ParamScheduler#

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

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.

New in version 0.4.5.

Methods

`get_param`	Method to get current optimizer's parameter values
`load_state_dict`	Copies parameters from `state_dict` into this ParamScheduler.
`plot_values`	Method to plot simulated scheduled values during num_events events.
`simulate_values`	Method to simulate scheduled values during num_events events.
`state_dict`	Returns a dictionary containing a whole state of ParamScheduler.

abstract get_param()[source]#

Method to get current optimizer’s parameter values

Returns: list of params, or scalar param
Return type: Union[List[float], float]

load_state_dict(state_dict)[source]#

Copies parameters from state_dict into this ParamScheduler.

Parameters: state_dict (Mapping) – a dict containing parameters.
Return type: None

classmethod plot_values(num_events, **scheduler_kwargs)[source]#

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)[source]#

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()

state_dict()[source]#

Returns a dictionary containing a whole state of ParamScheduler.

Returns: a dictionary containing a whole state of ParamScheduler
Return type: dict