add some activation api examples.

python/paddle/fluid/layers/nn.py

@@ -6833,6 +6833,13 @@ def elu(x, alpha=1.0, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+        x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+        y = fluid.layers.elu(x, alpha=0.2)
     """
     helper = LayerHelper('elu', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6856,6 +6863,13 @@ def relu6(x, threshold=6.0, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+        x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+        y = fluid.layers.relu6(x, threshold=6.0)
     """
     helper = LayerHelper('relu6', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6879,6 +6893,13 @@ def pow(x, factor=1.0, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+        x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+        y = fluid.layers.pow(x, factor=2.0)
     """
     helper = LayerHelper('pow', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6903,6 +6924,13 @@ def stanh(x, scale_a=2.0 / 3.0, scale_b=1.7159, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+        x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+        y = fluid.layers.stanh(x, scale_a=0.6667, scale_b=1.7159)
     """
     helper = LayerHelper('stanh', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6928,6 +6956,13 @@ def hard_sigmoid(x, slope=0.2, offset=0.5, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+        x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+        y = fluid.layers.hard_sigmoid(x, slope=0.3, offset=0.8)
     """
     helper = LayerHelper('hard_sigmoid', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6952,6 +6987,13 @@ def swish(x, beta=1.0, name=None):
 
     Returns:
         output(${out_type}): ${out_comment}
+
+    Examples:
+
+        .. code-block:: python
+
+        x = fluid.layers.data(name="x", shape=[3,10,32,32], dtype="float32")
+        y = fluid.layers.swish(x, beta=1.2)
     """
     helper = LayerHelper('swish', **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
@@ -6988,8 +7030,8 @@ def prelu(x, mode, param_attr=None, name=None):
         .. code-block:: python
 
          x = fluid.layers.data(name="x", shape=[10,10], dtype="float32")
-            mode = 'channel'
-            output = fluid.layers.prelu(x,mode)
+         mode = 'channel'
+         output = fluid.layers.prelu(x,mode)
     """
     helper = LayerHelper('prelu', **locals())
     if mode not in ['all', 'channel', 'element']: