def testDecay(self):
num_training_steps = 1000
initial_lr = 1.0
for step in range(0, 1500, 250):
decayed_lr = learning_rate_decay_v2.cosine_decay_restarts(
initial_lr, step, num_training_steps)
expected = self.np_cosine_decay_restarts(step, num_training_steps)
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testDecay(self):
num_training_steps = 1000
initial_lr = 1.0
for step in range(0, 1500, 250):
decayed_lr = learning_rate_decay_v2.cosine_decay_restarts(
initial_lr, step, num_training_steps)
expected = self.np_cosine_decay_restarts(step, num_training_steps)
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def cosine_decay_restarts(learning_rate,
global_step,
first_decay_steps,
t_mul=2.0,
m_mul=1.0,
alpha=0.0,
name=None):
"""Applies cosine decay with restarts 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 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:
```python
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.
Raises:
ValueError: if `global_step` is not supplied.
@compatibility(eager)
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.
@end_compatibility
"""
decayed_lr = learning_rate_decay_v2.cosine_decay_restarts(
learning_rate,
global_step,
first_decay_steps,
t_mul=t_mul,
m_mul=m_mul,
alpha=alpha,
name=name)
if not context.executing_eagerly():
decayed_lr = decayed_lr()
return decayed_lr
def cosine_decay_restarts(learning_rate,
global_step,
first_decay_steps,
t_mul=2.0,
m_mul=1.0,
alpha=0.0,
name=None):
"""Applies cosine decay with restarts 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 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:
```python
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.
Raises:
ValueError: if `global_step` is not supplied.
@compatibility(eager)
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.
@end_compatibility
"""
decayed_lr = learning_rate_decay_v2.cosine_decay_restarts(
learning_rate,
global_step,
first_decay_steps,
t_mul=t_mul,
m_mul=m_mul,
alpha=alpha,
name=name)
if not context.executing_eagerly():
decayed_lr = decayed_lr()
return decayed_lr