Scales tensor values to range [0,1] using min-max normalization. Args: tensor: Input tensor epsilon: Small value to prevent division by zero (default: 1e-6) Returns: Normalized tensor with values in [0,1]
(tensor: Tensor, epsilon: float = 1e-6)
| 28 | |
| 29 | |
| 30 | def MinMaxNormalization(tensor: Tensor, epsilon: float = 1e-6) -> Tensor: |
| 31 | """ |
| 32 | Scales tensor values to range [0,1] using min-max normalization. |
| 33 | |
| 34 | Args: |
| 35 | tensor: Input tensor |
| 36 | epsilon: Small value to prevent division by zero (default: 1e-6) |
| 37 | |
| 38 | Returns: |
| 39 | Normalized tensor with values in [0,1] |
| 40 | """ |
| 41 | tensor = smart_detect_inf(tensor) |
| 42 | min_tensor = tensor.min() |
| 43 | max_tensor = tensor.max() |
| 44 | range_tensor = max_tensor - min_tensor |
| 45 | return tensor.add_(-min_tensor).div_(range_tensor + epsilon) |
| 46 | |
| 47 | |
| 48 | def update_running_avg(new: Tensor, current: Tensor, gamma: float): |
no test coverage detected