# NormLimitRegularizer¶

class NormLimitRegularizer(*, weight=1.0, apply_only_once=False, dim=-1, p=2.0, power_norm=True, max_norm=1.0, **kwargs)[source]

Bases: Regularizer

A regularizer which formulates a soft constraint on a maximum norm.

Initialize the regularizer.

Parameters:
• weight (float) – The relative weight of the regularization

• apply_only_once (bool) – Should the regularization be applied more than once after reset?

• dim (Optional[int]) – the dimension along which to calculate the Lp norm, cf. powersum_norm()

• p (float) – the parameter $$p$$ of the Lp norm, cf. powersum_norm()

• power_norm (bool) – whether to use the $$p$$ power of the norm instead

• max_norm (float) – the maximum norm until which no penalty is added

• kwargs – additional keyword-based parameters passed to Regularizer.__init__()

Methods Summary

 Compute the regularization term for one tensor.

Methods Documentation

forward(x)[source]

Compute the regularization term for one tensor.

Return type:

FloatTensor

Parameters:

x (FloatTensor) –