MultiStepStateScheduler

class ignite.handlers.state_param_scheduler.MultiStepStateScheduler(initial_value, gamma, milestones, param_name, save_history=False)

Update a parameter during training by using a multi step function. The function decays the parameter value by gamma once the number of steps reaches one of the milestones. Based on MultiStepLR from PyTorch. https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.MultiStepLR.html

Parameters

• initial_value (float) – Starting value of the parameter.

• gamma (float) – Multiplicative factor of parameter value decay.

• milestones (List[int]) – List of step indices. Must be increasing.

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

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

Examples

...
engine = Engine(train_step)

param_scheduler = MultiStepStateScheduler(
    param_name="param", initial_value=10, gamma=0.99, milestones=[3, 6],
)

param_scheduler.attach(engine, Events.EPOCH_COMPLETED)

# basic handler to print scheduled state parameter
engine.add_event_handler(Events.EPOCH_COMPLETED, lambda _ : print(engine.state.param))

engine.run([0] * 8, max_epochs=10)

New in version 0.4.7.

Methods

get_param

Method to get current parameter values

get_param()

Method to get current parameter values

Returns

list of params, or scalar param

Return type

Union[List[float], float]