garage.tf.optimizers.penalty_lbfgs_optimizer¶
Penalized Limited-memory BFGS (L-BFGS) optimizer.
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class
PenaltyLbfgsOptimizer(max_opt_itr=20, initial_penalty=1.0, min_penalty=0.01, max_penalty=1000000.0, increase_penalty_factor=2, decrease_penalty_factor=0.5, max_penalty_itr=10, adapt_penalty=True)¶ Penalized Limited-memory BFGS (L-BFGS) optimizer.
Performs constrained optimization via penalized L-BFGS. The penalty term is adaptively adjusted to make sure that the constraint is satisfied.
Parameters: - max_opt_itr (int) – Maximum iteration for update.
- initial_penalty (float) – Initial penalty.
- min_penalty (float) – Minimum penalty allowed. Penalty will be clipped if lower than this value.
- max_penalty (float) – Maximum penalty allowed. Penalty will be clipped if higher than this value.
- increase_penalty_factor (float) – Factor to increase penalty in each penalty iteration.
- decrease_penalty_factor (float) – Factor to decrease penalty in each penalty iteration.
- max_penalty_itr (int) – Maximum penalty iterations to perform.
- adapt_penalty (bool) – Whether the penalty is adaptive or not. If false, penalty will not change.
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update_opt(self, loss, target, leq_constraint, inputs, constraint_name='constraint', name='PenaltyLbfgsOptimizer', **kwargs)¶ Construct operation graph for the optimizer.
Parameters: - loss (tf.Tensor) – Loss objective to minimize.
- target (object) – Target object to optimize. The object should implemenet get_params() and get_param_values.
- leq_constraint (tuple) – It contains a tf.Tensor and a float value. The tf.Tensor represents the constraint term, and the float value is the constraint value.
- inputs (list[tf.Tensor]) – List of input placeholders.
- constraint_name (str) – Constraint name for logging.
- name (str) – Name scope.
- kwargs (dict) – Extra unused keyword arguments. Some optimizers have extra input, e.g. KL constraint.
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loss(self, inputs)¶ The loss.
Parameters: inputs (list[numpy.ndarray]) – List of input values. Returns: Loss. Return type: float Raises: Exception– If loss function is None, i.e. not defined.