diff options
Diffstat (limited to 'tensorflow/contrib/constrained_optimization/python/external_regret_optimizer.py')
| -rw-r--r-- | tensorflow/contrib/constrained_optimization/python/external_regret_optimizer.py | 53 |
1 files changed, 35 insertions, 18 deletions
diff --git a/tensorflow/contrib/constrained_optimization/python/external_regret_optimizer.py b/tensorflow/contrib/constrained_optimization/python/external_regret_optimizer.py index 01c6e4f08a..d1af15f7e4 100644 --- a/tensorflow/contrib/constrained_optimization/python/external_regret_optimizer.py +++ b/tensorflow/contrib/constrained_optimization/python/external_regret_optimizer.py @@ -70,11 +70,13 @@ def _project_multipliers_wrt_euclidean_norm(multipliers, radius): region w.r.t. the Euclidean norm. Raises: - ValueError: if the `multipliers` tensor does not have a fully-known shape, - or is not one-dimensional. + ValueError: if the `multipliers` tensor is not floating-point, does not have + a fully-known shape, or is not one-dimensional. """ + if not multipliers.dtype.is_floating: + raise ValueError("multipliers must have a floating-point dtype") multipliers_shape = multipliers.get_shape() - if multipliers_shape is None: + if multipliers_shape.ndims is None: raise ValueError("multipliers must have known shape") if multipliers_shape.ndims != 1: raise ValueError( @@ -101,12 +103,12 @@ def _project_multipliers_wrt_euclidean_norm(multipliers, radius): (radius - standard_ops.reduce_sum(multipliers)) / standard_ops.maximum( 1.0, standard_ops.reduce_sum(inactive))) multipliers += scale * inactive - new_inactive = standard_ops.to_float(multipliers > 0) + new_inactive = standard_ops.cast(multipliers > 0, multipliers.dtype) multipliers *= new_inactive return (iteration, multipliers, new_inactive, inactive) iteration = standard_ops.constant(0) - inactive = standard_ops.ones_like(multipliers) + inactive = standard_ops.ones_like(multipliers, dtype=multipliers.dtype) # We actually want a do-while loop, so we explicitly call while_loop_body() # once before tf.while_loop(). @@ -189,16 +191,16 @@ class _ExternalRegretOptimizer(constrained_optimizer.ConstrainedOptimizer): def _projection_op(self, state, name=None): pass - 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. + 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 `Operation` for minimizing the constrained problem. The `optimizer` constructor parameter will be used to update the model parameters, while the Lagrange multipliers will be updated using @@ -216,8 +218,11 @@ class _ExternalRegretOptimizer(constrained_optimizer.ConstrainedOptimizer): name: as in `tf.train.Optimizer`'s `minimize` method. grad_loss: as in `tf.train.Optimizer`'s `minimize` method. + Raises: + ValueError: If the minimization_problem tensors have different dtypes. + Returns: - TensorFlow Op. + `Operation`, the train_op. """ objective = minimization_problem.objective @@ -225,6 +230,14 @@ class _ExternalRegretOptimizer(constrained_optimizer.ConstrainedOptimizer): proxy_constraints = minimization_problem.proxy_constraints if proxy_constraints is None: proxy_constraints = constraints + + # Make sure that the objective, constraints and proxy constraints all have + # the same dtype. + if (objective.dtype.base_dtype != constraints.dtype.base_dtype or + objective.dtype.base_dtype != proxy_constraints.dtype.base_dtype): + raise ValueError("objective, constraints and proxy_constraints must " + "have the same dtype") + # Flatten both constraints tensors to 1d. num_constraints = minimization_problem.num_constraints constraints = standard_ops.reshape(constraints, shape=(num_constraints,)) @@ -241,8 +254,10 @@ class _ExternalRegretOptimizer(constrained_optimizer.ConstrainedOptimizer): multipliers = self._lagrange_multipliers(state) loss = ( - objective + standard_ops.tensordot(multipliers, proxy_constraints, 1)) - multipliers_gradient = constraints + objective + standard_ops.tensordot( + standard_ops.cast(multipliers, proxy_constraints.dtype), + proxy_constraints, 1)) + multipliers_gradient = standard_ops.cast(constraints, multipliers.dtype) update_ops = [] if self.constraint_optimizer is None: @@ -356,6 +371,8 @@ class AdditiveExternalRegretOptimizer(_ExternalRegretOptimizer): # For an AdditiveExternalRegretOptimizer, the internal state is simply a # tensor of Lagrange multipliers with shape (m,), where m is the number of # constraints. + # + # FUTURE WORK: make the dtype a parameter. return standard_ops.zeros((num_constraints,), dtype=dtypes.float32) def _lagrange_multipliers(self, state): |