# Source code for lumin.nn.losses.basic_weighted

```
from typing import Optional
import torch.nn as nn
import torch
from torch import Tensor
__all__ = ['WeightedMSE', 'WeightedMAE', 'WeightedCCE']
[docs]class WeightedMSE(nn.MSELoss):
r'''
Class for computing Mean Squared-Error loss with optional weights per prediction.
For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss.
Arguments:
weight: sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss
Examples::
>>> loss = WeightedMSE()
>>>
>>> loss = WeightedMSE(weights)
'''
def __init__(self, weight:Optional[Tensor]=None):
super().__init__(reduction='mean' if weight is None else 'none')
self.weights = weight
[docs] def forward(self, input:Tensor, target:Tensor) -> Tensor:
r'''
Evaluate loss for given predictions
Arguments:
input: prediction tensor
target: target tensor
Returns:
(weighted) loss
'''
if self.weights is not None: return torch.mean(self.weights*super().forward(input, target))
else: return super().forward(input, target)
[docs]class WeightedMAE(nn.L1Loss):
r'''
Class for computing Mean Absolute-Error loss with optional weights per prediction.
For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss.
Arguments:
weight: sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss
Examples::
>>> loss = WeightedMAE()
>>>
>>> loss = WeightedMAE(weights)
'''
def __init__(self, weight:Optional[Tensor]=None):
super().__init__(reduction='mean' if weight is None else 'none')
self.weights = weight
[docs] def forward(self, input:Tensor, target:Tensor) -> Tensor:
r'''
Evaluate loss for given predictions
Arguments:
input: prediction tensor
target: target tensor
Returns:
(weighted) loss
'''
if self.weights is not None: return torch.mean(self.weights*super().forward(input, target))
else: return super().forward(input, target)
[docs]class WeightedCCE(nn.NLLLoss):
r'''
Class for computing Categorical Cross-Entropy loss with optional weights per prediction.
For compatability with using basic PyTorch losses, weights are passed during initialisation rather than when computing the loss.
Arguments:
weight: sample weights as PyTorch Tensor, to be used with data to be passed when computing the loss
Examples::
>>> loss = WeightedCCE()
>>>
>>> loss = WeightedCCE(weights)
'''
def __init__(self, weight:Optional[Tensor]=None):
super().__init__(reduction='mean')
self.weights = weight
[docs] def forward(self, input:Tensor, target:Tensor) -> Tensor:
r'''
Evaluate loss for given predictions
Arguments:
input: prediction tensor
target: target tensor
Returns:
(weighted) loss
'''
if self.weights is not None: return torch.mean(self.weights*super().forward(input, target))
else: return super().forward(input, target)
```