Regularizer¶
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class
ampligraph.latent_features.Regularizer(hyperparam_dict, verbose=False)¶ Abstract class for Regularizer.
Methods
__init__(hyperparam_dict[, verbose])Initialize the regularizer.
get_state(param_name)Get the state value.
_init_hyperparams(hyperparam_dict)Initializes the hyperparameters needed by the algorithm.
apply(trainable_params)Interface to external world.
_apply(trainable_params)Apply the regularization function.
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__init__(hyperparam_dict, verbose=False)¶ Initialize the regularizer.
- Parameters
hyperparam_dict (dict) – dictionary of hyperparams (Keys are described in the hyperparameters section)
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get_state(param_name)¶ Get the state value.
- Parameters
param_name (string) – name of the state for which one wants to query the value
- Returns
the value of the corresponding state
- Return type
param_value
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_init_hyperparams(hyperparam_dict)¶ 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
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apply(trainable_params)¶ Interface to external world. This function performs input checks, input pre-processing, and and applies the loss function.
- Parameters
trainable_params (list, shape [n]) – List of trainable params that should be reqularized
- Returns
loss – Regularization Loss
- Return type
tf.Tensor
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_apply(trainable_params)¶ Apply the regularization function. Every inherited class must implement this function.
(All the TF code must go in this function.)
- Parameters
trainable_params (list, shape [n]) – List of trainable params that should be reqularized
- Returns
loss – Regularization Loss
- Return type
tf.Tensor
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