LPRegularizer¶
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class
ampligraph.latent_features.
LPRegularizer
(hyperparam_dict={'lambda': 1e-05, 'p': 2}, verbose=False)¶ Performs LP regularization
\[\mathcal{L}(Reg) = \sum_{i=1}^{n} \lambda_i * \mid w_i \mid_p\]where n is the number of model parameters, p is the p-norm and \(\lambda\) is the regularization weight.
p==1 does L1 regularization; p==2 does L2 regularization and so on.
Methods
__init__
([hyperparam_dict, verbose])Initializes the hyperparameters needed by the algorithm. -
__init__
(hyperparam_dict={'lambda': 1e-05, 'p': 2}, verbose=False)¶ Initializes the hyperparameters needed by the algorithm.
Parameters: hyperparam_dict (dictionary) – Consists of key value pairs. The regularizer will check the keys to get the corresponding params:
- lambda: float. Weight of regularization loss for each parameter (default: 1e-5)
- p: int: norm (default: 2)
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