Merge pull request #22691 from Bantena:vowel-check

PiperOrigin-RevId: 216046506

3 files changed, 5 insertions, 5 deletions

diff --git a/tensorflow/contrib/estimator/python/estimator/boosted_trees.py b/tensorflow/contrib/estimator/python/estimator/boosted_trees.py
index a1f1c5f3d7..b131ed4f12 100644
--- a/tensorflow/contrib/estimator/python/estimator/boosted_trees.py
+++ b/tensorflow/contrib/estimator/python/estimator/boosted_trees.py
@@ -75,7 +75,7 @@ class _BoostedTreesEstimator(canned_boosted_trees._BoostedTreesBase):  # pylint:
         layer.
       head: the `Head` instance defined for Estimator.
       model_dir: Directory to save model parameters, graph and etc. This can
-        also be used to load checkpoints from the directory into a estimator
+        also be used to load checkpoints from the directory into an estimator
         to continue training a previously saved model.
       weight_column: A string or a `_NumericColumn` created by
         `tf.feature_column.numeric_column` defining feature column representing
@@ -199,7 +199,7 @@ def boosted_trees_classifier_train_in_memory(
       the model. All items in the set should be instances of classes derived
       from `FeatureColumn`.
     model_dir: Directory to save model parameters, graph and etc. This can
-      also be used to load checkpoints from the directory into a estimator
+      also be used to load checkpoints from the directory into an estimator
       to continue training a previously saved model.
     n_classes: number of label classes. Default is binary classification.
       Multiclass support is not yet implemented.
@@ -345,7 +345,7 @@ def boosted_trees_regressor_train_in_memory(
       the model. All items in the set should be instances of classes derived
       from `FeatureColumn`.
     model_dir: Directory to save model parameters, graph and etc. This can
-      also be used to load checkpoints from the directory into a estimator
+      also be used to load checkpoints from the directory into an estimator
       to continue training a previously saved model.
     label_dimension: Number of regression targets per example.
       Multi-dimensional support is not yet implemented.
diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py b/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py
index 724bc2c82f..4e7965ef26 100644
--- a/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py
+++ b/tensorflow/contrib/estimator/python/estimator/dnn_linear_combined.py
@@ -118,7 +118,7 @@ class DNNLinearCombinedEstimator(estimator.Estimator):
       head: A `_Head` instance constructed with a method such as
         `tf.contrib.estimator.multi_label_head`.
       model_dir: Directory to save model parameters, graph and etc. This can
-        also be used to load checkpoints from the directory into a estimator
+        also be used to load checkpoints from the directory into an estimator
         to continue training a previously saved model.
       linear_feature_columns: An iterable containing all the feature columns
         used by linear part of the model. All items in the set must be
diff --git a/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py b/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py
index 6ca7aaf989..40a91175b7 100644
--- a/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py
+++ b/tensorflow/contrib/estimator/python/estimator/dnn_with_layer_annotations.py
@@ -248,7 +248,7 @@ def DNNClassifierWithLayerAnnotations(  # pylint: disable=invalid-name
       model. All items in the set should be instances of classes derived from
       `_FeatureColumn`.
     model_dir: Directory to save model parameters, graph and etc. This can also
-      be used to load checkpoints from the directory into a estimator to
+      be used to load checkpoints from the directory into an estimator to
       continue training a previously saved model.
     n_classes: Number of label classes. Defaults to 2, namely binary
       classification. Must be > 1.