tf.keras.optimizers.schedules.PolynomialDecay     View source on GitHub    A LearningRateSchedule that uses a polynomial decay schedule. Inherits From: LearningRateSchedule  View aliases  Main aliases 
tf.optimizers.schedules.PolynomialDecay Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.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
)
 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: 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
 If cycle is True then a multiple of decay_steps is used, the first one that is bigger than step. def decayed_learning_rate(step):
  decay_steps = decay_steps * ceil(step / decay_steps)
  return ((initial_learning_rate - end_learning_rate) *
          (1 - step / decay_steps) ^ (power)
         ) + end_learning_rate
 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): ...
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
    starter_learning_rate,
    decay_steps,
    end_learning_rate,
    power=0.5)

model.compile(optimizer=tf.keras.optimizers.SGD(
                  learning_rate=learning_rate_fn),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(data, labels, epochs=5)
 The learning rate schedule is also serializable and deserializable using tf.keras.optimizers.schedules.serialize and tf.keras.optimizers.schedules.deserialize.
 


 Returns   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.  

 


 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.