class

MultiplicativeLR

extends_LRScheduler

MultiplicativeLR(optimizer: Optimizer, lr_lambda: Callable[[int], float], last_epoch: int = -1, verbose: bool = False)

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Multiply the learning rate by a factor returned by a function each epoch.

Unlike LambdaLR, which computes the learning rate as base_lr * fn(t), MultiplicativeLR accumulates the factor: each epoch the current learning rate is multiplied by lr_lambda(t):

\eta_t = \eta_{t-1} \cdot \lambda(t)

This means the schedule depends on the entire history of lr_lambda values, not only the current epoch index.

Parameters

optimizerOptimizer

Wrapped optimizer.

lr_lambdacallable

A function f(epoch: int) -> float returning the multiplicative factor to apply at that epoch.

last_epochint= -1

The index of the last epoch (default: -1).

verbosebool= False

Print the updated LR after each step if True (default: False).

Attributes

lr_lambdacallable

The factor function applied each epoch.

Notes

At epoch 0 the learning rate is left unchanged (the factor is not applied on the very first step so that the initial LR from the optimizer is honored).

Examples

>>> import lucid.nn as nn
>>> model = nn.Linear(4, 2)
>>> import lucid.optim as optim
>>> optimizer = optim.SGD(model.parameters(), lr=0.1)
>>> scheduler = optim.MultiplicativeLR(optimizer, lr_lambda=lambda t: 0.95)
>>> for epoch in range(50):
...     optimizer.step()
...     scheduler.step()

Used by 1

lucid.optim

Constructors

dunder

init

→None

__init__(optimizer: Optimizer, lr_lambda: Callable[[int], float], last_epoch: int = -1, verbose: bool = False)

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Initialise the MultiplicativeLR. See the class docstring for parameter semantics.

Instance methods

get_lr

→list[float]

get_lr()

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Compute the learning rate for each parameter group at the current step.

Returns

list[float]

One learning rate per param group, derived from the schedule formula documented in the class docstring.

>>> import lucid.nn as nn >>> model = nn.Linear(4, 2) >>> import lucid.optim as optim >>> optimizer = optim.SGD(model.parameters(), lr=0.1) >>> scheduler = optim.MultiplicativeLR(optimizer, lr_lambda=lambda t: 0.95) >>> for epoch in range(50): ... optimizer.step() ... scheduler.step()