netket.callbacks.ConvergenceStopping

netket.callbacks.ConvergenceStopping#

class netket.callbacks.ConvergenceStopping[source]#

Bases: Pytree

A simple callback to stop the optimisation when the monitored quantity gets below a certain threshold for at least patience steps.

Inheritance
Inheritance diagram of netket.callbacks.ConvergenceStopping
__init__(target, monitor='mean', *, smoothing_window=10, patience=10)[source]#

Construct a callback stopping the optimisation when the monitored quantity gets below a certain threshold for at least patience steps.

Parameters:

  • target (float) – the threshold value for the monitored quantity. Training will stop if the driver drops below this value.

  • monitor (str) – a string with the name of the quantity to be monitored. This is applied to the standard loss optimised by a driver, such as the Energy for the VMC driver. Should be one of ‘mean’, ‘variance’, ‘error_of_mean’ (default: ‘mean’).

  • smoothing_window (int) – an integer number of steps over which the monitored value is averaged before comparing to target.

  • patience (int) – Number of steps to wait before stopping the execution after the tracked quantity drops below the target value (default 0, meaning that it stops immediately).

Attributes
target: float#

Target value for the monitored quantity. Training will stop if the driver drops below this value.

monitor: str#

Loss statistic to monitor. Should be one of ‘mean’, ‘variance’, ‘error_of_mean’.

smoothing_window: int#

The loss is smoothed over the last smoothing_window iterations to reduce statistical fluctuations

patience: int#

The loss must be consistently below this value for this number of iterations in order to stop the optimisation.

Methods
__call__(step, log_data, driver)[source]#

A boolean function that determines whether or not to stop training.

Parameters:

  • step – An integer corresponding to the step (iteration or epoch) in training.

  • log_data – A dictionary containing log data for training.

  • driver – A NetKet variational driver.

Returns:

A boolean. If True, training continues, else, it does not.

replace(**kwargs)[source]#

Replace the values of the fields of the object with the values of the keyword arguments. If the object is a dataclass, dataclasses.replace will be used. Otherwise, a new object will be created with the same type as the original object.

Return type:

TypeVar(P, bound= Pytree)

Parameters:

  • self (P)

  • kwargs (Any)

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