tf.keras.optimizers.schedules.PolynomialDecay

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

__call__

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__call__(
    step
)

Call self as a function.

from_config

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@classmethod
from_config(
    config
)

Instantiates a LearningRateSchedule from its config.

Args:

  • config: Output of get_config().

Returns:

A LearningRateSchedule instance.

get_config

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get_config()