xlnstorch.optim.lr_scheduler.LNSConstantLR

xlnstorch.optim.lr_scheduler.LNSConstantLR#

class xlnstorch.optim.lr_scheduler.LNSConstantLR(optimizer, factor=0.3333333333333333, total_iters=0, last_epoch=-1)#

An LNS learning rate scheduler that sets the learning rate of each parameter group to a constant value until a pre-determined number of epochs is reached.

See also: torch.optim.lr_scheduler.ConstantLR

Parameters:

  • optimizer (LNSOptimizer) – Wrapped optimizer.

  • factor (float | LNSTensor) – Multiplicative factor of the learning rate.

  • total_iters (int, optional) – The number of iterations for which the learning rate will be constant. Default: 0.

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

__init__(optimizer, factor=0.3333333333333333, total_iters=0, last_epoch=-1)#
Parameters:

  • optimizer (LNSOptimizer)

  • factor (float | LNSTensor)

  • total_iters (int)

  • last_epoch (int)

Methods

__init__(optimizer[, factor, total_iters, ...])

get_last_lr()

Return last computed learning rate by current scheduler.

get_lr()

Compute the learning rate of each parameter group.

load_state_dict(state_dict)

Load the scheduler's state.

state_dict()

Return the state of the scheduler as a dict.

step([epoch])

Perform a step.
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