tfa.optimizers.TriangularCyclicalLearningRate
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A LearningRateSchedule that uses cyclical schedule.
Inherits From: CyclicalLearningRate
tfa.optimizers.TriangularCyclicalLearningRate(
initial_learning_rate: Union[FloatTensorLike, Callable],
maximal_learning_rate: Union[FloatTensorLike, Callable],
step_size: tfa.types.FloatTensorLike ,
scale_mode: str = 'cycle',
name: str = 'TriangularCyclicalLearningRate'
)
Args | |
|---|---|
initial_learning_rate
|
A scalar float32 or float64 Tensor or
a Python number. The initial learning rate.
|
maximal_learning_rate
|
A scalar float32 or float64 Tensor or
a Python number. The maximum learning rate.
|
step_size
|
A scalar float32 or float64 Tensor or a
Python number. Step size denotes the number of training iterations it takes to get to maximal_learning_rate
|
scale_mode
|
['cycle', 'iterations']. Mode to apply during cyclic schedule |
name
|
(Optional) Name for the operation. |
Methods
from_config
@classmethodfrom_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.