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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Defines base class for `ConstrainedOptimizer`s."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import six
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import standard_ops
from tensorflow.python.training import optimizer as train_optimizer
@six.add_metaclass(abc.ABCMeta)
class ConstrainedOptimizer(object):
"""Base class representing a constrained optimizer.
A ConstrainedOptimizer wraps a tf.train.Optimizer (or more than one), and
applies it to a ConstrainedMinimizationProblem. Unlike a tf.train.Optimizer,
which takes a tensor to minimize as a parameter to its minimize() method, a
constrained optimizer instead takes a ConstrainedMinimizationProblem.
"""
def __init__(self, optimizer):
"""Constructs a new `ConstrainedOptimizer`.
Args:
optimizer: tf.train.Optimizer, used to optimize the
ConstraintedMinimizationProblem.
Returns:
A new `ConstrainedOptimizer`.
"""
self._optimizer = optimizer
@property
def optimizer(self):
"""Returns the `tf.train.Optimizer` used for optimization."""
return self._optimizer
def minimize_unconstrained(self,
minimization_problem,
global_step=None,
var_list=None,
gate_gradients=train_optimizer.Optimizer.GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=None,
grad_loss=None):
"""Returns an `Op` for minimizing the unconstrained problem.
Unlike `minimize_constrained`, this function ignores the `constraints` (and
`proxy_constraints`) portion of the minimization problem entirely, and only
minimizes `objective`.
Args:
minimization_problem: ConstrainedMinimizationProblem, the problem to
optimize.
global_step: as in `tf.train.Optimizer`'s `minimize` method.
var_list: as in `tf.train.Optimizer`'s `minimize` method.
gate_gradients: as in `tf.train.Optimizer`'s `minimize` method.
aggregation_method: as in `tf.train.Optimizer`'s `minimize` method.
colocate_gradients_with_ops: as in `tf.train.Optimizer`'s `minimize`
method.
name: as in `tf.train.Optimizer`'s `minimize` method.
grad_loss: as in `tf.train.Optimizer`'s `minimize` method.
Returns:
TensorFlow Op.
"""
return self.optimizer.minimize(
minimization_problem.objective,
global_step=global_step,
var_list=var_list,
gate_gradients=gate_gradients,
aggregation_method=aggregation_method,
colocate_gradients_with_ops=colocate_gradients_with_ops,
name=name,
grad_loss=grad_loss)
@abc.abstractmethod
def minimize_constrained(self,
minimization_problem,
global_step=None,
var_list=None,
gate_gradients=train_optimizer.Optimizer.GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=None,
grad_loss=None):
"""Returns an `Op` for minimizing the constrained problem.
Unlike `minimize_unconstrained`, this function attempts to find a solution
that minimizes the `objective` portion of the minimization problem while
satisfying the `constraints` portion.
Args:
minimization_problem: ConstrainedMinimizationProblem, the problem to
optimize.
global_step: as in `tf.train.Optimizer`'s `minimize` method.
var_list: as in `tf.train.Optimizer`'s `minimize` method.
gate_gradients: as in `tf.train.Optimizer`'s `minimize` method.
aggregation_method: as in `tf.train.Optimizer`'s `minimize` method.
colocate_gradients_with_ops: as in `tf.train.Optimizer`'s `minimize`
method.
name: as in `tf.train.Optimizer`'s `minimize` method.
grad_loss: as in `tf.train.Optimizer`'s `minimize` method.
Returns:
TensorFlow Op.
"""
pass
def minimize(self,
minimization_problem,
unconstrained_steps=None,
global_step=None,
var_list=None,
gate_gradients=train_optimizer.Optimizer.GATE_OP,
aggregation_method=None,
colocate_gradients_with_ops=False,
name=None,
grad_loss=None):
"""Returns an `Op` for minimizing the constrained problem.
This method combines the functionality of `minimize_unconstrained` and
`minimize_constrained`. If global_step < unconstrained_steps, it will
perform an unconstrained update, and if global_step >= unconstrained_steps,
it will perform a constrained update.
The reason for this functionality is that it may be best to initialize the
constrained optimizer with an approximate optimum of the unconstrained
problem.
Args:
minimization_problem: ConstrainedMinimizationProblem, the problem to
optimize.
unconstrained_steps: int, number of steps for which we should perform
unconstrained updates, before transitioning to constrained updates.
global_step: as in `tf.train.Optimizer`'s `minimize` method.
var_list: as in `tf.train.Optimizer`'s `minimize` method.
gate_gradients: as in `tf.train.Optimizer`'s `minimize` method.
aggregation_method: as in `tf.train.Optimizer`'s `minimize` method.
colocate_gradients_with_ops: as in `tf.train.Optimizer`'s `minimize`
method.
name: as in `tf.train.Optimizer`'s `minimize` method.
grad_loss: as in `tf.train.Optimizer`'s `minimize` method.
Returns:
TensorFlow Op.
Raises:
ValueError: If unconstrained_steps is provided, but global_step is not.
"""
def unconstrained_fn():
"""Returns an `Op` for minimizing the unconstrained problem."""
return self.minimize_unconstrained(
minimization_problem=minimization_problem,
global_step=global_step,
var_list=var_list,
gate_gradients=gate_gradients,
aggregation_method=aggregation_method,
colocate_gradients_with_ops=colocate_gradients_with_ops,
name=name,
grad_loss=grad_loss)
def constrained_fn():
"""Returns an `Op` for minimizing the constrained problem."""
return self.minimize_constrained(
minimization_problem=minimization_problem,
global_step=global_step,
var_list=var_list,
gate_gradients=gate_gradients,
aggregation_method=aggregation_method,
colocate_gradients_with_ops=colocate_gradients_with_ops,
name=name,
grad_loss=grad_loss)
if unconstrained_steps is not None:
if global_step is None:
raise ValueError(
"global_step cannot be None if unconstrained_steps is provided")
unconstrained_steps_tensor = ops.convert_to_tensor(unconstrained_steps)
dtype = unconstrained_steps_tensor.dtype
return control_flow_ops.cond(
standard_ops.cast(global_step, dtype) < unconstrained_steps_tensor,
true_fn=unconstrained_fn,
false_fn=constrained_fn)
else:
return constrained_fn()
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