(self,input,label)
| 34 | self.fc_layer_final = torch.nn.Linear(in_features=1024,out_features=H * W) |
| 35 | |
| 36 | def forward(self,input,label): |
| 37 | x = self.fc_layer_1(input) |
| 38 | y = self.fc_layer_2(label) |
| 39 | x = torch.cat([x,y],dim=1) |
| 40 | x = self.fc_layer_3(x) |
| 41 | x = self.fc_layer_4(x) |
| 42 | out = torch.tanh(self.fc_layer_final(x)) |
| 43 | return out |
| 44 | |
| 45 | class Discriminator(torch.nn.Module): |
| 46 | def __init__(self,W = 28,H = 28,out_features = 1): |
nothing calls this directly
no outgoing calls
no test coverage detected