View source on GitHub |
Applies cosine decay with restarts to the learning rate.
tf.compat.v1.train.cosine_decay_restarts(
learning_rate,
global_step,
first_decay_steps,
t_mul=2.0,
m_mul=1.0,
alpha=0.0,
name=None
)
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)
Returns | |
---|---|
A scalar Tensor of the same type as learning_rate . The decayed
learning rate.
|
Raises | |
---|---|
ValueError
|
if global_step is not supplied.
|
References | |
---|---|
Stochastic Gradient Descent with Warm Restarts: Loshchilov et al., 2017 (pdf) |
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.