Fix typo (#17258)

* fix typo

diff --git a/tensorflow/contrib/slim/python/slim/data/parallel_reader.py b/tensorflow/contrib/slim/python/slim/data/parallel_reader.py
index ad5e985..b3343ae 100644 (file)
--- a/tensorflow/contrib/slim/python/slim/data/parallel_reader.py
+++ b/tensorflow/contrib/slim/python/slim/data/parallel_reader.py
@@ -221,7 +221,7 @@ def parallel_read(data_sources,
         the data will be cycled through indefinitely.
     num_readers: a integer, number of Readers to create.
     reader_kwargs: an optional dict, of kwargs for the reader.
-    shuffle: boolean, wether should shuffle the files and the records by using
+    shuffle: boolean, whether should shuffle the files and the records by using
       RandomShuffleQueue as common_queue.
     dtypes:  A list of types.  The length of dtypes must equal the number
         of elements in each record. If it is None it will default to

diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py
index 343d158..8cb9f9e 100644 (file)
--- a/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py
+++ b/tensorflow/python/kernel_tests/linalg/linear_operator_diag_test.py
@@ -129,7 +129,7 @@ class LinearOperatorDiagTest(
     with self.test_session() as sess:
       x = random_ops.random_normal(shape=(2, 2, 3, 4))
 
-      # This LinearOperatorDiag will be brodacast to (2, 2, 3, 3) during solve
+      # This LinearOperatorDiag will be broadcast to (2, 2, 3, 3) during solve
       # and matmul with 'x' as the argument.
       diag = random_ops.random_uniform(shape=(2, 1, 3))
       operator = linalg.LinearOperatorDiag(diag, is_self_adjoint=True)

diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py
index bed4cbb..1d605c5 100644 (file)
--- a/tensorflow/python/ops/distributions/special_math.py
+++ b/tensorflow/python/ops/distributions/special_math.py
@@ -213,7 +213,7 @@ def _ndtri(p):
 
   # Compute x for p <= exp(-2): x = z - log(z)/z - (1/z) P(1/z) / Q(1/z),
   # where z = sqrt(-2. * log(p)), and P/Q are chosen between two different
-  # arrays based on wether p < exp(-32).
+  # arrays based on whether p < exp(-32).
   z = math_ops.sqrt(-2. * math_ops.log(sanitized_mcp))
   first_term = z - math_ops.log(z) / z
   second_term_small_p = (_create_polynomial(1. / z, p2)

diff --git a/tensorflow/python/ops/linalg/linear_operator_diag.py b/tensorflow/python/ops/linalg/linear_operator_diag.py
index b3ec3d5..e180e83 100644 (file)
--- a/tensorflow/python/ops/linalg/linear_operator_diag.py
+++ b/tensorflow/python/ops/linalg/linear_operator_diag.py
@@ -67,7 +67,7 @@ class LinearOperatorDiag(linear_operator.LinearOperator):
   operator = LinearOperatorDiag(diag)
 
   # Create a shape [2, 1, 4, 2] vector.  Note that this shape is compatible
-  # since the batch dimensions, [2, 1], are brodcast to
+  # since the batch dimensions, [2, 1], are broadcast to
   # operator.batch_shape = [2, 3].
   y = tf.random_normal(shape=[2, 1, 4, 2])
   x = operator.solve(y)