ParamScheduler

class ignite.handlers.param_scheduler.ParamScheduler(optimizer, param_name, save_history=False, param_group_index=None)

An abstract class for updating an optimizer’s parameter value during training.

Parameters

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

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

• 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

Parameter scheduler works independently of the internal state of the attached optimizer. More precisely, whatever the state of the optimizer (newly created or used by another scheduler) the scheduler sets defined absolute values.

Methods

simulate_values

Method to simulate scheduled values during num_events events.

classmethod simulate_values(num_events, **scheduler_kwargs)

Method to simulate scheduled values during num_events events.

Parameters

• num_events (int) – number of events during the simulation.

• scheduler_kwargs (Any) – parameter scheduler configuration kwargs.

Returns

event_index, value

Return type

List[List[int]]

Examples:

lr_values = np.array(LinearCyclicalScheduler.simulate_values(num_events=50, param_name='lr',
                                                             start_value=1e-1, end_value=1e-3,
                                                             cycle_size=10))

plt.plot(lr_values[:, 0], lr_values[:, 1], label="learning rate")
plt.xlabel("events")
plt.ylabel("values")
plt.legend()