poisson_nll_loss#

ivy.poisson_nll_loss(input, target, *, log_input=True, full=False, eps=1e-08, reduction='mean')[source]#

Compute the Poisson Negative Log Likelihood Loss.

This function calculates the negative log likelihood loss between the input and target`under the assumption that the target follows a Poisson distribution. By default, the loss is not the exact loss, but the loss minus a constant term [log(z!)]. This omission does not affect optimization but can be significant for relative loss comparisons. The Stirling’s Approximation is used to approximate the log factorial term when `full is set to True.

Parameters:

input (Union[Array, NativeArray]) – Expectation of the underlying Poisson distribution.
target (Union[Array, NativeArray]) – Random sample from the Poisson distribution described by the input.
log_input (bool, default: True) – If True, the loss is computed as \(exp(input) - target * input\). If False, the loss is computed as \(input - target * log(input + eps)\). Default is True.
full (bool, default: False) – Whether to compute the full loss, i.e., to add the Stirling approximation term \(target * log(target) - target + 0.5 * log(2 * pi * target)\). Default is False.
eps (float, default: 1e-08) – Small value to prevent evaluation of log(0) when log_input is False. Default is 1e-8.
reduction (str, default: 'mean') – Specifies the reduction applied to the output. Options are ‘none’, ‘mean’, or ‘sum’. ‘none’: no reduction will be applied. ‘mean’: the output will be averaged. ‘sum’: the output will be summed. Default is ‘mean’.

Return type:

Array

Returns:

ret – An array of the same shape as input representing the Poisson Negative Log Likelihood Loss.

Raises:

ValueError – If the input and target tensors do not have the same shape.

Examples

>>> input_tensor = ivy.array([1, 2, 3, 4], dtype=ivy.float64)
>>> target_tensor = ivy.array([2, 2, 2, 2], dtype=ivy.float64)
>>> loss = ivy.poisson_nll_loss(input_tensor, target_tensor, log_input=False)
>>> print(loss)
ivy.array(0.91097307)

Array.poisson_nll_loss(self, target, *, log_input=True, full=False, eps=1e-08, reduction='mean')[source]#

Compute the Poisson Negative Log Likelihood Loss.

Parameters:

input – Expectation of the underlying Poisson distribution.
target (Union[Array, NativeArray]) – Random sample from the Poisson distribution described by the input.
log_input (bool, default: True) – If True, the loss is computed as \(exp(input) - target * input\). If False, the loss is computed as \(input - target * log(input + eps)\). Default is True.
full (bool, default: False) – Whether to compute the full loss, i.e., to add the Stirling approximation term \(target * log(target) - target + 0.5 * log(2 * pi * target)\). Default is False.
eps (float, default: 1e-08) – Small value to prevent evaluation of log(0) when log_input is False. Default is 1e-8.
reduction (str, default: 'mean') – Specifies the reduction applied to the output. Options are ‘none’, ‘mean’, or ‘sum’. ‘none’: no reduction will be applied. ‘mean’: the output will be averaged. ‘sum’: the output will be summed. Default is ‘mean’.

Return type:

Array

Returns:

ret – An array of the same shape as input representing the Poisson Negative Log Likelihood Loss.

Raises:

ValueError – If the input and target tensors do not have the same shape.

Examples

>>> input_tensor = ivy.array([1, 2, 3, 4], dtype=ivy.float64)
>>> target_tensor = ivy.array([2, 2, 2, 2], dtype=ivy.float64)
>>> loss = input_tensor.poisson_nll_loss(target_tensor, log_input=True)
>>> print(loss)
ivy.array(16.1977562)

Container.poisson_nll_loss(self, target, *, log_input=True, full=False, eps=1e-08, reduction='mean', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#

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

Parameters:

self (Union[Container, Array, NativeArray]) – input array or container containing input labels.
target (Union[Container, Array, NativeArray]) – input array or container containing the target labels.
log_input ([typing.Union[bool, ivy.Container]], default: True) – If True, the loss is computed as \(exp(input) - target * input\). If False, the loss is computed as \(input - target * log(input + eps)\). Default is True.
full ([typing.Union[bool, ivy.Container]], default: False) – Whether to compute the full loss, i.e., to add the Stirling approximation term \(target * log(target) - target + 0.5 * log(2 * pi * target)\). Default is False.
eps ([typing.Union[float, ivy.Container]], default: 1e-08) – Small value to prevent evaluation of log(0) when log_input is False. Default is 1e-8.
reduction ([typing.Union[str, ivy.Container]], default: 'mean') – Specifies the reduction applied to the output. Options are ‘none’, ‘mean’, or ‘sum’. ‘none’: no reduction will be applied. ‘mean’: the output will be averaged. ‘sum’: the output will be summed. Default is ‘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.

Return type:

Container

Returns:

ret – An array of the same shape as input representing the Poisson Negative Log Likelihood Loss.

Raises:

ValueError – If the input and target tensors do not have the same shape.

Examples

>>> x = ivy.Container(a=ivy.array([[1, 0, 2]], dtype=ivy.float32),
...              b=ivy.array([[3, 2, 1]], dtype=ivy.float32))
>>> y = ivy.Container(a=ivy.array([[0.6, 0.2, 0.3]], dtype=ivy.float32),
...              b=ivy.array([[0.8, 0.2, 0.2]], dtype=ivy.float32))
>>> z = x.poisson_nll_loss(y)
>>> print(z)
{
    a: ivy.array(3.30244565),
    b: ivy.array(9.06429195)
}