tf.train.noisy_linear_cosine_decay(
learning_rate,
global_step,
decay_steps,
initial_variance=1.0,
variance_decay=0.55,
num_periods=0.5,
alpha=0.0,
beta=0.001,
name=None
)
Defined in tensorflow/python/training/learning_rate_decay.py
.
Applies noisy linear cosine decay to the learning rate.
See [Bello et al., ICML2017] Neural Optimizer Search with RL. https://arxiv.org/abs/1709.07417
For the idea of warm starts here controlled by num_periods
,
see [Loshchilov & Hutter, ICLR2016] SGDR: Stochastic Gradient Descent
with Warm Restarts. https://arxiv.org/abs/1608.03983
Note that linear cosine decay is more aggressive than cosine decay and larger initial learning rates can typically be used.
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies a noisy linear
cosine decay function 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. It is computed as:
global_step = min(global_step, decay_steps)
linear_decay = (decay_steps - global_step) / decay_steps)
cosine_decay = 0.5 * (
1 + cos(pi * 2 * num_periods * global_step / decay_steps))
decayed = (alpha + linear_decay + eps_t) * cosine_decay + beta
decayed_learning_rate = learning_rate * decayed
where eps_t is 0-centered gaussian noise with variance initial_variance / (1 + global_step) ** variance_decay
Example usage:
decay_steps = 1000
lr_decayed = noisy_linear_cosine_decay(
learning_rate, global_step, 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.decay_steps
: A scalarint32
orint64
Tensor
or a Python number. Number of steps to decay over.initial_variance
: initial variance for the noise. See computation above.variance_decay
: decay for the noise's variance. See computation above.num_periods
: Number of periods in the cosine part of the decay. See computation above.alpha
: See computation above.beta
: See computation above.name
: String. Optional name of the operation. Defaults to 'NoisyLinearCosineDecay'.
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.