tf.train.cosine_decay(
learning_rate,
global_step,
decay_steps,
alpha=0.0,
name=None
)
Defined in tensorflow/python/training/learning_rate_decay.py
.
Applies cosine decay 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
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)
cosine_decay = 0.5 * (1 + cos(pi * global_step / decay_steps))
decayed = (1 - alpha) * cosine_decay + alpha
decayed_learning_rate = learning_rate * decayed
Example usage:
decay_steps = 1000
lr_decayed = 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.alpha
: A scalarfloat32
orfloat64
Tensor or a Python number. Minimum learning rate value as a fraction of learning_rate.name
: String. Optional name of the operation. Defaults to 'CosineDecay'.
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.