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mindspore.mint.nn.L1Loss

class mindspore.mint.nn.L1Loss(reduction='mean')

L1Loss is used to calculate the mean absolute error between the predicted value and the target value.

Assuming that the $x$ and $y$ are 1-D Tensor, length $N$, then calculate the loss of $x$ and $y$ without dimensionality reduction (the reduction parameter is set to 'none' ). The formula is as follows:

$$\ell (x,y)=L=\{l_1,\dots ,l_N{\}}^{\mathrm{\top }},\text{with }{l}_n=|x_n-y_n|,$$

where $N$ is the batch size. If reduction is not 'none' , then:

$$\begin{array}{r}\ell (x,y)=\{\begin{array}{ll}\mathrm{mean}(L),& \text{if reduction}=\text{'mean';}\\ \mathrm{sum}(L),& \text{if reduction}=\text{'sum'.}\end{array}\end{array}$$

Parameters

reduction (str, optional) –

Apply specific reduction method to the output: 'none' , 'mean' , 'sum' . Default: 'mean' .

• 'none': no reduction will be applied.

• 'mean': compute and return the mean of elements in the output.

• 'sum': the output elements will be summed.

Inputs:

• logits (Tensor) - Predicted value, Tensor of any dimension.

• labels (Tensor) - Target value, same shape as the logits in common cases. However, it supports the shape of logits is different from the shape of labels and they should be broadcasted to each other.

Outputs:

Tensor, data type is float.

Raises

• ValueError – If reduction is not one of 'none' , 'mean' or 'sum' .

• ValueError – If logits and labels have different shapes and cannot be broadcasted to each other.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> from mindspore import Tensor, mint
>>> import numpy as np
>>> # Case 1: logits.shape = labels.shape = (3,)
>>> loss = mint.nn.L1Loss()
>>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> labels = Tensor(np.array([1, 2, 2]), mindspore.float32)
>>> output = loss(logits, labels)
>>> print(output)
0.33333334
>>> # Case 2: logits.shape = (3,), labels.shape = (2, 3)
>>> loss = mint.nn.L1Loss(reduction='none')
>>> logits = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> labels = Tensor(np.array([[1, 1, 1], [1, 2, 2]]), mindspore.float32)
>>> output = loss(logits, labels)
>>> print(output)
[[0. 1. 2.]
 [0. 0. 1.]]