LostTech.TensorFlow : API Documentation

Type PolynomialDecay

Namespace tensorflow.keras.optimizers.schedules

Parent LearningRateSchedule

Interfaces IPolynomialDecay

A LearningRateSchedule that uses a polynomial decay schedule.



Public static methods

PolynomialDecay NewDyn(object initial_learning_rate, object decay_steps, ImplicitContainer<T> end_learning_rate, ImplicitContainer<T> power, ImplicitContainer<T> cycle, object name)

Applies a polynomial decay to the learning rate.

It is commonly observed that a monotonically decreasing learning rate, whose degree of change is carefully chosen, results in a better performing model. This schedule applies a polynomial decay function to an optimizer step, given a provided `initial_learning_rate`, to reach an `end_learning_rate` in the given `decay_steps`.

It requires a `step` value to compute the decayed learning rate. You can just pass a TensorFlow variable that you increment at each training step.

The schedule is a 1-arg callable that produces a decayed learning rate when passed the current optimizer step. This can be useful for changing the learning rate value across different invocations of optimizer functions. It is computed as: If `cycle` is True then a multiple of `decay_steps` is used, the first one that is bigger than `step`. You can pass this schedule directly into a tf.keras.optimizers.Optimizer as the learning rate. Example: Fit a model while decaying from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5): The learning rate schedule is also serializable and deserializable using tf.keras.optimizers.schedules.serialize and tf.keras.optimizers.schedules.deserialize.
object initial_learning_rate
A scalar `float32` or `float64` `Tensor` or a Python number. The initial learning rate.
object decay_steps
A scalar `int32` or `int64` `Tensor` or a Python number. Must be positive. See the decay computation above.
ImplicitContainer<T> end_learning_rate
A scalar `float32` or `float64` `Tensor` or a Python number. The minimal end learning rate.
ImplicitContainer<T> power
A scalar `float32` or `float64` `Tensor` or a Python number. The power of the polynomial. Defaults to linear, 1.0.
ImplicitContainer<T> cycle
A boolean, whether or not it should cycle beyond decay_steps.
object name
String. Optional name of the operation. Defaults to 'PolynomialDecay'.
A 1-arg callable learning rate schedule that takes the current optimizer step and outputs the decayed learning rate, a scalar `Tensor` of the same type as `initial_learning_rate`.
Show Example
def decayed_learning_rate(step):
              step = min(step, decay_steps)
              return ((initial_learning_rate - end_learning_rate) *
                      (1 - step / decay_steps) ^ (power)
                     ) + end_learning_rate 

Public properties

bool cycle get; set;

int decay_steps get; set;

double end_learning_rate get; set;

double initial_learning_rate get; set;

object name get; set;

double power get; set;

object PythonObject get;