BCEWithLogitsLoss¶

class
BCEWithLogitsLoss
(size_average=None, reduce=None, reduction='mean')[source]¶ Bases:
pykeen.losses.PointwiseLoss
A module for 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
\[\sigma(x) = \frac{1}{1 + \exp(x)}\]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 wellsuited for translational distance models because these models produce a negative distance as score and cannot produce positive model outputs.
See also
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Attributes Summary
Methods Summary
forward
(scores, labels)Defines the computation performed at every call.
Attributes Documentation
Methods Documentation

forward
(scores, labels)[source]¶ Defines 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
