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class Scheduler:
 | def __init__(
 |     self,
 |     optimizer: torch.optim.Optimizer,
 |     param_group_field: str,
 |     last_epoch: int = -1
 | ) -> None

A Scheduler is a generalization of PyTorch learning rate schedulers.

A scheduler can be used to update any field in an optimizer's parameter groups, not just the learning rate.

During training using the AllenNLP Trainer, this is the API and calling sequence for step and step_batch::

scheduler = ... # creates scheduler

batch_num_total = 0 for epoch in range(num_epochs): for batch in batchs_in_epoch: # compute loss, update parameters with current learning rates # call step_batch AFTER updating parameters batch_num_total += 1 scheduler.step_batch(batch_num_total) # call step() at the END of each epoch scheduler.step(validation_metrics, epoch)


class Scheduler:
 | ...
 | def state_dict(self) -> Dict[str, Any]

Returns the state of the scheduler as a dict.


class Scheduler:
 | ...
 | def load_state_dict(self, state_dict: Dict[str, Any]) -> None

Load the schedulers state.


  • state_dict : Dict[str, Any]
    Scheduler state. Should be an object returned from a call to state_dict.


class Scheduler:
 | ...
 | def get_values(self)


class Scheduler:
 | ...
 | def step(self, metric: float = None) -> None


class Scheduler:
 | ...
 | def step_batch(self, batch_num_total: int = None) -> None

By default, a scheduler is assumed to only update every epoch, not every batch. So this does nothing unless it's overriden.