View source: R/learning_rate_schedules.R
learning_rate_schedule_cosine_decay_restarts | R Documentation |
A LearningRateSchedule that uses a cosine decay schedule with restarts
learning_rate_schedule_cosine_decay_restarts(
initial_learning_rate,
first_decay_steps,
t_mul = 2,
m_mul = 1,
alpha = 0,
...,
name = NULL
)
initial_learning_rate |
A scalar |
first_decay_steps |
A scalar |
t_mul |
A scalar |
m_mul |
A scalar |
alpha |
A scalar |
... |
For backwards and forwards compatibility |
name |
String. Optional name of the operation. Defaults to 'SGDRDecay'. |
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.
You can pass this schedule directly into a keras Optimizer
as the learning_rate
.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.