Update the layer parameters using the accrued gradients and layer optimizer. Flush all gradients once the update is complete.
(self, cur_loss=None)
| 74 | self.gradients[k] = np.zeros_like(v) |
| 75 | |
| 76 | def update(self, cur_loss=None): |
| 77 | """ |
| 78 | Update the layer parameters using the accrued gradients and layer |
| 79 | optimizer. Flush all gradients once the update is complete. |
| 80 | """ |
| 81 | assert self.trainable, "Layer is frozen" |
| 82 | self.optimizer.step() |
| 83 | for k, v in self.gradients.items(): |
| 84 | if k in self.parameters: |
| 85 | self.parameters[k] = self.optimizer(self.parameters[k], v, k, cur_loss) |
| 86 | self.flush_gradients() |
| 87 | |
| 88 | def set_params(self, summary_dict): |
| 89 | """ |
nothing calls this directly
no test coverage detected