1 files changed, 7 insertions, 7 deletions
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 361667ec49..a917f51087 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -538,7 +538,7 @@ def slice(input_, begin, size, name=None):
   words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
   want to slice from.
 
-  Note that @{tf.Tensor.__getitem__} is typically a more pythonic way to
+  Note that `tf.Tensor.__getitem__` is typically a more pythonic way to
   perform slices, as it allows you to write `foo[3:7, :-2]` instead of
   `tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])`.
 
@@ -594,7 +594,7 @@ def strided_slice(input_,
 
   **Instead of calling this op directly most users will want to use the
   NumPy-style slicing syntax (e.g. `tensor[..., 3:4:-1, tf.newaxis, 3]`), which
-  is supported via @{tf.Tensor.__getitem__} and @{tf.Variable.__getitem__}.**
+  is supported via `tf.Tensor.__getitem__` and `tf.Variable.__getitem__`.**
   The interface of this op is a low-level encoding of the slicing syntax.
 
   Roughly speaking, this op extracts a slice of size `(end-begin)/stride`
@@ -636,10 +636,10 @@ def strided_slice(input_,
   `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor.
 
   If the ith bit of `shrink_axis_mask` is set, it implies that the ith
-  specification shrinks the dimensionality by 1. `begin[i]`, `end[i]` and
-  `strides[i]` must imply a slice of size 1 in the dimension. For example in
-  Python one might do `foo[:, 3, :]` which would result in
-  `shrink_axis_mask` equal to 2.
+  specification shrinks the dimensionality by 1, taking on the value at index
+  `begin[i]`. `end[i]` and `strides[i]` are ignored in this case. For example in
+  Python one might do `foo[:, 3, :]` which would result in `shrink_axis_mask`
+  equal to 2.
 
 
   NOTE: `begin` and `end` are zero-indexed.
@@ -723,7 +723,7 @@ def _SliceHelperVar(var, slice_spec):
   """Creates a slice helper object given a variable.
 
   This allows creating a sub-tensor from part of the current contents
-  of a variable. See @{tf.Tensor.__getitem__} for detailed examples
+  of a variable. See `tf.Tensor.__getitem__` for detailed examples
   of slicing.
 
   This function in addition also allows assignment to a sliced range.