diff --git a/tensorflow/python/ops/special_math_ops.py b/tensorflow/python/ops/special_math_ops.py
index ad44d428fb4..d8e43d41891 100644
--- a/tensorflow/python/ops/special_math_ops.py
+++ b/tensorflow/python/ops/special_math_ops.py
@@ -252,7 +252,7 @@ def einsum(equation, *inputs, **kwargs):
         [format(key) for key in sorted(list(kwargs.keys()))]))
   with ops.name_scope(name, 'einsum', [equation, inputs]) as name:
     inputs = list(inputs)
-    input_shapes = [x.get_shape() for x in inputs]
+    input_shapes = [x.shape for x in inputs]
     input_axis_labels, output_axis_labels = _einsum_parse_and_resolve_equation(
         equation, input_shapes)
 
@@ -417,14 +417,14 @@ def _einsum_reduction(t0, t0_axis_labels, t1, t1_axis_labels, axes_to_sum):
       `t0_axis_labels`, or that of `t1` does not match the length of
       `t1_axis_labels`.
   """
-  if len(t0_axis_labels) != len(t0.get_shape()):
+  if len(t0_axis_labels) != len(t0.shape):
     raise ValueError(
         'Tensor t0 of rank %d does not match einsum reduction of length %d' %
-        (len(t0.get_shape()), len(t0_axis_labels)))
-  if len(t1_axis_labels) != len(t1.get_shape()):
+        (len(t0.shape), len(t0_axis_labels)))
+  if len(t1_axis_labels) != len(t1.shape):
     raise ValueError(
         'Tensor t1 of rank %d does not match einsum reduction of length %d' %
-        (len(t1.get_shape()), len(t1_axis_labels)))
+        (len(t1.shape), len(t1_axis_labels)))
 
   # This function computes the result of a two-argument einsum() using batch
   # matrix multiplication.  This involves
@@ -531,7 +531,7 @@ def _reshape_if_necessary(tensor, new_shape):
   """Like reshape(), but avoids creating a new tensor if possible."""
   # Accept None as an alias for -1 in new_shape.
   new_shape = tuple(-1 if x is None else x for x in new_shape)
-  cur_shape = tuple(x.value for x in tensor.get_shape().dims)
+  cur_shape = tuple(x.value for x in tensor.shape.dims)
   if (len(new_shape) == len(cur_shape) and
       all(not isinstance(d1, ops.Tensor) and (d0 == d1 or d1 == -1)
           for d0, d1 in zip(cur_shape, new_shape))):
@@ -544,7 +544,7 @@ def _get_shape(tensor):
   """Like get_shape().as_list(), but explicitly queries the shape of a tensor
   if necessary to ensure that the returned value contains no unknown value."""
 
-  shape = tensor.get_shape().as_list()
+  shape = tensor.shape.as_list()
   none_indices = [i for i, d in enumerate(shape) if d is None]
   if none_indices:
     # Query the shape if shape contains None values
@@ -569,7 +569,7 @@ def _total_size(shape_values):
 def _exponential_space_einsum(equation, *inputs):
   """Fallback implementation that supports summing an index over > 2 inputs."""
   inputs = list(inputs)
-  input_shapes = [x.get_shape() for x in inputs]
+  input_shapes = [x.shape for x in inputs]
   idx_in, idx_out = _einsum_parse_and_resolve_equation(equation, input_shapes)
 
   idx_all = set(''.join(idx_in) + idx_out)
@@ -588,11 +588,11 @@ def _exponential_space_einsum(equation, *inputs):
 
   # transpose inputs so axes are in order
   for i, (input_, axes_) in enumerate(zip(inputs, idx_in)):
-    if input_.get_shape().ndims != len(axes_):
+    if input_.shape.ndims != len(axes_):
       raise ValueError(
           'Input %d with axes %s has incorrect' \
           ' number of dimensions (expected %d, got %d)' % (
-              i, axes_, len(axes_), input_.get_shape().ndims
+              i, axes_, len(axes_), input_.shape.ndims
           )
       )
 
@@ -609,7 +609,7 @@ def _exponential_space_einsum(equation, *inputs):
 
   reduction_idx = []
   shapes = [[dim if dim else -1
-             for dim in tensor.get_shape().as_list()]
+             for dim in tensor.shape.as_list()]
             for tensor in inputs]
 
   # validate shapes for broadcasting