Consistent capitalization of Python in array_ops.py
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index e163cf9..62f5799 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -50,7 +50,7 @@
newaxis = None
tf_export("newaxis").export_constant(__name__, "newaxis")
-# We override the 'slice' for the "slice" op, so we keep python's
+# We override the 'slice' for the "slice" op, so we keep Python's
# existing 'slice' for later use in this module.
_BaseSlice = slice
@@ -296,7 +296,7 @@
"""Returns a tensor with a length 1 axis inserted at index `axis`.
Given a tensor `input`, this operation inserts a dimension of length 1 at the
- dimension index `axis` of `input`'s shape. The dimension index follows python
+ dimension index `axis` of `input`'s shape. The dimension index follows Python
indexing rules: It's zero-based, a negative index it is counted backward
from the end.
@@ -322,14 +322,14 @@
>>> tf.expand_dims(image, axis=1).shape.as_list()
[10, 1, 10, 3]
- Following standard python indexing rules, a negative `axis` counts from the
+ Following standard Python indexing rules, a negative `axis` counts from the
end so `axis=-1` adds an inner most dimension:
>>> tf.expand_dims(image, -1).shape.as_list()
[10, 10, 3, 1]
This operation requires that `axis` is a valid index for `input.shape`,
- following python indexing rules:
+ following Python indexing rules:
```
-1-tf.rank(input) <= axis <= tf.rank(input)
@@ -368,7 +368,7 @@
"""Returns a tensor with a length 1 axis inserted at index `axis`.
Given a tensor `input`, this operation inserts a dimension of length 1 at the
- dimension index `axis` of `input`'s shape. The dimension index follows python
+ dimension index `axis` of `input`'s shape. The dimension index follows Python
indexing rules: It's zero-based, a negative index it is counted backward
from the end.
@@ -394,14 +394,14 @@
>>> tf.expand_dims(image, axis=1).shape.as_list()
[10, 1, 10, 3]
- Following standard python indexing rules, a negative `axis` counts from the
+ Following standard Python indexing rules, a negative `axis` counts from the
end so `axis=-1` adds an inner most dimension:
>>> tf.expand_dims(image, -1).shape.as_list()
[10, 10, 3, 1]
This operation requires that `axis` is a valid index for `input.shape`,
- following python indexing rules:
+ following Python indexing rules:
```
-1-tf.rank(input) <= axis <= tf.rank(input)
@@ -1072,7 +1072,7 @@
shrink_axis_mask=0,
var=None,
name=None):
- """Extracts a strided slice of a tensor (generalized python array indexing).
+ """Extracts a strided slice of a tensor (generalized Python array indexing).
See also `tf.slice`.
@@ -2220,7 +2220,7 @@
# If we know the number of dimensions (statically), we can do two things:
# 1. Check that `a` is a (batch) matrix.
- # 2. Use a python list for perm. This preserves static shape information
+ # 2. Use a Python list for perm. This preserves static shape information
# and avoids extra computations.
a_shape = a.get_shape()
ndims = a_shape.ndims
