KL Divergence¶

Module Interface¶

class torchmetrics.KLDivergence(log_prob=False, reduction='mean', **kwargs)[source]

$D_{KL}(P||Q) = \sum_{x\in\mathcal{X}} P(x) \log\frac{P(x)}{Q{x}}$

Where $P$ and $Q$ are probability distributions where $P$ usually represents a distribution over data and $Q$ is often a prior or approximation of $P$ . It should be noted that the KL divergence is a non-symetrical metric i.e. $D_{KL}(P||Q) \neq D_{KL}(Q||P)$ .

Parameters

p¶ – data distribution with shape [N, d]
q¶ – prior or approximate distribution with shape [N, d]
log_prob¶ (bool) – bool indicating if input is log-probabilities or probabilities. If given as probabilities, will normalize to make sure the distributes sum to 1.
reduction¶ (Literal[‘mean’, ‘sum’, ‘none’, None]) –
Determines how to reduce over the N/batch dimension:
- 'mean' [default]: Averages score across samples
- 'sum': Sum score across samples
- 'none' or None: Returns score per sample
kwargs¶ (Dict[str, Any]) – Additional keyword arguments, see Advanced metric settings for more info.

Raises

TypeError – If log_prob is not an bool.
ValueError – If reduction is not one of 'mean', 'sum', 'none' or None.

Note

Half precision is only support on GPU for this metric

Example

>>> import torch
>>> from torchmetrics.functional import kl_divergence
>>> p = torch.tensor([[0.36, 0.48, 0.16]])
>>> q = torch.tensor([[1/3, 1/3, 1/3]])
>>> kl_divergence(p, q)
tensor(0.0853)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

compute()[source]

Override this method to compute the final metric value from state variables synchronized across the distributed backend.

Return type: Tensor

update(p, q)[source]

Override this method to update the state variables of your metric class.

Return type: None

Functional Interface¶

torchmetrics.functional.kl_divergence(p, q, log_prob=False, reduction='mean')[source]

Computes KL divergence

$D_{KL}(P||Q) = \sum_{x\in\mathcal{X}} P(x) \log\frac{P(x)}{Q{x}}$

Where $P$ and $Q$ are probability distributions where $P$ usually represents a distribution over data and $Q$ is often a prior or approximation of $P$ . It should be noted that the KL divergence is a non-symetrical metric i.e. $D_{KL}(P||Q) \neq D_{KL}(Q||P)$ .

Parameters

p¶ (Tensor) – data distribution with shape [N, d]
q¶ (Tensor) – prior or approximate distribution with shape [N, d]
log_prob¶ (bool) – bool indicating if input is log-probabilities or probabilities. If given as probabilities, will normalize to make sure the distributes sum to 1
reduction¶ (Literal[‘mean’, ‘sum’, ‘none’, None]) –
Determines how to reduce over the N/batch dimension:
- 'mean' [default]: Averages score across samples
- 'sum': Sum score across samples
- 'none' or None: Returns score per sample

Example

>>> import torch
>>> p = torch.tensor([[0.36, 0.48, 0.16]])
>>> q = torch.tensor([[1/3, 1/3, 1/3]])
>>> kl_divergence(p, q)
tensor(0.0853)

Return type: Tensor