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A LearningRateSchedule that uses a cosine decay schedule with restarts.
Inherits From: LearningRateSchedule
tf.keras.optimizers.schedules.CosineDecayRestarts(
initial_learning_rate,
first_decay_steps,
t_mul=2.0,
m_mul=1.0,
alpha=0.0,
name=None
)
See Loshchilov & Hutter, ICLR2016, SGDR: Stochastic Gradient Descent with Warm Restarts.
When training a model, it is often useful to lower the learning rate as
the training progresses. This schedule applies a cosine decay function with
restarts to an optimizer step, given a provided initial learning rate.
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.
The learning rate multiplier first decays
from 1 to alpha
for first_decay_steps
steps. Then, a warm
restart is performed. Each new warm restart runs for t_mul
times more
steps and with m_mul
times initial learning rate as the new learning rate.
Example usage:
first_decay_steps = 1000
lr_decayed_fn = (
tf.keras.optimizers.schedules.CosineDecayRestarts(
initial_learning_rate,
first_decay_steps))
You can pass this schedule directly into a tf.keras.optimizers.Optimizer
as the learning rate. 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 .
|
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.