
        )
    
    def encode(self, x, condition):
        x = x.view(x.size(0), -1)
        condition = condition.view(condition.size(0), -1)
        x_cond = torch.cat([x, condition], dim=1)
        h = self.encoder(x_cond)
        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, condition):
        z_cond = torch.cat([z, condition], dim=1)
        return self.decoder(z_cond)
    
    def forward(self, x, condition):
        mu, logvar = self.encode(x, condition)
        z = self.reparameterize(mu, logvar)
        return self.decode(z, condition), mu, logvar
```