diff options
Diffstat (limited to 'tensorflow/contrib/constrained_optimization/python/constrained_optimizer.py')
| -rw-r--r-- | tensorflow/contrib/constrained_optimization/python/constrained_optimizer.py | 128 |
1 files changed, 97 insertions, 31 deletions
diff --git a/tensorflow/contrib/constrained_optimization/python/constrained_optimizer.py b/tensorflow/contrib/constrained_optimization/python/constrained_optimizer.py index 8055545366..0b79bdf7c0 100644 --- a/tensorflow/contrib/constrained_optimization/python/constrained_optimizer.py +++ b/tensorflow/contrib/constrained_optimization/python/constrained_optimizer.py @@ -55,20 +55,21 @@ class ConstrainedOptimizer(object): """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. + @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): + """Version of `minimize_constrained` to be overridden by subclasses. - Unlike `minimize_constrained`, this function ignores the `constraints` (and - `proxy_constraints`) portion of the minimization problem entirely, and only - minimizes `objective`. + Implementations of this method should ignore the `pre_train_ops` property of + the `minimization_problem`. The public `minimize_constrained` method will + take care of executing these before the returned train_op. Args: minimization_problem: ConstrainedMinimizationProblem, the problem to @@ -83,19 +84,10 @@ class ConstrainedOptimizer(object): grad_loss: as in `tf.train.Optimizer`'s `minimize` method. Returns: - TensorFlow Op. + `Operation`, the train_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) + pass - @abc.abstractmethod def minimize_constrained(self, minimization_problem, global_step=None, @@ -105,7 +97,7 @@ class ConstrainedOptimizer(object): colocate_gradients_with_ops=False, name=None, grad_loss=None): - """Returns an `Op` for minimizing the constrained problem. + """Returns an `Operation` 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 @@ -124,9 +116,83 @@ class ConstrainedOptimizer(object): grad_loss: as in `tf.train.Optimizer`'s `minimize` method. Returns: - TensorFlow Op. + `Operation`, the train_op. """ - pass + + def train_op_callback(): + return self._minimize_constrained( + 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 we have pre_train_ops, use tf.control_dependencies() to ensure that + # they execute before the train_op. + pre_train_ops = minimization_problem.pre_train_ops + if pre_train_ops: + with ops.control_dependencies(pre_train_ops): + train_op = train_op_callback() + else: + train_op = train_op_callback() + + return train_op + + 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 `Operation` 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: + `Operation`, the train_op. + """ + + def train_op_callback(): + 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) + + # If we have pre_train_ops, use tf.control_dependencies() to ensure that + # they execute before the train_op. + pre_train_ops = minimization_problem.pre_train_ops + if pre_train_ops: + with ops.control_dependencies(pre_train_ops): + train_op = train_op_callback() + else: + train_op = train_op_callback() + + return train_op def minimize(self, minimization_problem, @@ -138,7 +204,7 @@ class ConstrainedOptimizer(object): colocate_gradients_with_ops=False, name=None, grad_loss=None): - """Returns an `Op` for minimizing the constrained problem. + """Returns an `Operation` for minimizing the constrained problem. This method combines the functionality of `minimize_unconstrained` and `minimize_constrained`. If global_step < unconstrained_steps, it will @@ -164,14 +230,14 @@ class ConstrainedOptimizer(object): grad_loss: as in `tf.train.Optimizer`'s `minimize` method. Returns: - TensorFlow Op. + `Operation`, the train_op. Raises: ValueError: If unconstrained_steps is provided, but global_step is not. """ def unconstrained_fn(): - """Returns an `Op` for minimizing the unconstrained problem.""" + """Returns an `Operation` for minimizing the unconstrained problem.""" return self.minimize_unconstrained( minimization_problem=minimization_problem, global_step=global_step, @@ -183,7 +249,7 @@ class ConstrainedOptimizer(object): grad_loss=grad_loss) def constrained_fn(): - """Returns an `Op` for minimizing the constrained problem.""" + """Returns an `Operation` for minimizing the constrained problem.""" return self.minimize_constrained( minimization_problem=minimization_problem, global_step=global_step, |