def testHalfWayWithEnd(self): step = 5 lr = 0.05 end_lr = 0.001 decayed_lr = learning_rate_decay_v2.polynomial_decay(lr, step, 10, end_lr) expected = (lr + end_lr) * 0.5 self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testBeyondEnd(self): step = 15 lr = 0.05 end_lr = 0.001 decayed_lr = learning_rate_decay_v2.polynomial_decay(lr, step, 10, end_lr) expected = end_lr self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testBeyondEnd(self): step = 15 lr = 0.05 end_lr = 0.001 decayed_lr = learning_rate_decay_v2.polynomial_decay(lr, step, 10, end_lr) expected = end_lr self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testHalfWayWithEnd(self): step = 5 lr = 0.05 end_lr = 0.001 decayed_lr = learning_rate_decay_v2.polynomial_decay(lr, step, 10, end_lr) expected = (lr + end_lr) * 0.5 self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testBeginWithCycle(self):
lr = 0.001
decay_steps = 10
step = 0
decayed_lr = learning_rate_decay_v2.polynomial_decay(
lr, step, decay_steps, cycle=True)
expected = lr
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testBeyondEndWithCycle(self):
step = 15
lr = 0.05
end_lr = 0.001
decayed_lr = learning_rate_decay_v2.polynomial_decay(
lr, step, 10, end_lr, cycle=True)
expected = (lr - end_lr) * 0.25 + end_lr
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testBeginWithCycle(self):
lr = 0.001
decay_steps = 10
step = 0
decayed_lr = learning_rate_decay_v2.polynomial_decay(
lr, step, decay_steps, cycle=True)
expected = lr
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testBeyondEndWithCycle(self):
step = 15
lr = 0.05
end_lr = 0.001
decayed_lr = learning_rate_decay_v2.polynomial_decay(
lr, step, 10, end_lr, cycle=True)
expected = (lr - end_lr) * 0.25 + end_lr
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testHalfWay(self):
step = 5
lr = 0.05
end_lr = 0.0
power = 0.5
decayed_lr = learning_rate_decay_v2.polynomial_decay(
lr, step, 10, end_lr, power=power)
expected = lr * 0.5**power
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def testHalfWay(self):
step = 5
lr = 0.05
end_lr = 0.0
power = 0.5
decayed_lr = learning_rate_decay_v2.polynomial_decay(
lr, step, 10, end_lr, power=power)
expected = lr * 0.5**power
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
def polynomial_decay(learning_rate,
global_step,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False,
name=None):
# learning_rate = learning_rate * random.uniform(0, 1)
decayed_lr = learning_rate_decay_v2.polynomial_decay(
learning_rate,
global_step,
decay_steps,
end_learning_rate=end_learning_rate,
power=power,
cycle=cycle,
name=name)
if not context.executing_eagerly():
decayed_lr = decayed_lr()
return decayed_lr
def polynomial_decay(learning_rate,
global_step,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False,
name=None):
"""Applies a polynomial decay to the learning rate.
It is commonly observed that a monotonically decreasing learning rate, whose
degree of change is carefully chosen, results in a better performing model.
This function applies a polynomial decay function to a provided initial
`learning_rate` to reach an `end_learning_rate` in the given `decay_steps`.
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:
```python
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate
```
If `cycle` is True then a multiple of `decay_steps` is used, the first one
that is bigger than `global_steps`.
```python
decay_steps = decay_steps * ceil(global_step / decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate
```
Example: decay from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):
```python
...
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate = tf.train.polynomial_decay(starter_learning_rate, global_step,
decay_steps, end_learning_rate,
power=0.5)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
tf.train.GradientDescentOptimizer(learning_rate)
.minimize(...my loss..., global_step=global_step)
)
```
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. Must not be negative.
decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
Must be positive. See the decay computation above.
end_learning_rate: A scalar `float32` or `float64` `Tensor` or a
Python number. The minimal end learning rate.
power: A scalar `float32` or `float64` `Tensor` or a
Python number. The power of the polynomial. Defaults to linear, 1.0.
cycle: A boolean, whether or not it should cycle beyond decay_steps.
name: String. Optional name of the operation. Defaults to
'PolynomialDecay'.
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.polynomial_decay(
learning_rate,
global_step,
decay_steps,
end_learning_rate=end_learning_rate,
power=power,
cycle=cycle,
name=name)
if not context.executing_eagerly():
decayed_lr = decayed_lr()
return decayed_lr
def polynomial_decay(learning_rate,
global_step,
decay_steps,
end_learning_rate=0.0001,
power=1.0,
cycle=False,
name=None):
"""Applies a polynomial decay to the learning rate.
It is commonly observed that a monotonically decreasing learning rate, whose
degree of change is carefully chosen, results in a better performing model.
This function applies a polynomial decay function to a provided initial
`learning_rate` to reach an `end_learning_rate` in the given `decay_steps`.
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:
```python
global_step = min(global_step, decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate
```
If `cycle` is True then a multiple of `decay_steps` is used, the first one
that is bigger than `global_steps`.
```python
decay_steps = decay_steps * ceil(global_step / decay_steps)
decayed_learning_rate = (learning_rate - end_learning_rate) *
(1 - global_step / decay_steps) ^ (power) +
end_learning_rate
```
Example: decay from 0.1 to 0.01 in 10000 steps using sqrt (i.e. power=0.5):
```python
...
global_step = tf.Variable(0, trainable=False)
starter_learning_rate = 0.1
end_learning_rate = 0.01
decay_steps = 10000
learning_rate = tf.train.polynomial_decay(starter_learning_rate, global_step,
decay_steps, end_learning_rate,
power=0.5)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
tf.train.GradientDescentOptimizer(learning_rate)
.minimize(...my loss..., global_step=global_step)
)
```
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. Must not be negative.
decay_steps: A scalar `int32` or `int64` `Tensor` or a Python number.
Must be positive. See the decay computation above.
end_learning_rate: A scalar `float32` or `float64` `Tensor` or a
Python number. The minimal end learning rate.
power: A scalar `float32` or `float64` `Tensor` or a
Python number. The power of the polynomial. Defaults to linear, 1.0.
cycle: A boolean, whether or not it should cycle beyond decay_steps.
name: String. Optional name of the operation. Defaults to
'PolynomialDecay'.
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.polynomial_decay(
learning_rate,
global_step,
decay_steps,
end_learning_rate=end_learning_rate,
power=power,
cycle=cycle,
name=name)
if not context.executing_eagerly():
decayed_lr = decayed_lr()
return decayed_lr
