netket.callbacks.EarlyStopping#
- class netket.callbacks.EarlyStopping[source]#
Bases:
object
A simple callback to stop NetKet if there are no more improvements in the training. based on driver._loss_name.
- Inheritance
- Attributes
-
baseline:
Optional
[float
] = None# Baseline value for the monitored quantity. Training will stop if the driver hits the baseline.
-
min_reldelta:
float
= 0.0# Minimum relative change in the monitored quantity to qualify as an improvement.
This behaves similarly to min_delta but is more useful for intensive quantities that converge to 0, where absolute tolerances might not be effective.
-
baseline:
- 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(**updates)#
Returns a new object replacing the specified fields with new values.