def _create_learning_rate(learning_rate_config, global_step=None):
"""Create optimizer learning rate based on config.
Args:
learning_rate_config: A LearningRate proto message.
global_step: A variable representing the current step.
If None, defaults to tf.train.get_or_create_global_step()
Returns:
A learning rate.
Raises:
ValueError: when using an unsupported input data type.
"""
if global_step is None:
global_step = tf.train.get_or_create_global_step()
learning_rate = None
learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
if learning_rate_type == 'constant_learning_rate':
config = learning_rate_config.constant_learning_rate
learning_rate = tf.constant(config.learning_rate, dtype=tf.float32,
name='learning_rate')
if learning_rate_type == 'exponential_decay_learning_rate':
config = learning_rate_config.exponential_decay_learning_rate
learning_rate = learning_schedules.exponential_decay_with_burnin(
global_step,
config.initial_learning_rate,
config.decay_steps,
config.decay_factor,
burnin_learning_rate=config.burnin_learning_rate,
burnin_steps=config.burnin_steps,
min_learning_rate=config.min_learning_rate,
staircase=config.staircase)
if learning_rate_type == 'manual_step_learning_rate':
config = learning_rate_config.manual_step_learning_rate
if not config.schedule:
raise ValueError('Empty learning rate schedule.')
learning_rate_step_boundaries = [x.step for x in config.schedule]
learning_rate_sequence = [config.initial_learning_rate]
learning_rate_sequence += [x.learning_rate for x in config.schedule]
learning_rate = learning_schedules.manual_stepping(
global_step, learning_rate_step_boundaries,
learning_rate_sequence, config.warmup)
if learning_rate_type == 'cosine_decay_learning_rate':
config = learning_rate_config.cosine_decay_learning_rate
learning_rate = learning_schedules.cosine_decay_with_warmup(
global_step,
config.learning_rate_base,
config.total_steps,
config.warmup_learning_rate,
config.warmup_steps,
config.hold_base_rate_steps)
if learning_rate is None:
raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
return learning_rate
def graph_fn(global_step):
boundaries = [2, 3, 7]
rates = [1.0, 2.0, 3.0, 4.0]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate, )
def graph_fn(global_step):
boundaries = [4, 6, 8]
rates = [0.02, 0.10, 0.01, 0.001]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates, warmup=True)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate,)
def graph_fn(global_step):
boundaries = [4, 6, 8]
rates = [0.02, 0.10, 0.01, 0.001]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates, warmup=True)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate,)
def graph_fn(global_step):
boundaries = [2, 3, 7]
rates = [1.0, 2.0, 3.0, 4.0]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates)
assert learning_rate.op.name.endswith('learning_rate')
return (learning_rate,)
def _create_learning_rate(learning_rate_config):
"""Create optimizer learning rate based on config.
Args:
learning_rate_config: A LearningRate proto message.
Returns:
A learning rate.
Raises:
ValueError: when using an unsupported input data type.
"""
learning_rate = None
learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
if learning_rate_type == 'constant_learning_rate':
config = learning_rate_config.constant_learning_rate
learning_rate = tf.constant(config.learning_rate, dtype=tf.float32,
name='learning_rate')
if learning_rate_type == 'exponential_decay_learning_rate':
config = learning_rate_config.exponential_decay_learning_rate
learning_rate = learning_schedules.exponential_decay_with_burnin(
tf.train.get_or_create_global_step(),
config.initial_learning_rate,
config.decay_steps,
config.decay_factor,
burnin_learning_rate=config.burnin_learning_rate,
burnin_steps=config.burnin_steps,
min_learning_rate=config.min_learning_rate,
staircase=config.staircase)
if learning_rate_type == 'manual_step_learning_rate':
config = learning_rate_config.manual_step_learning_rate
if not config.schedule:
raise ValueError('Empty learning rate schedule.')
learning_rate_step_boundaries = [x.step for x in config.schedule]
learning_rate_sequence = [config.initial_learning_rate]
learning_rate_sequence += [x.learning_rate for x in config.schedule]
learning_rate = learning_schedules.manual_stepping(
tf.train.get_or_create_global_step(), learning_rate_step_boundaries,
learning_rate_sequence, config.warmup)
if learning_rate_type == 'cosine_decay_learning_rate':
config = learning_rate_config.cosine_decay_learning_rate
learning_rate = learning_schedules.cosine_decay_with_warmup(
tf.train.get_or_create_global_step(),
config.learning_rate_base,
config.total_steps,
config.warmup_learning_rate,
config.warmup_steps,
config.hold_base_rate_steps)
if learning_rate is None:
raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
return learning_rate
def _create_learning_rate(learning_rate_config, global_summaries):
"""Create optimizer learning rate based on config.
Args:
learning_rate_config: A LearningRate proto message.
global_summaries: A set to attach learning rate summary to.
Returns:
A learning rate.
Raises:
ValueError: when using an unsupported input data type.
