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Applies linear cosine decay to the learning rate.
tf.compat.v1.train.linear_cosine_decay(
learning_rate, global_step, decay_steps, num_periods=0.5, alpha=0.0, beta=0.001,
name=None
)
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 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) * cosine_decay + beta
decayed_learning_rate = learning_rate * decayed
Example usage:
decay_steps = 1000
lr_decayed = linear_cosine_decay(learning_rate, global_step, 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.
|
decay_steps
|
A scalar int32 or int64 Tensor or a Python number. Number
of steps to decay over.
|
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 'LinearCosineDecay'. |
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:
Neural Optimizer Search with Reinforcement Learning: Bello et al., 2017 (pdf) 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.