huber_loss#

ivy.huber_loss(true, pred, /, *, delta=1.0, reduction='mean', out=None)[source]#

Compute the Huber loss (smooth L1 loss) between true and predicted values.

Parameters:

true (array_like) – The true (ground truth) values.
pred (array_like) – The predicted values by the model.
delta (float, optional) – The threshold parameter that determines the point where the loss transitions fro -m squared error to absolute error. Default is 1.0.
reduction (str, optional) – The type of reduction to apply to the loss. Possible values are “mean” (default) and “sum”.
out (array_like, optional) – Optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret (array_like) – The Huber loss between the true and predicted values.

Examples

>>> true = ivy.array([2, 4, 7, 1])
>>> pred = ivy.array([2.5, 3.5, 8, 0.8])
>>> huber_loss(true, pred, delta=1.0)
ivy.array([0.125, 0.125, 0.5  , 0.125])

>>> huber_loss(true, pred, delta=2.0)
ivy.array([0.125, 0.125, 0.5  , 0.2  ])

>>> huber_loss(true, pred, delta=0.5)
ivy.array([0.25 , 0.25 , 0.   , 0.125])

Array.huber_loss(self, target, /, *, reduction='mean', delta=1.0, out=None)[source]#

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

Parameters:

self (Array) – input array containing true labels.
target (Union[Array, NativeArray]) – input array containing targeted labels.
reduction (str, optional) – The type of reduction to apply to the loss. Possible values are “mean” (default) and “sum”.
delta (Optional[float], default: 1.0) – The threshold parameter that determines the point where the loss transitions from squared error to absolute error. Default is 1.0.
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 Huber loss between the true and predicted values.

Examples

>>> true = ivy.array([2, 4, 7, 1])
>>> pred = ivy.array([2.5, 3.5, 8, 0.8])
>>> loss = true.huber_loss(pred, delta=1.0)
>>> print(loss)
ivy.array([0.125, 0.125, 0.5  , 0.125])

Container.huber_loss(self, pred, /, *, delta=1.0, reduction='mean', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

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

Parameters:

self (Container) – true container containing true labels.
pred (Union[Container, Array, NativeArray]) – true array or container containing the predicted labels.
delta (Optional[Union[float, Container]], default: 1.0) – The threshold parameter that determines the point where the loss transitions from squared error to absolute error. Default is 1.0.
reduction (str, optional) – The type of reduction to apply to the loss. Possible values are “mean” (default) and “sum”.
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 true, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is true.
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 trues broadcast to.

Return type:

Container

Returns:

ret – The Huber loss between the true and predicted values.

Examples

>>> x = ivy.Container(a=ivy.array([1, 0, 3]), b=ivy.array([0, 0, 2]))
>>> y = ivy.Container(a=ivy.array([1.5, 0.2, 2.8]), b=ivy.array([0.5, 0.2, 1.9])
)
>>> z = x.huber_loss(y, delta=1.0)
>>> print(z)
{
    a: ivy.array(0.0575),
    b: ivy.array(0.005)
}