1 files changed, 86 insertions, 0 deletions
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index 377adcdab8..93a9656950 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -30,6 +30,8 @@
 @@sparse_reset_shape
 @@sparse_fill_empty_rows
 @@sparse_transpose
+@@sparse_reduce_max
+@@sparse_reduce_max_sparse
 @@sparse_reduce_sum
 @@sparse_reduce_sum_sparse
 @@sparse_add
@@ -710,6 +712,90 @@ def sparse_to_dense(sparse_indices,
       name=name)
 
 
+def sparse_reduce_max(sp_input, axis=None, keep_dims=False,
+                      reduction_axes=None):
+  """Computes the max of elements across dimensions of a SparseTensor.
+
+  This Op takes a SparseTensor and is the sparse counterpart to
+  `tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
+  instead of a sparse one.
+
+  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+  with length 1.
+
+  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+  with a single element is returned.  Additionally, the axes can be negative,
+  similar to the indexing rules in Python.
+
+  For example:
+
+  ```python
+  # 'x' represents [[1, ?, 2]
+  #                 [?, 3, ?]]
+  # where ? is implicitly-zero.
+  tf.sparse_reduce_max(x) ==> 3
+  tf.sparse_reduce_max(x, 0) ==> [1, 3, 2]
+  tf.sparse_reduce_max(x, 1) ==> [2, 3]  # Can also use -1 as the axis.
+  tf.sparse_reduce_max(x, 1, keep_dims=True) ==> [[2], [3]]
+  tf.sparse_reduce_max(x, [0, 1]) ==> 3
+  ```
+
+  Args:
+    sp_input: The SparseTensor to reduce. Should have numeric type.
+    axis: The dimensions to reduce; list or scalar. If `None` (the
+      default), reduces all dimensions.
+    keep_dims: If true, retain reduced dimensions with length 1.
+    reduction_axes: Deprecated name of axis.
+
+  Returns:
+    The reduced Tensor.
+  """
+  return gen_sparse_ops.sparse_reduce_max(
+      sp_input.indices, sp_input.values,
+      sp_input.dense_shape,
+      math_ops._ReductionDims(sp_input, axis, reduction_axes),
+      keep_dims)
+
+
+def sparse_reduce_max_sparse(sp_input, axis=None, keep_dims=False,
+                             reduction_axes=None):
+  """Computes the max of elements across dimensions of a SparseTensor.
+
+  This Op takes a SparseTensor and is the sparse counterpart to
+  `tf.reduce_max()`.  In contrast to SparseReduceSum, this Op returns a
+  SparseTensor.
+
+  Reduces `sp_input` along the dimensions given in `reduction_axes`.  Unless
+  `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
+  `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained
+  with length 1.
+
+  If `reduction_axes` has no entries, all dimensions are reduced, and a tensor
+  with a single element is returned.  Additionally, the axes can be negative,
+  which are interpreted according to the indexing rules in Python.
+
+  Args:
+    sp_input: The SparseTensor to reduce. Should have numeric type.
+    axis: The dimensions to reduce; list or scalar. If `None` (the
+      default), reduces all dimensions.
+    keep_dims: If true, retain reduced dimensions with length 1.
+    reduction_axes: Deprecated name of axis
+
+  Returns:
+    The reduced SparseTensor.
+  """
+  output_ind, output_val, output_shape = (
+      gen_sparse_ops.sparse_reduce_max_sparse(
+          sp_input.indices, sp_input.values,
+          sp_input.dense_shape, math_ops._ReductionDims(sp_input, axis,
+                                                        reduction_axes),
+          keep_dims))
+
+  return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
+
+
 def sparse_reduce_sum(sp_input, axis=None, keep_dims=False,
                       reduction_axes=None):
   """Computes the sum of elements across dimensions of a SparseTensor.