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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)
Args | |
|---|---|
learning_rate
|
A scalar float32 or float64 Tensor or a Python number.
The initial learning rate.
|
global_step
|
A scalar int32 or int64 Tensor or a Python number. Global
step to use for the decay computation.
|
first_decay_steps
|
A scalar int32 or int64 Tensor or a Python number.
Number of steps to decay over.
|
t_mul
|
A scalar float32 or float64 Tensor or a Python number. Used to
derive the number of iterations in the i-th period
|
m_mul
|
A scalar float32 or float64 Tensor or a Python number.
Used to derive the initial learning rate of the i-th period:
|
alpha
|
A scalar float32 or float64 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.
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Raises | |
|---|---|
ValueError
|
if global_step is not supplied.
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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.