(self,x)
| 22 | self.z_log_var = torch.nn.Linear(in_features=256,out_features=latent_dim) |
| 23 | |
| 24 | def forward(self,x): |
| 25 | x = x.view(-1,784) |
| 26 | x = self.initial_dense(x) |
| 27 | |
| 28 | z_mean = self.z_mean(x) |
| 29 | z_log_var = self.z_log_var(x) |
| 30 | |
| 31 | return z_mean,z_log_var |
| 32 | |
| 33 | class Decoder(torch.nn.Module): |
| 34 | def __init__(self,hidden_dim = 256,latent_dim = 2,num_features = 784): |
nothing calls this directly
no outgoing calls
no test coverage detected