diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py
index 10e279f767c143cc8e27022eabda7aad75c9170d..aae419566f051e894f25e83902099ee0f39a469c 100755
--- a/python/paddle/trainer_config_helpers/layers.py
+++ b/python/paddle/trainer_config_helpers/layers.py
@@ -1916,7 +1916,7 @@ def cos_sim(a, b, scale=1, size=1, name=None, layer_attr=None):
 @layer_support()
 def hsigmoid(input,
              label,
-             num_classes,
+             num_classes=None,
              name=None,
              bias_attr=None,
              param_attr=None,
@@ -1932,8 +1932,7 @@ def hsigmoid(input,
     ..  code-block:: python
 
         cost = hsigmoid(input=[layer1, layer2],
-                        label=data_layer,
-                        num_classes=3)
+                        label=data_layer)
 
     :param input: Input layers. It could be a LayerOutput or list/tuple of
                  LayerOutput.
@@ -1941,12 +1940,14 @@ def hsigmoid(input,
     :param label: Label layer.
     :type label: LayerOutput
     :param num_classes: number of classes.
-    :type num_classes: int
+    :type num_classes: int|None
     :param name: layer name
     :type name: basestring
     :param bias_attr: Bias attribute. None means default bias.
                       False means no bias.
     :type bias_attr: ParameterAttribute|False
+    :param param_attr: Parameter Attribute. None means default parameter.
+    :type param_attr: ParameterAttribute|None
     :param layer_attr: Extra Layer Attribute.
     :type layer_attr: ExtraLayerAttribute
     :return: LayerOutput object.
@@ -1966,6 +1967,11 @@ def hsigmoid(input,
     assert isinstance(label, LayerOutput)
     assert label.layer_type == LayerType.DATA
 
+    if num_classes is None:
+        num_classes = label.size
+    if num_classes is None or num_classes <= 2:
+        raise ValueError("hsigmoid label size must larger than 2.")
+
     ipts_for_layer = []
     parents = []
     for each_input, each_param_attr in zip(input, param_attr):