# l1_loss#

ivy.l1_loss(input, target, /, *, reduction='mean', out=None)[source]#

Compute L1 loss (Mean Absolute Error - MAE) between targeticted and input values.

Parameters:
• input (Union[ivy.Array, ivy.NativeArray]) – Input array containing input values.

• target (Union[ivy.Array, ivy.NativeArray]) – Input array containing targeted values.

• reduction (str, optional) – Reduction method for the output loss. Options: “none” (no reduction), “mean” (mean of losses), “sum” (sum of losses). Default: “mean”.

• out (Optional[ivy.Array], optional) – Optional output array for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Array`

Returns:

ivy.Array – The L1 loss (MAE) between the given input and targeticted values.

Examples

```>>> x = ivy.array([1.0, 2.0, 3.0])
>>> y = ivy.array([0.5, 2.5, 2.0])
>>> print(ivy.l1_loss(x, y))
ivy.array(0.6)
>>> a = ivy.array([[1.0, 2.0], [3.0, 4.0]])
>>> b = ivy.array([[0.5, 1.5], [2.5, 3.5]])
>>> print(ivy.l1_loss(a, b))
ivy.array(0.5)
```
Array.l1_loss(self, target, /, *, reduction='mean', out=None)[source]#

ivy.Array instance method variant of ivy.l1_loss. This method simply wraps the function, and so the docstring for ivy.l1_loss also applies to this method with minimal changes.

Parameters:
• self (`Union`[`Array`, `NativeArray`]) – input array containing true labels.

• target (`Union`[`Array`, `NativeArray`]) – input array containing targeted labels.

• reduction (`Optional`[`str`], default: `'mean'`) – `'mean'`: The output will be averaged. `'sum'`: The output will be summed. `'none'`: No reduction will be applied to the output. Default: `'mean'`.

• out (`Optional`[`Array`], default: `None`) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Array`

Returns:

ret – The L1 loss between the input array and the targeticted values.

Examples

```>>> x = ivy.array([1.0, 2.0, 3.0])
>>> y = ivy.array([0.7, 1.8, 2.9])
>>> z = x.l1_loss(y)
>>> print(z)
ivy.array(0.20000000000000004)
```
Container.l1_loss(self, target, /, *, reduction='mean', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.l1_loss. This method simply wraps the function, and so the docstring for ivy.l1_loss also applies to this method with minimal changes.

Parameters:
• self (`Container`) – input container.

• target (`Union`[`Container`, `Array`, `NativeArray`]) – input array or container containing the targeticted values.

• reduction (`Optional`[`Union`[`str`, `Container`]], default: `'mean'`) – `'mean'`: The output will be averaged. `'sum'`: The output will be summed. `'none'`: No reduction will be applied to the output. Default: `'mean'`.

• key_chains (`Optional`[`Union`[`List`[`str`], `Dict`[`str`, `str`], `Container`]], default: `None`) – The key-chains to apply or not apply the method to. Default is `None`.

• to_apply (`Union`[`bool`, `Container`], default: `True`) – If input, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is `input`.

• prune_unapplied (`Union`[`bool`, `Container`], default: `False`) – Whether to prune key_chains for which the function was not applied. Default is `False`.

• map_sequences (`Union`[`bool`, `Container`], default: `False`) – Whether to also map method to sequences (lists, tuples). Default is `False`.

• out (`Optional`[`Container`], default: `None`) – optional output container, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

`Container`

Returns:

ret – The L1 loss between the input array and the targeticted values.

Examples

```>>> x = ivy.Container(a=ivy.array([1, 2, 3]), b=ivy.array([4, 5, 6]))
>>> y = ivy.Container(a=ivy.array([2, 2, 2]), b=ivy.array([5, 5, 5]))
>>> z = x.l1_loss(y)
>>> print(z)
{
a: ivy.array(1.),
b: ivy.array(0.)
}
```