def testStaircase(self):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = resource_variable_ops.ResourceVariable(0)
decayed_lr = learning_rate_decay_v2.inverse_time_decay(
initial_lr, step, k, decay_rate, staircase=True)
self.evaluate(variables.global_variables_initializer())
for i in range(k + 1):
expected = initial_lr / (1 + decay_rate * (i // k))
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
self.evaluate(step.assign_add(1))
def testStaircase(self):
initial_lr = 0.1
k = 10
decay_rate = 0.96
step = resource_variable_ops.ResourceVariable(0)
decayed_lr = learning_rate_decay_v2.inverse_time_decay(
initial_lr, step, k, decay_rate, staircase=True)
self.evaluate(variables.global_variables_initializer())
for i in range(k + 1):
expected = initial_lr / (1 + decay_rate * (i // k))
self.assertAllClose(self.evaluate(decayed_lr()), expected, 1e-6)
self.evaluate(step.assign_add(1))
def inverse_time_decay(learning_rate,
global_step,
decay_steps,
decay_rate,
staircase=False,
name=None):
"""Applies inverse time decay to the initial learning rate.
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies an inverse decay function
to a provided initial learning rate. It requires an `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
decayed_learning_rate = learning_rate / (1 + decay_rate * global_step /
decay_step)
```
or, if `staircase` is `True`, as:
```python
decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step /
decay_step))
```
Example: decay 1/t with a rate of 0.5:
```python
...
global_step = tf.Variable(0, trainable=False)
learning_rate = 0.1
decay_steps = 1.0
decay_rate = 0.5
learning_rate = tf.train.inverse_time_decay(learning_rate, global_step,
decay_steps, decay_rate)
# 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 Python number.
Global step to use for the decay computation. Must not be negative.
decay_steps: How often to apply decay.
decay_rate: A Python number. The decay rate.
staircase: Whether to apply decay in a discrete staircase, as opposed to
continuous, fashion.
name: String. Optional name of the operation. Defaults to
'InverseTimeDecay'.
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.inverse_time_decay(learning_rate,
global_step,
decay_steps,
decay_rate,
staircase=staircase,
name=name)
if not context.executing_eagerly():
decayed_lr = decayed_lr()
return decayed_lr
def inverse_time_decay(learning_rate,
global_step,
decay_steps,
decay_rate,
staircase=False,
name=None):
"""Applies inverse time decay to the initial learning rate.
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies an inverse decay function
to a provided initial learning rate. It requires an `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
decayed_learning_rate = learning_rate / (1 + decay_rate * global_step /
decay_step)
```
or, if `staircase` is `True`, as:
```python
decayed_learning_rate = learning_rate / (1 + decay_rate * floor(global_step /
decay_step))
```
Example: decay 1/t with a rate of 0.5:
```python
...
global_step = tf.Variable(0, trainable=False)
learning_rate = 0.1
decay_steps = 1.0
decay_rate = 0.5
learning_rate = tf.train.inverse_time_decay(learning_rate, global_step,
decay_steps, decay_rate)
# 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 Python number.
Global step to use for the decay computation. Must not be negative.
decay_steps: How often to apply decay.
decay_rate: A Python number. The decay rate.
staircase: Whether to apply decay in a discrete staircase, as opposed to
continuous, fashion.
name: String. Optional name of the operation. Defaults to
'InverseTimeDecay'.
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.inverse_time_decay(
learning_rate,
global_step,
decay_steps,
decay_rate,
staircase=staircase,
name=name)
if not context.executing_eagerly():
decayed_lr = decayed_lr()
return decayed_lr