Fix misspells. (#10000)

3 files changed, 3 insertions, 3 deletions

diff --git a/tensorflow/contrib/data/python/kernel_tests/resample_test.py b/tensorflow/contrib/data/python/kernel_tests/resample_test.py
index f6ce77054b..fb66acdcac 100644
--- a/tensorflow/contrib/data/python/kernel_tests/resample_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/resample_test.py
@@ -65,7 +65,7 @@ class ResampleTest(test.TestCase):
     self.assertAllEqual([compat.as_bytes(str(c))
                          for c in returned_classes], returned_data)
     total_returned = len(returned_classes)
-    # Subsampling rejects a large precentage of the initial data in
+    # Subsampling rejects a large percentage of the initial data in
     # this case.
     self.assertGreater(total_returned, 20000 * 0.2)
     class_counts = np.array([
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
index 58368d92c4..1c67a1b8f6 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
@@ -166,7 +166,7 @@ class ShapesFromLocAndScaleTest(test.TestCase):
       batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
           loc, scale)
       # batch_shape depends on both args, and so is dynamic.  Since loc did not
-      # have static shape, we infered event shape entirely from scale, and this
+      # have static shape, we inferred event shape entirely from scale, and this
       # is available statically.
       self.assertAllEqual(
           [5, 2], batch_shape.eval(feed_dict={loc: np.zeros((2, 3))}))
diff --git a/tensorflow/core/kernels/parallel_map_dataset_op.cc b/tensorflow/core/kernels/parallel_map_dataset_op.cc
index 6b1214e660..0be7835ba5 100644
--- a/tensorflow/core/kernels/parallel_map_dataset_op.cc
+++ b/tensorflow/core/kernels/parallel_map_dataset_op.cc
@@ -188,7 +188,7 @@ class ParallelMapDatasetOp : public OpKernel {
 
           if (!output_buffer_.empty() && output_buffer_.front().is_produced) {
             // A new output element is available. Forward the status
-            // from computing it, and (if we sucessfully got an
+            // from computing it, and (if we successfully got an
             // element) the output values.
             Status s = output_buffer_.front().output_status;
             if (s.ok()) {