diff options
Diffstat (limited to 'tensorflow/contrib/optimizer_v2/optimizer_v2.py')
| -rw-r--r-- | tensorflow/contrib/optimizer_v2/optimizer_v2.py | 11 |
1 files changed, 7 insertions, 4 deletions
diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2.py b/tensorflow/contrib/optimizer_v2/optimizer_v2.py index 8c11d8bcfd..f6ecaba834 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2.py @@ -34,6 +34,7 @@ from tensorflow.python.ops import state_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.training import distribute as distribute_lib +from tensorflow.python.training import distribution_strategy_context from tensorflow.python.training import optimizer as optimizer_v1 from tensorflow.python.training import slot_creator from tensorflow.python.training.checkpointable import base as checkpointable @@ -620,7 +621,7 @@ class OptimizerV2(optimizer_v1.Optimizer): # Map from graph_key to state for that graph. We use the graph_key # since it works in both eager and graph mode, and gives the outer # graph inside functions. - tower_context = distribute_lib.get_tower_context() + tower_context = distribution_strategy_context.get_tower_context() if tower_context is None: # In a cross-tower context for a DistributionStrategy, which means # only one Optimizer will be created, not one per tower. @@ -769,7 +770,8 @@ class OptimizerV2(optimizer_v1.Optimizer): distribute_lib.get_loss_reduction() == variable_scope.VariableAggregation.MEAN) if scale_loss_by_num_towers: - num_towers = distribute_lib.get_distribution_strategy().num_towers + num_towers = distribution_strategy_context.get_distribution_strategy( + ).num_towers if num_towers > 1: loss_value *= 1. / num_towers @@ -788,7 +790,8 @@ class OptimizerV2(optimizer_v1.Optimizer): distribute_lib.get_loss_reduction() == variable_scope.VariableAggregation.MEAN) if scale_loss_by_num_towers: - num_towers = distribute_lib.get_distribution_strategy().num_towers + num_towers = distribution_strategy_context.get_distribution_strategy( + ).num_towers if num_towers > 1: loss *= 1. / num_towers @@ -862,7 +865,7 @@ class OptimizerV2(optimizer_v1.Optimizer): if not filtered: raise ValueError("No gradients provided for any variable: %s." % ([str(v) for _, v in grads_and_vars],)) - return distribute_lib.get_tower_context().merge_call( + return distribution_strategy_context.get_tower_context().merge_call( self._distributed_apply, filtered, global_step=global_step, name=name) def _get_or_create_state(self, var_list=None): |