(self, x: torch.Tensor)
| 126 | self.decodeL = nn.Linear(self.latent_size, linear_size) |
| 127 | |
| 128 | def encode_forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: |
| 129 | x = self.encode(x) |
| 130 | x = self.intermediate(x) |
| 131 | x = x.view(x.shape[0], -1) |
| 132 | mu = self.mu(x) |
| 133 | logvar = self.logvar(x) |
| 134 | return mu, logvar |
| 135 | |
| 136 | def decode_forward(self, z: torch.Tensor, use_sigmoid: bool = True) -> torch.Tensor: |
| 137 | x = F.relu(self.decodeL(z)) |