"""
learning_rate = None
learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
if learning_rate_type == 'constant_learning_rate':
config = learning_rate_config.constant_learning_rate
learning_rate = config.learning_rate
if learning_rate_type == 'exponential_decay_learning_rate':
config = learning_rate_config.exponential_decay_learning_rate
learning_rate = tf.train.exponential_decay(
config.initial_learning_rate,
tf.train.get_or_create_global_step(),
config.decay_steps,
config.decay_factor,
staircase=config.staircase)
if learning_rate_type == 'manual_step_learning_rate':
config = learning_rate_config.manual_step_learning_rate
if not config.schedule:
raise ValueError('Empty learning rate schedule.')
learning_rate_step_boundaries = [x.step for x in config.schedule]
learning_rate_sequence = [config.initial_learning_rate]
learning_rate_sequence += [x.learning_rate for x in config.schedule]
learning_rate = learning_schedules.manual_stepping(
tf.train.get_or_create_global_step(), learning_rate_step_boundaries,
learning_rate_sequence)
if learning_rate_type == 'cosine_decay_learning_rate':
config = learning_rate_config.cosine_decay_learning_rate
learning_rate = learning_schedules.cosine_decay_with_warmup(
tf.train.get_or_create_global_step(),
config.learning_rate_base,
config.total_steps,
config.warmup_learning_rate,
config.warmup_steps)
if learning_rate is None:
raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
global_summaries.add(tf.summary.scalar('Learning_Rate', learning_rate))
return learning_rate
def testManualStepping(self):
global_step = tf.placeholder(tf.int64, [])
boundaries = [2, 3, 7]
rates = [1.0, 2.0, 3.0, 4.0]
exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]
learning_rate = learning_schedules.manual_stepping(global_step, boundaries,
rates)
with self.test_session() as sess:
output_rates = []
for input_global_step in range(10):
output_rate = sess.run(learning_rate,
feed_dict={global_step: input_global_step})
output_rates.append(output_rate)
self.assertAllClose(output_rates, exp_rates)
def testManualStepping(self):
global_step = tf.placeholder(tf.int64, [])
boundaries = [2, 3, 7]
rates = [1.0, 2.0, 3.0, 4.0]
exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates)
with self.test_session() as sess:
output_rates = []
for input_global_step in range(10):
output_rate = sess.run(
learning_rate, feed_dict={global_step: input_global_step})
output_rates.append(output_rate)
self.assertAllClose(output_rates, exp_rates)
def _create_learning_rate(learning_rate_config, global_step=None):
if global_step is None:
global_step = tf.train.get_or_create_global_step()
learning_rate = None
learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
if learning_rate_type == 'constant_learning_rate':
config = learning_rate_config.constant_learning_rate
learning_rate = tf.constant(config.learning_rate, dtype=tf.float32,
name='learning_rate')
if learning_rate_type == 'exponential_decay_learning_rate':
config = learning_rate_config.exponential_decay_learning_rate
learning_rate = learning_schedules.exponential_decay_with_burnin(
global_step,
config.initial_learning_rate,
config.decay_steps,
config.decay_factor,
burnin_learning_rate=config.burnin_learning_rate,
burnin_steps=config.burnin_steps,
min_learning_rate=config.min_learning_rate,
staircase=config.staircase)
if learning_rate_type == 'manual_step_learning_rate':
config = learning_rate_config.manual_step_learning_rate
if not config.schedule:
raise ValueError('Empty learning rate schedule.')
learning_rate_step_boundaries = [x.step for x in config.schedule]
learning_rate_sequence = [config.initial_learning_rate]
learning_rate_sequence += [x.learning_rate for x in config.schedule]
learning_rate = learning_schedules.manual_stepping(
global_step, learning_rate_step_boundaries,
learning_rate_sequence, config.warmup)
if learning_rate_type == 'cosine_decay_learning_rate':
config = learning_rate_config.cosine_decay_learning_rate
learning_rate = learning_schedules.cosine_decay_with_warmup(
global_step,
config.learning_rate_base,
config.total_steps,
config.warmup_learning_rate,
config.warmup_steps,
config.hold_base_rate_steps)
if learning_rate is None:
raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
return learning_rate
def _create_learning_rate(learning_rate_config, global_summaries):
"""Create optimizer learning rate based on config.
Args:
learning_rate_config: A LearningRate proto message.
global_summaries: A set to attach learning rate summary to.
Returns:
A learning rate.
Raises:
ValueError: when using an unsupported input data type.
"""
learning_rate = None
learning_rate_type = learning_rate_config.WhichOneof('learning_rate')
if learning_rate_type == 'constant_learning_rate':
config = learning_rate_config.constant_learning_rate
learning_rate = config.learning_rate
if learning_rate_type == 'exponential_decay_learning_rate':
config = learning_rate_config.exponential_decay_learning_rate
learning_rate = tf.train.exponential_decay(
config.initial_learning_rate,
slim.get_or_create_global_step(),
config.decay_steps,
config.decay_factor,
staircase=config.staircase)
if learning_rate_type == 'manual_step_learning_rate':
config = learning_rate_config.manual_step_learning_rate
if not config.schedule:
raise ValueError('Empty learning rate schedule.')
learning_rate_step_boundaries = [x.step for x in config.schedule]
learning_rate_sequence = [config.initial_learning_rate]
learning_rate_sequence += [x.learning_rate for x in config.schedule]
learning_rate = learning_schedules.manual_stepping(
slim.get_or_create_global_step(), learning_rate_step_boundaries,
learning_rate_sequence)
if learning_rate is None:
raise ValueError('Learning_rate %s not supported.' % learning_rate_type)
global_summaries.add(tf.summary.scalar('Learning Rate', learning_rate))
return learning_rate
def graph_fn(global_step):
boundaries = []
rates = [0.01]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates)
return (learning_rate, )
def graph_fn(global_step):
boundaries = [2, 3, 7]
rates = [1.0, 2.0, 3.0, 4.0]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates)
return (learning_rate, )
def graph_fn(global_step):
boundaries = []
rates = [0.01]
learning_rate = learning_schedules.manual_stepping(
global_step, boundaries, rates)
return (learning_rate,)
