tfm.optimization.StepCosineDecayWithOffset

Stepwise cosine learning rate decay with offset.

Main aliases

tfm.optimization.lr_schedule.StepCosineDecayWithOffset

tfm.optimization.StepCosineDecayWithOffset(
 boundaries,
 values,
 offset: int = 0,
 name: str = 'StepCosineDecayWithOffset'
)

Learning rate is equivalent to one or more cosine decay(s) starting and ending at each interval.

ExampleL

 boundaries: [100000, 110000]
 values: [1.0, 0.5]
 lr_decayed_fn = (
 lr_schedule.StepCosineDecayWithOffset(
 boundaries,
 values))

from 0 to 100000 step, it will cosine decay from 1.0 to 0.5 from 100000 to 110000 step, it cosine decay from 0.5 to 0.0

Args
`boundaries`	A list of `Tensor`s or `int`s with strictly increasing entries, and with all elements having the same type as the optimizer step.
`values`	A list of `Tensor`s or `float`s that specifies the values for the intervals defined by `boundaries`. It should have one more element than `boundaries`, and all elements should have the same type.
`offset`	The offset when computing the power decay.
`name`	Optional, name of learning rate schedule.

Methods

@classmethod
from_config(
 config
)

Instantiates a LearningRateSchedule from its config.

Args
`config`	Output of `get_config()`.

Returns
A `LearningRateSchedule` instance.

get_config()

__call__(
 global_step
)

Call self as a function.