tf.keras.optimizers.schedules.PolynomialDecay

TensorFlow 2 version

View source on GitHub

A LearningRateSchedule that uses a polynomial decay schedule.

Inherits From: LearningRateSchedule

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.optimizers.schedules.PolynomialDecay, `tf.compat.v2.keras.optimizers.schedules.PolynomialDecay`, `tf.compat.v2.optimizers.schedules.PolynomialDecay`

tf.keras.optimizers.schedules.PolynomialDecay(
    initial_learning_rate, decay_steps, end_learning_rate=0.0001, power=1.0,
    cycle=False, name=None
)

Args
initial_learning_rate	A scalar float32 or float64 Tensor or a Python number. The initial learning rate.
decay_steps	A scalar int32 or int64 Tensor or a Python number. Must be positive. See the decay computation above.
end_learning_rate	A scalar float32 or float64 Tensor or a Python number. The minimal end learning rate.
power	A scalar float32 or float64 Tensor or a Python number. The power of the polynomial. Defaults to linear, 1.0.
cycle	A boolean, whether or not it should cycle beyond decay_steps.
name	String. Optional name of the operation. Defaults to 'PolynomialDecay'.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Instantiates a LearningRateSchedule from its config.

Args
config	Output of get_config().

Returns
A LearningRateSchedule instance.

get_config

View source

get_config()

__call__

View source

__call__(
    step
)

Call self as a function.