Regularizers
Regularization in PyKEEN.
Classes
|
A simple L_p norm based regularizer. |
|
A regularizer which does not perform any regularization. |
|
A convex combination of regularizers. |
|
A simple x^p based regularizer. |
|
A regularizer for the soft constraints in TransH. |
Class Inheritance Diagram
Base Classes
- class Regularizer(weight=1.0, apply_only_once=False, parameters=None)[source]
A base class for all regularizers.
Instantiate the regularizer.
- Parameters
weight (
float
) – The relative weight of the regularizationapply_only_once (
bool
) – Should the regularization be applied more than once after reset?parameters (
Optional
[Iterable
[Parameter
]]) – Specific parameters to track. if none given, it’s expected that your model automatically delegates to theupdate()
function.
- apply_only_once: bool
Should the regularization only be applied once? This was used for ConvKB and defaults to False.
- abstract forward(x)[source]
Compute the regularization term for one tensor.
- Return type
FloatTensor
- classmethod get_normalized_name()[source]
Get the normalized name of the regularizer class.
- Return type
- hpo_default: ClassVar[Mapping[str, Any]]
The default strategy for optimizing the regularizer’s hyper-parameters
- pop_regularization_term()[source]
Return the weighted regularization term, and reset the regularize afterwards.
- Return type
FloatTensor
- regularization_term: torch.FloatTensor
The current regularization term (a scalar)
- property term: torch.FloatTensor
Return the weighted regularization term.
- Return type
FloatTensor
- weight: torch.FloatTensor
The overall regularization weight