diff --git a/doc/fluid/api/layers.rst b/doc/fluid/api/layers.rst
index 5c02886efd7d1..3790f09c84563 100644
--- a/doc/fluid/api/layers.rst
+++ b/doc/fluid/api/layers.rst
@@ -815,3 +815,8 @@ zeros
 ..  autofunction:: paddle.fluid.layers.zeros
     :noindex:
 
+topk
+----
+
+..  autofunction:: paddle.fluid.layers.topk
+    :noindex:
diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py
index b9a53eda9144e..4b707973e2739 100644
--- a/python/paddle/fluid/layers/control_flow.py
+++ b/python/paddle/fluid/layers/control_flow.py
@@ -32,7 +32,6 @@
     'Switch',
     'lod_rank_table',
     'max_sequence_len',
-    'topk',
     'lod_tensor_to_array',
     'array_to_lod_tensor',
     'increment',
@@ -751,43 +750,6 @@ def max_sequence_len(rank_table):
     return res
 
 
-def topk(input, k):
-    """
-    **topk**
-
-    This function performs the operation that selects the k entries in the input
-    vector and outputs their values and indices as vectors. Thus topk_out[j] is
-    the j-th largest entry in input, and its index is topk_indices[j]
-
-    Args:
-        input (Variable|list): The input tensor that has all the data.
-        k (int): The number of top elements that the function will pick.
-
-    Returns:
-        Variable: The variable of type array that contains the k largest entries
-                  from input.
-        Variable: The variable of type array that contains the indices of k
-                  largest entries from input.
-
-    Examples:
-        .. code-block:: python
-
-          x = fluid.layers.data(name='x', shape=[10])
-          k = 5
-          array = fluid.layers.topk(x, k)
-    """
-    helper = LayerHelper('topk', **locals())
-    topk_out = helper.create_tmp_variable(dtype=input.dtype)
-    topk_indices = helper.create_tmp_variable(dtype='int64')
-    helper.append_op(
-        type='top_k',
-        inputs={'X': [input]},
-        outputs={'Out': [topk_out],
-                 'Indices': [topk_indices]},
-        attrs={'k': k})
-    return topk_out, topk_indices
-
-
 def lod_tensor_to_array(x, table):
     """ Convert a LOD_TENSOR to an LOD_TENSOR_ARRAY.
 
diff --git a/python/paddle/fluid/layers/metric.py b/python/paddle/fluid/layers/metric.py
index a60e411eafc47..cab2eb5551054 100644
--- a/python/paddle/fluid/layers/metric.py
+++ b/python/paddle/fluid/layers/metric.py
@@ -28,10 +28,10 @@
 def accuracy(input, label, k=1, correct=None, total=None):
     """
     This function computes the accuracy using the input and label.
-    The output is the top_k inputs and their indices.
+    The output is the top k inputs and their indices.
     """
     helper = LayerHelper("accuracy", **locals())
-    topk_out, topk_indices = nn.top_k(input, k=k)
+    topk_out, topk_indices = nn.topk(input, k=k)
     acc_out = helper.create_tmp_variable(dtype="float32")
     if correct is None:
         correct = helper.create_tmp_variable(dtype="int64")
@@ -62,12 +62,7 @@ def auc(input, label, curve='ROC', num_thresholds=200):
     helper = LayerHelper("auc", **locals())
     topk_out = helper.create_tmp_variable(dtype=input.dtype)
     topk_indices = helper.create_tmp_variable(dtype="int64")
-    helper.append_op(
-        type="top_k",
-        inputs={"X": [input]},
-        outputs={"Out": [topk_out],
-                 "Indices": [topk_indices]},
-        attrs={"k": k})
+    topk_out, topk_indices = nn.topk(input, k=k)
     auc_out = helper.create_tmp_variable(dtype="float32")
     if correct is None:
         correct = helper.create_tmp_variable(dtype="int64")
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 5bc9f129dadd4..a1e9d5b7b5fda 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -60,7 +60,7 @@
     'edit_distance',
     'l2_normalize',
     'matmul',
-    'top_k',
+    'topk',
     'warpctc',
     'sequence_reshape',
     'transpose',
@@ -2572,7 +2572,7 @@ def __check_input(x, y):
     return out
 
 
-def top_k(input, k):
+def topk(input, k):
     """
     This operator is used to find values and indices of the k largest entries
     for the last dimension.
@@ -2598,7 +2598,7 @@ def top_k(input, k):
     Examples:
         .. code-block:: python
 
-            top5_values, top5_indices = layers.top_k(input, k=5)
+            top5_values, top5_indices = layers.topk(input, k=5)
     """
     shape = input.shape
     if k < 1 and k >= shape[-1]:
@@ -2760,7 +2760,7 @@ def ctc_greedy_decoder(input, blank, name=None):
             cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
     """
     helper = LayerHelper("ctc_greedy_decoder", **locals())
-    _, topk_indices = top_k(input, k=1)
+    _, topk_indices = topk(input, k=1)
 
     # ctc align op
     ctc_out = helper.create_tmp_variable(dtype="int64")
diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py
index 37aaea7e64410..17d6afdee1614 100644
--- a/python/paddle/fluid/tests/unittests/test_layers.py
+++ b/python/paddle/fluid/tests/unittests/test_layers.py
@@ -354,7 +354,7 @@ def test_topk(self):
         program = Program()
         with program_guard(program):
             data = layers.data(name="label", shape=[200], dtype="float32")
-            values, indices = layers.top_k(data, k=5)
+            values, indices = layers.topk(data, k=5)
             self.assertIsNotNone(values)
             self.assertIsNotNone(indices)
         print(str(program))