ExponentialLR

class torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma, last_epoch=-1, verbose='deprecated')

Decays the learning rate of each parameter group by gamma every epoch.

When last_epoch=-1, sets initial lr as lr.

Parameters

• optimizer (Optimizer) – Wrapped optimizer.

• gamma (float) – Multiplicative factor of learning rate decay.

• last_epoch (int) – The index of last epoch. Default: -1.

• verbose (bool | str) –

  If True, prints a message to stdout for each update. Default: False.

  Deprecated since version 2.2: verbose is deprecated. Please use get_last_lr() to access the learning rate.

get_last_lr()

Return last computed learning rate by current scheduler.

Return type

List[float]

get_lr()

Compute the learning rate of each parameter group.

load_state_dict(state_dict)

Load the scheduler’s state.

Parameters

state_dict (dict) – scheduler state. Should be an object returned from a call to state_dict().

print_lr(is_verbose, group, lr, epoch=None)

Display the current learning rate.

Deprecated since version 2.4: print_lr() is deprecated. Please use get_last_lr() to access the learning rate.

state_dict()

Return the state of the scheduler as a dict.

It contains an entry for every variable in self.__dict__ which is not the optimizer.

step(epoch=None)

Perform a step.