# Function Differences with torch.nn.L1Loss [](https://gitee.com/mindspore/docs/blob/r2.0/docs/mindspore/source_en/note/api_mapping/pytorch_diff/L1Loss.md) ## torch.nn.L1Loss ```text torch.nn.L1Loss(size_average=None, reduce=None, reduction='mean')(input, target) -> Tensor ``` For more information, see [torch.nn.L1Loss](https://pytorch.org/docs/1.8.1/generated/torch.nn.L1Loss.html). ## mindspore.nn.L1Loss ```text mindspore.nn.L1Loss(reduction='mean')(logits, labels) -> Tensor ``` For more information, see [mindspore.nn.L1Loss](https://mindspore.cn/docs/en/r2.0/api_python/nn/mindspore.nn.L1Loss.html). ## Differences PyTorch: L1Loss is used to calculate the average absolute error between the predicted and target values. MindSpore: Includes PyTorch function, which can still run when logits and labels have different shapes but can broadcast to each other, while PyTorch cannot. | Categories | Subcategories |PyTorch | MindSpore | Difference | | ---- | ----- | ------- | --------- | ------------- | | Parameters | Parameter 1 | size_average | - | Deprecated, function taken over by reduction | | | Parameter 2 | reduce | - | Deprecated, function taken over by reduction| | | Parameter 3 | reduction | reduction | - | | Input | Input 1 | input | logits | Same function, different parameter names | | | Input 2 | target | labels | Same function, different parameter names | ### Code Example > The two APIs achieve the same function and have the same usage. ```python # PyTorch import torch import torch.nn as nn loss = nn.L1Loss() input = torch.tensor([2, 2, 3], dtype=torch.float32) target = torch.tensor([1, 2, 2], dtype=torch.float32) output = loss(input, target) output = output.detach().numpy() print(output) # 0.6666667 # MindSpore import mindspore from mindspore import Tensor, nn import numpy as np loss = nn.L1Loss() logits = Tensor(np.array([2, 2, 3]), mindspore.float32) labels = Tensor(np.array([1, 2, 2]), mindspore.float32) output = loss(logits, labels) print(output) # 0.6666667 ```