(self, x)
| 47 | nn.init.constant_(m.bias, 0) |
| 48 | |
| 49 | def forward(self, x): |
| 50 | probability = [] |
| 51 | if self.shared_projected_layer is None: |
| 52 | for classifier in self.classifier_list: |
| 53 | logit = classifier(x) |
| 54 | probability.append(self._softmax(logit)) |
| 55 | else: |
| 56 | x = self.shared_projected_layer(x) |
| 57 | for classifier in self.classifier_list: |
| 58 | logit = classifier(x) |
| 59 | probability.append(self._softmax(logit)) |
| 60 | |
| 61 | return probability |
| 62 | |
| 63 | |
| 64 | class Mlp(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected