mindspore.nn.MAELoss

class mindspore.nn.MAELoss(reduction='mean')

MAELoss creates a criterion to measure the average absolute error between $x$ and $y$ element-wise, where $x$ is the input and $y$ is the labels.

For simplicity, let $x$ and $y$ be 1-dimensional Tensor with length $N$, the unreduced loss (i.e. with argument reduction set to 'none') of $x$ and $y$ is given as:

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

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) - Tensor of shape $(M,\ast )$ where $\ast$ means, any number of additional dimensions.

• labels (Tensor) - Tensor of shape $(N,\ast )$, 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, weighted loss float tensor, the shape is zero if reduction is 'mean' or 'sum' ., while the shape of output is the broadcasted shape if reduction is 'none'.

Raises

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> # Case 1: logits.shape = labels.shape = (3,)
>>> loss = nn.MAELoss()
>>> 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 = nn.MAELoss(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.]]