class ignite.handlers.param_scheduler.LinearCyclicalScheduler(optimizer, param_name, start_value, end_value, cycle_size, cycle_mult=1.0, start_value_mult=1.0, end_value_mult=1.0, save_history=False, param_group_index=None)[source]#

Linearly adjusts param value to ‘end_value’ for a half-cycle, then linearly adjusts it back to ‘start_value’ for a half-cycle.

  • optimizer (Optimizer) – torch optimizer or any object with attribute param_groups as a sequence.

  • param_name (str) – name of optimizer’s parameter to update.

  • start_value (float) – value at start of cycle.

  • end_value (float) – value at the middle of the cycle.

  • cycle_size (int) – length of cycle.

  • cycle_mult (float) – ratio by which to change the cycle_size at the end of each cycle (default=1).

  • start_value_mult (float) – ratio by which to change the start value at the end of each cycle (default=1.0).

  • end_value_mult (float) – ratio by which to change the end value at the end of each cycle (default=1.0).

  • save_history (bool) – whether to log the parameter values to engine.state.param_history, (default=False).

  • param_group_index (Optional[int]) – optimizer’s parameters group to use.


If the scheduler is bound to an ‘ITERATION_*’ event, ‘cycle_size’ should usually be the number of batches in an epoch.


from ignite.handlers.param_scheduler import LinearCyclicalScheduler

scheduler = LinearCyclicalScheduler(optimizer, 'lr', 1e-3, 1e-1, len(train_loader))
trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)
# Linearly increases the learning rate from 1e-3 to 1e-1 and back to 1e-3
# over the course of 1 epoch

New in version 0.4.5.



Method to get current optimizer's parameter values


Method to get current optimizer’s parameter values


list of params, or scalar param

Return type