Fix minor typos (#19070)

diff --git a/tensorflow/python/estimator/training.py b/tensorflow/python/estimator/training.py
index 534c357..9536613 100644 (file)
--- a/tensorflow/python/estimator/training.py
+++ b/tensorflow/python/estimator/training.py
@@ -588,7 +588,7 @@ class _TrainingExecutor(object):
     # max_steps, the evaluator will send the final export signal. There is a
     # small chance that the Estimator.train stopping logic sees a different
     # global_step value (due to global step race condition and the fact the
-    # saver sees a larger value for checkpoing saving), which does not end
+    # saver sees a larger value for checkpoint saving), which does not end
     # the training. When the training ends, a new checkpoint is generated, which
     # triggers the listener again. So, it could be the case the final export is
     # triggered twice.

diff --git a/tensorflow/python/feature_column/feature_column.py b/tensorflow/python/feature_column/feature_column.py
index 9e6429e..40386ae 100644 (file)
--- a/tensorflow/python/feature_column/feature_column.py
+++ b/tensorflow/python/feature_column/feature_column.py
@@ -280,7 +280,7 @@ def input_layer(features,
 # TODO(akshayka): InputLayer should be a subclass of Layer, and it
 # should implement the logic in input_layer using Layer's build-and-call
 # paradigm; input_layer should create an instance of InputLayer and
-# return the result of inovking its apply method, just as functional layers do.
+# return the result of invoking its apply method, just as functional layers do.
 class InputLayer(object):
   """An object-oriented version of `input_layer` that reuses variables."""
 
@@ -834,7 +834,7 @@ def shared_embedding_columns(
     tensor_name_in_ckpt=None, max_norm=None, trainable=True):
   """List of dense columns that convert from sparse, categorical input.
 
-  This is similar to `embedding_column`, except that that it produces a list of
+  This is similar to `embedding_column`, except that it produces a list of
   embedding columns that share the same embedding weights.
 
   Use this when your inputs are sparse and of the same type (e.g. watched and