PiecewiseLinear#
-
class
ignite.handlers.param_scheduler.PiecewiseLinear(optimizer, param_name, milestones_values, save_history=False, param_group_index=None)[source]# Piecewise linear parameter scheduler
- Parameters
optimizer (torch.optim.optimizer.Optimizer) – torch optimizer or any object with attribute
param_groupsas a sequence.param_name (str) – name of optimizer’s parameter to update.
milestones_values (List[Tuple[int, float]]) – list of tuples (event index, parameter value) represents milestones and parameter. Milestones should be increasing integers.
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.
scheduler = PiecewiseLinear(optimizer, "lr", milestones_values=[(10, 0.5), (20, 0.45), (21, 0.3), (30, 0.1), (40, 0.1)]) # Attach to the trainer trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) # # Sets the learning rate to 0.5 over the first 10 iterations, then decreases linearly from 0.5 to 0.45 between # 10th and 20th iterations. Next there is a jump to 0.3 at the 21st iteration and LR decreases linearly # from 0.3 to 0.1 between 21st and 30th iterations and remains 0.1 until the end of the iterations. #
New in version 0.5.1.
Methods
Method to get current optimizer’s parameter values
Copies parameters from
state_dictinto this ParamScheduler.Method to plot simulated scheduled values during num_events events.
Method to simulate scheduled values during num_events events.
Returns a dictionary containing a whole state of ParamScheduler.
-
get_param()[source]# Method to get current optimizer’s parameter values
- Returns
list of params, or scalar param
- Return type
-
load_state_dict(state_dict)# Copies parameters from
state_dictinto this ParamScheduler.- Parameters
state_dict (Mapping) – a dict containing parameters.
- Return type
-
classmethod
plot_values(num_events, **scheduler_kwargs)# Method to plot simulated scheduled values during num_events events.
This class requires matplotlib package to be installed:
pip install matplotlib
- Parameters
num_events (int) – number of events during the simulation.
scheduler_kwargs (Mapping) – parameter scheduler configuration kwargs.
- Returns
matplotlib.lines.Line2D
- Return type
Any
Examples
import matplotlib.pylab as plt plt.figure(figsize=(10, 7)) LinearCyclicalScheduler.plot_values(num_events=50, param_name='lr', start_value=1e-1, end_value=1e-3, cycle_size=10))
-
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()