BCEWithLogitsLoss
- class BCEWithLogitsLoss(reduction='mean')[source]
Bases:
PointwiseLoss
The 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
torch
module 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.
- Parameters:
reduction (
str
) – the reduction, cf. _Loss.__init__
Attributes Summary
synonyms of this loss
Methods Summary
forward
(scores, labels)Define the computation performed at every call.
Attributes Documentation
Methods Documentation
- forward(scores, labels)[source]
Define the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type:
FloatTensor
- Parameters:
scores (FloatTensor) –
labels (FloatTensor) –