diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py
index 20508f37eb7..de29cc53c1f 100644
--- a/tensorflow/python/framework/tensor_shape.py
+++ b/tensorflow/python/framework/tensor_shape.py
@@ -17,6 +17,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import functools
+import operator
 import six
 
 from tensorflow.core.framework import tensor_shape_pb2
@@ -916,10 +918,7 @@ class TensorShape(object):
   def num_elements(self):
     """Returns the total number of elements, or none for incomplete shapes."""
     if self.is_fully_defined():
-      size = 1
-      for dim in self._dims:
-        size *= dim.value
-      return size
+      return functools.reduce(operator.mul, self.as_list(), 1)
     else:
       return None
 
@@ -942,19 +941,20 @@ class TensorShape(object):
     other = as_shape(other)
     if self._dims is None:
       return other
+    if other.dims is None:
+      return self
     else:
       try:
         self.assert_same_rank(other)
-        new_dims = []
-        for i, dim in enumerate(self._dims):
-          new_dims.append(dim.merge_with(other[i]))
+        new_dims = [
+            dim.merge_with(other_dim)
+            for dim, other_dim in zip(self._dims, other.dims)
+        ]
         return TensorShape(new_dims)
       except ValueError:
         raise ValueError("Shapes %s and %s are not compatible" % (self, other))
 
   def __add__(self, other):
-    if not isinstance(other, TensorShape):
-      other = TensorShape(other)
     return self.concatenate(other)
 
   def __radd__(self, other):
@@ -1157,10 +1157,10 @@ class TensorShape(object):
     if self._dims is None or other.dims is None or self.rank != other.rank:
       return unknown_shape()
 
-    dims = [(Dimension(None))] * self.rank
-    for i, (d1, d2) in enumerate(zip(self._dims, other.dims)):
-      if d1 is not None and d2 is not None and d1 == d2:
-        dims[i] = d1
+    dims = [
+        d1 if d1 is not None and d2 is not None and d1 == d2 else None
+        for d1, d2 in zip(self._dims, other.dims)
+    ]
     return TensorShape(dims)
 
   def is_fully_defined(self):