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mindspore.ops.l1_loss

mindspore.ops.l1_loss(input, target, reduction='mean')

Calculate the mean absolute error between the input value and the target value.

Assuming that the $x$ and $y$ are 1-D Tensor, length $N$, reduction is set to “none” , then calculate the loss of $x$ and $y$ without dimensionality reduction.

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 mean or sum, 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

• input (Tensor) – Predicted value, Tensor of any dimension.

• target (Tensor) – Target value, usually has the same shape as the input. If input and target have different shape, make sure they can broadcast to each other.

• reduction (str, optional) – Type of reduction to be applied to loss. The optional value is “mean”, “sum” or “none”. Default: 'mean' .

Returns

Tensor or Scalar, if reduction is “none”, return a Tensor with same shape and dtype as input. Otherwise, a scalar value will be returned.

Raises

• TypeError – If input is not a Tensor.

• TypeError – If target is not a Tensor.

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

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = ms.Tensor([[1, 2, 3], [4, 5, 6]], mstype.float32)
>>> target = ms.Tensor([[6, 5, 4], [3, 2, 1]], mstype.float32)
>>> output = ops.l1_loss(x, target, reduction="mean")
>>> print(output)
3.0