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log_poisson_loss#

ivy.log_poisson_loss(true, pred, /, *, compute_full_loss=False, axis=-1, reduction='none', out=None)[source]#

Compute the log-likelihood loss between the prediction and the target under the assumption that the target has a Poisson distribution. Caveat: By default, this is not the exact loss, but the loss minus a constant term [log(z!)]. That has no effect for optimization, but does not play well with relative loss comparisons. To compute an approximation of the log factorial term, specify compute_full_loss=True to enable Stirling’s Approximation.

Parameters:

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

  • pred (Union[Array, NativeArray]) – input array containing Predicted labels.

  • compute_full_loss (bool, default: False) – whether to compute the full loss. If false, a constant term is dropped in favor of more efficient optimization. Default: False.

  • axis (int, default: -1) – the axis along which to compute the log-likelihood loss. If axis is -1, the log-likelihood loss will be computed along the last dimension. Default: -1.

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

  • 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 binary log-likelihood loss between the given distributions.

Examples

>>> x = ivy.array([0, 0, 1, 0])
>>> y = ivy.array([0.25, 0.25, 0.25, 0.25])
>>> print(ivy.log_poisson_loss(x, y))
ivy.array([1.28402555, 1.28402555, 1.03402555, 1.28402555])

>>> z = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(ivy.log_poisson_loss(x, z, reduction='mean'))
ivy.array(1.1573164)
Array.log_poisson_loss(self, target, /, *, compute_full_loss=False, axis=-1, reduction='none', out=None)[source]#

ivy.Array instance method variant of ivy.log_poisson_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.

  • compute_full_loss (bool, default: False) – whether to compute the full loss. If false, a constant term is dropped in favor of more efficient optimization. Default: False.

  • axis (int, default: -1) – the axis along which to compute the log-likelihood loss. If axis is -1, the log-likelihood loss will be computed along the last dimension. Default: -1.

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

  • 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 binary log-likelihood loss between the given distributions.

Examples

>>> x = ivy.array([0, 0, 1, 0])
>>> y = ivy.array([0.25, 0.25, 0.25, 0.25])
>>> loss = x.log_poisson_loss(y)
>>> print(loss)
ivy.array([1.28402555, 1.28402555, 1.03402555, 1.28402555])

>>> z = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> loss = x.log_poisson_loss(z, reduction='mean')
>>> print(loss)
ivy.array(1.1573164)
Container.log_poisson_loss(self, target, /, *, compute_full_loss=False, axis=-1, reduction='mean', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.log_poisson_loss. This method simply wraps the function, and so the docstring for ivy.log_poisson_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.

  • compute_full_loss (bool, default: False) – whether to compute the full loss. If false, a constant term is dropped in favor of more efficient optimization. Default: False.

  • axis (int, default: -1) – the axis along which to compute the log-likelihood loss. If axis is -1, the log-likelihood loss will be computed along the last dimension. Default: -1.

  • 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: 'none'.

  • 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.log_poisson_loss(y)
>>> print(z)
{
    a: ivy.array(3.3890561),
    b: ivy.array(123.413159)
}
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