TensorFlow 1 version | View source on GitHub |
A LearningRateSchedule that uses a polynomial decay schedule.
Inherits From: LearningRateSchedule
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
@classmethod
from_config( config )
Instantiates a LearningRateSchedule
from its config.
Args | |
---|---|
config
|
Output of get_config() .
|
Returns | |
---|---|
A LearningRateSchedule instance.
|
get_config
get_config()
__call__
__call__(
step
)
Call self as a function.