mindspore.mint.nn.functional.l1_loss

mindspore.mint.nn.functional.l1_loss(input, target, reduction='mean')[source]

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

Assuming that the \(x\) and \(y\) are the predicted value and target value, both are one-dimensional tensors of length \(N\), length \(N\), reduction is set to 'none' , then calculate the loss of \(x\) and \(y\) without dimensionality reduction.

The formula is as follows:

\[\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad \text{with } l_n = \left| x_n - y_n \right|,\]

where \(N\) is the batch size.

If reduction is 'mean' or 'sum' , then:

\[\begin{split}\ell(x, y) = \begin{cases} \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{'sum'.} \end{cases}\end{split}\]
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 shapes, make sure they can broadcast to each other.

  • 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. Notice: At least one of the input and target is float type when the reduction is 'mean' .

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

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

Examples

>>> from mindspore import Tensor, mint
>>> from mindspore import dtype as mstype
>>> x = Tensor([[1, 2, 3], [4, 5, 6]], mstype.float32)
>>> target = Tensor([[6, 5, 4], [3, 2, 1]], mstype.float32)
>>> output = mint.nn.functional.l1_loss(x, target, reduction="mean")
>>> print(output)
3.0