diff options
Diffstat (limited to 'tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py')
| -rw-r--r-- | tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py | 57 |
1 files changed, 39 insertions, 18 deletions
diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py index b98adf862b..48ac429701 100644 --- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py +++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py @@ -22,6 +22,7 @@ import collections from six.moves import range from tensorflow.contrib.linear_optimizer.python.ops.sharded_mutable_dense_hashtable import ShardedMutableDenseHashTable +from tensorflow.python.compat import compat from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -485,24 +486,44 @@ class SdcaModel(object): sparse_weights.append(batch_gathered_weights) # pylint: disable=protected-access - esu, sfw, dfw = gen_sdca_ops.sdca_optimizer( - sparse_example_indices, - sparse_feature_indices, - sparse_features_values, - self._convert_n_to_tensor(self._examples['dense_features']), - internal_convert_to_tensor(self._examples['example_weights']), - internal_convert_to_tensor(self._examples['example_labels']), - sparse_indices, - sparse_weights, - self._convert_n_to_tensor(self._slots[ - 'unshrinked_dense_features_weights']), - example_state_data, - loss_type=self._options['loss_type'], - l1=self._options['symmetric_l1_regularization'], - l2=self._symmetric_l2_regularization(), - num_loss_partitions=self._num_loss_partitions(), - num_inner_iterations=1, - adaptative=self._adaptive()) + if compat.forward_compatible(year=2018, month=10, day=30): + esu, sfw, dfw = gen_sdca_ops.sdca_optimizer_v2( + sparse_example_indices, + sparse_feature_indices, + sparse_features_values, + self._convert_n_to_tensor(self._examples['dense_features']), + internal_convert_to_tensor(self._examples['example_weights']), + internal_convert_to_tensor(self._examples['example_labels']), + sparse_indices, + sparse_weights, + self._convert_n_to_tensor(self._slots[ + 'unshrinked_dense_features_weights']), + example_state_data, + loss_type=self._options['loss_type'], + l1=self._options['symmetric_l1_regularization'], + l2=self._symmetric_l2_regularization(), + num_loss_partitions=self._num_loss_partitions(), + num_inner_iterations=1, + adaptive=self._adaptive()) + else: + esu, sfw, dfw = gen_sdca_ops.sdca_optimizer( + sparse_example_indices, + sparse_feature_indices, + sparse_features_values, + self._convert_n_to_tensor(self._examples['dense_features']), + internal_convert_to_tensor(self._examples['example_weights']), + internal_convert_to_tensor(self._examples['example_labels']), + sparse_indices, + sparse_weights, + self._convert_n_to_tensor(self._slots[ + 'unshrinked_dense_features_weights']), + example_state_data, + loss_type=self._options['loss_type'], + l1=self._options['symmetric_l1_regularization'], + l2=self._symmetric_l2_regularization(), + num_loss_partitions=self._num_loss_partitions(), + num_inner_iterations=1, + adaptative=self._adaptive()) # pylint: enable=protected-access with ops.control_dependencies([esu]): |