LinearCyclicalScheduler#
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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 (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.
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 0.5.1.
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.
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get_param()[source]# Method to get current optimizer’s parameter values
- Returns
list of params, or scalar param
- Return type
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load_state_dict(state_dict)# Copies parameters from
state_dictinto this ParamScheduler.- Parameters
state_dict (Mapping) – a dict containing parameters.
- Return type
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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))
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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()