BCEWithLogitsLoss
- class BCEWithLogitsLoss(reduction: str = 'mean', pos_weight: None | float = None)[source]
Bases:
PointwiseLossThe binary cross entropy loss.
For label function \(l:\mathcal{E} \times \mathcal{R} \times \mathcal{E} \rightarrow \{0,1\}\) and interaction function \(f:\mathcal{E} \times \mathcal{R} \times \mathcal{E} \rightarrow \mathbb{R}\), the binary cross entropy loss is defined as:
\[L(h, r, t) = -(l(h,r,t) \cdot \log(\sigma(f(h,r,t))) + (1 - l(h,r,t)) \cdot \log(1 - \sigma(f(h,r,t))))\]where represents the logistic sigmoid function
\[\sigma(x) = \frac{1}{1 + \exp(-x)}\]Note
The softplus activation function \(h_{\text{softplus}}(x) = -\log(\sigma(x))\).
Thus, the problem is framed as a binary classification problem of triples, where the interaction functions’ outputs are regarded as logits.
Warning
This loss is not well-suited for translational distance models because these models produce a negative distance as score and cannot produce positive model outputs.
Note
The related
torchmodule istorch.nn.BCEWithLogitsLoss, but it can not be used interchangeably in PyKEEN because of the extended functionality implemented in PyKEEN’s loss functions.Initialize the loss criterion.
- Parameters:
Attributes Summary
The default strategy for optimizing the loss's hyper-parameters
synonyms of this loss
Methods Summary
forward(x, target[, weight])Calculate the point-wise loss.
Attributes Documentation
- hpo_default: ClassVar[Mapping[str, Any]] = {'pos_weight': {'high': 1024, 'log': True, 'low': 0.25, 'type': <class 'float'>}, 'reduction': {'choices': ['mean', 'sum'], 'type': 'categorical'}}
The default strategy for optimizing the loss’s hyper-parameters
Methods Documentation