LinearCyclicalScheduler#
- 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.
- Parameters
optimizer (Optimizer) – torch optimizer or any object with attribute
param_groupsas 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.
Note
If the scheduler is bound to an ‘ITERATION_*’ event, ‘cycle_size’ should usually be the number of batches in an epoch.
Examples:
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.
Methods
Method to get current optimizer's parameter values