def periodic_target_update(target_variables,
source_variables,
update_period,
tau=1.0,
use_locking=False,
name="periodic_target_update"):
"""Returns an op to periodically update a list of target variables.
The `update_target_variables` op is executed every `update_period`
executions of the `periodic_target_update` op.
The update rule is:
`target_variable = (1 - tau) * target_variable + tau * source_variable`.
Args:
target_variables: a list of the variables to be updated.
source_variables: a list of the variables used for the update.
update_period: inverse frequency with which to apply the update.
tau: weight used to gate the update. The permitted range is 0 < tau <= 1,
with small tau representing an incremental update, and tau == 1
representing a full update (that is, a straight copy).
use_locking: use `tf.variable.Assign`'s locking option when assigning
source variable values to target variables.
name: sets the `name_scope` for this op.
Returns:
An op that periodically updates `target_variables` with `source_variables`.
"""
def update_op():
return update_target_variables(
target_variables, source_variables, tau, use_locking)
with tf.name_scope(name, values=target_variables + source_variables):
return periodic_ops.periodically(update_op, update_period)
def periodic_target_update(target_variables,
source_variables,
update_period,
tau=1.0,
use_locking=False,
name="periodic_target_update"):
"""Returns an op to periodically update a list of target variables.
The `update_target_variables` op is executed every `update_period`
executions of the `periodic_target_update` op.
The update rule is:
`target_variable = (1 - tau) * target_variable + tau * source_variable`.
Args:
target_variables: a list of the variables to be updated.
source_variables: a list of the variables used for the update.
update_period: inverse frequency with which to apply the update.
tau: weight used to gate the update. The permitted range is 0 < tau <= 1,
with small tau representing an incremental update, and tau == 1
representing a full update (that is, a straight copy).
use_locking: use `tf.variable.Assign`'s locking option when assigning
source variable values to target variables.
name: sets the `name_scope` for this op.
Returns:
An op that periodically updates `target_variables` with `source_variables`.
"""
def update_op():
return update_target_variables(target_variables, source_variables, tau,
use_locking)
with tf.name_scope(name, values=target_variables + source_variables):
return periodic_ops.periodically(update_op, update_period)
def testPeriodOne(self):
"""Tests that the function is called every time if period == 1."""
target = tf.Variable(0)
periodic_update = periodic_ops.periodically(
body=lambda: target.assign_add(1).op, period=1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
for desired_value in range(1, 11):
_, result = sess.run([periodic_update, target])
self.assertEqual(desired_value, result)
def testPeriodOne(self):
"""Tests that the function is called every time if period == 1."""
target = tf.Variable(0)
periodic_update = periodic_ops.periodically(
body=lambda: target.assign_add(1).op, period=1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
for desired_value in range(1, 11):
_, result = sess.run([periodic_update, target])
self.assertEqual(desired_value, result)
def testPeriodically(self):
"""Tests that a function is called exactly every `period` steps."""
target = tf.Variable(0)
period = 3
periodic_update = periodic_ops.periodically(
body=lambda: target.assign_add(1).op, period=period)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
desired_values = [1, 1, 1, 2, 2, 2, 3, 3, 3, 4]
for desired_value in desired_values:
sess.run(periodic_update)
result = sess.run(target)
self.assertEqual(desired_value, result)
def testPeriodically(self):
"""Tests that a function is called exactly every `period` steps."""
target = tf.Variable(0)
period = 3
periodic_update = periodic_ops.periodically(
body=lambda: target.assign_add(1).op, period=period)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
desired_values = [1, 1, 1, 2, 2, 2, 3, 3, 3, 4]
for desired_value in desired_values:
sess.run(periodic_update)
result = sess.run(target)
self.assertEqual(desired_value, result)