tf.train.cosine_decay_restarts(
learning_rate,
global_step,
first_decay_steps,
t_mul=2.0,
m_mul=1.0,
alpha=0.0,
name=None
)
Defined in tensorflow/python/training/learning_rate_decay.py
.
Applies cosine decay with restarts to the learning rate.
See [Loshchilov & Hutter, ICLR2016], SGDR: Stochastic Gradient Descent with Warm Restarts. https://arxiv.org/abs/1608.03983
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies a cosine decay function with
restarts to a provided initial learning rate. It requires a global_step
value to compute the decayed learning rate. You can just pass a TensorFlow
variable that you increment at each training step.
The function returns the decayed learning rate while taking into account
possible warm restarts. 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 smaller initial learning rate.
Example usage:
first_decay_steps = 1000
lr_decayed = cosine_decay_restarts(learning_rate, global_step,
first_decay_steps)
Args:
learning_rate
: A scalarfloat32
orfloat64
Tensor or a Python number. The initial learning rate.global_step
: A scalarint32
orint64
Tensor
or a Python number. Global step to use for the decay computation.first_decay_steps
: A scalarint32
orint64
Tensor
or a Python number. Number of steps to decay over.t_mul
: A scalarfloat32
orfloat64
Tensor
or a Python number. Used to derive the number of iterations in the i-th periodm_mul
: A scalarfloat32
orfloat64
Tensor
or a Python number. Used to derive the initial learning rate of the i-th period:alpha
: A scalarfloat32
orfloat64
Tensor or a Python number. Minimum learning rate value as a fraction of the learning_rate.name
: String. Optional name of the operation. Defaults to 'SGDRDecay'.
Returns:
A scalar Tensor
of the same type as learning_rate
. The decayed
learning rate.
Raises:
ValueError
: ifglobal_step
is not supplied.
Eager Compatibility
When eager execution is enabled, this function returns a function which in turn returns the decayed learning rate Tensor. This can be useful for changing the learning rate value across different invocations of optimizer functions.