(self, Xs)
| 350 | self.act_fn = act_fn |
| 351 | |
| 352 | def forward(self, Xs): |
| 353 | self.Xs = [] |
| 354 | x = Xs[0].copy() |
| 355 | if not isinstance(x, torch.Tensor): |
| 356 | x = torchify(x) |
| 357 | |
| 358 | self.prod = x.clone() |
| 359 | x.retain_grad() |
| 360 | self.Xs.append(x) |
| 361 | |
| 362 | for i in range(1, len(Xs)): |
| 363 | x = Xs[i] |
| 364 | if not isinstance(x, torch.Tensor): |
| 365 | x = torchify(x) |
| 366 | |
| 367 | x.retain_grad() |
| 368 | self.Xs.append(x) |
| 369 | self.prod *= x |
| 370 | |
| 371 | self.prod.retain_grad() |
| 372 | self.Y = self.act_fn(self.prod) |
| 373 | self.Y.retain_grad() |
| 374 | return self.Y |
| 375 | |
| 376 | def extract_grads(self, X): |
| 377 | self.forward(X) |
no test coverage detected