(tensor: Tensor, epsilon: float = 1e-12)
| 6 | |
| 7 | |
| 8 | def MinMaxNormalization(tensor: Tensor, epsilon: float = 1e-12) -> Tensor: |
| 9 | min_tensor = tensor.min() |
| 10 | max_tensor = tensor.max() |
| 11 | range_tensor = max_tensor - min_tensor |
| 12 | return tensor.add_(-min_tensor).div_(range_tensor + epsilon) |
| 13 | |
| 14 | |
| 15 | def update_running_avg(new: Tensor, current: Dict[Module, Tensor], gammas: list): |
no outgoing calls
no test coverage detected