(self, X)
| 1580 | self.model = nn.Sequential(self.layer1) |
| 1581 | |
| 1582 | def forward(self, X): |
| 1583 | self.X = X |
| 1584 | if not isinstance(X, torch.Tensor): |
| 1585 | self.X = torch.from_numpy(X) |
| 1586 | |
| 1587 | self.out1 = self.layer1(self.X) |
| 1588 | self.out1.retain_grad() |
| 1589 | |
| 1590 | def extract_grads(self, X): |
| 1591 | self.forward(X) |