diff options
Diffstat (limited to 'tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py')
| -rw-r--r-- | tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py | 65 |
1 files changed, 42 insertions, 23 deletions
diff --git a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py index ff846b191a..2c673d9347 100644 --- a/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py +++ b/tensorflow/contrib/constrained_optimization/python/swap_regret_optimizer.py @@ -79,9 +79,11 @@ def _maximal_eigenvector_power_method(matrix, The maximal right-eigenvector of `matrix`. Raises: - ValueError: If the epsilon or maximum_iterations parameters violate their - bounds. + ValueError: If the `matrix` tensor is not floating-point, or if the + `epsilon` or `maximum_iterations` parameters violate their bounds. """ + if not matrix.dtype.is_floating: + raise ValueError("multipliers must have a floating-point dtype") if epsilon <= 0.0: raise ValueError("epsilon must be strictly positive") if maximum_iterations <= 0: @@ -139,11 +141,13 @@ def _project_stochastic_matrix_wrt_euclidean_norm(matrix): (i.e. the Frobenius norm). Raises: - ValueError: if the `matrix` tensor does not have a fully-known shape, or is - not two-dimensional and square. + ValueError: if the `matrix` tensor is not floating-point, does not have a + fully-known shape, or is not two-dimensional and square. """ + if not matrix.dtype.is_floating: + raise ValueError("multipliers must have a floating-point dtype") matrix_shape = matrix.get_shape() - if matrix_shape is None: + if matrix_shape.ndims is None: raise ValueError("matrix must have known shape") if matrix_shape.ndims != 2: raise ValueError( @@ -172,12 +176,12 @@ def _project_stochastic_matrix_wrt_euclidean_norm(matrix): matrix, axis=0, keepdims=True)) / standard_ops.maximum( 1.0, standard_ops.reduce_sum(inactive, axis=0, keepdims=True)) matrix += scale * inactive - new_inactive = standard_ops.to_float(matrix > 0) + new_inactive = standard_ops.cast(matrix > 0, matrix.dtype) matrix *= new_inactive return (iteration, matrix, new_inactive, inactive) iteration = standard_ops.constant(0) - inactive = standard_ops.ones_like(matrix) + inactive = standard_ops.ones_like(matrix, dtype=matrix.dtype) # We actually want a do-while loop, so we explicitly call while_loop_body() # once before tf.while_loop(). @@ -218,7 +222,7 @@ class _SwapRegretOptimizer(constrained_optimizer.ConstrainedOptimizer): """Base class representing a `_SwapRegretOptimizer`. This class contains most of the logic for performing constrained optimization, - minimizing external regret for the constraints player. What it *doesn't* do is + minimizing swap regret for the constraints player. What it *doesn't* do is keep track of the internal state (the stochastic matrix). Instead, the state is accessed via the _initial_state(), _stochastic_matrix(), _constraint_grad_and_var() and _projection_op() methods. @@ -291,16 +295,16 @@ class _SwapRegretOptimizer(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 constraint/objective weight matrix (the analogue of @@ -320,8 +324,11 @@ class _SwapRegretOptimizer(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 @@ -329,6 +336,14 @@ class _SwapRegretOptimizer(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,)) @@ -344,15 +359,18 @@ class _SwapRegretOptimizer(constrained_optimizer.ConstrainedOptimizer): name="swap_regret_optimizer_state") zero_and_constraints = standard_ops.concat( - (standard_ops.zeros((1,)), constraints), axis=0) + (standard_ops.zeros((1,), dtype=constraints.dtype), constraints), + axis=0) objective_and_proxy_constraints = standard_ops.concat( (standard_ops.expand_dims(objective, 0), proxy_constraints), axis=0) distribution = self._distribution(state) - loss = standard_ops.tensordot(distribution, objective_and_proxy_constraints, - 1) + loss = standard_ops.tensordot( + standard_ops.cast(distribution, objective_and_proxy_constraints.dtype), + objective_and_proxy_constraints, 1) matrix_gradient = standard_ops.matmul( - standard_ops.expand_dims(zero_and_constraints, 1), + standard_ops.expand_dims( + standard_ops.cast(zero_and_constraints, distribution.dtype), 1), standard_ops.expand_dims(distribution, 0)) update_ops = [] @@ -555,6 +573,7 @@ class MultiplicativeSwapRegretOptimizer(_SwapRegretOptimizer): log_initial_one = math.log(1.0 - (self._initial_multiplier_radius * (dimension - 1) / (dimension))) log_initial_zero = math.log(self._initial_multiplier_radius / dimension) + # FUTURE WORK: make the dtype a parameter. return standard_ops.concat( (standard_ops.constant( log_initial_one, dtype=dtypes.float32, shape=(1, dimension)), |