Add doc for layers.auc

python/paddle/fluid/layers/metric.py

@@ -53,6 +53,43 @@ def accuracy(input, label, k=1, correct=None, total=None):
 
 
 def auc(input, label, curve='ROC', num_thresholds=200):
+    """
+    **Area Under The Curve (AUC) Layer**
+
+    This implementation computes the AUC according to forward output and label.
+    It is used very widely in binary classification evaluation. 
+
+    As a note: If input label contains values other than 0 and 1, it will be 
+    cast to bool. You can find the relevant definitions `here 
+     <https://en.wikipedia.org/wiki/Receiver_operating_characteristic
+      #Area_under_the_curve>`_.
+
+    There are two types of possible curves:
+    1. ROC: Receiver operating characteristic
+    2. PR: Precision Recall
+
+    Args:
+        input(Variable): A floating-point 2D Variable, values are in the range 
+                         [0, 1]. Each row is sorted in descending order. This 
+                         input should be the output of topk. Typically, this 
+                         Variable indicates the probability of each label.
+        label(Variable): A 2D int Variable indicating the label of the training 
+                         data. The height is batch size and width is always 1.
+        curve(str): Curve type, can be 'ROC' or 'PR'. Default 'ROC'.
+        num_thresholds(int): The number of thresholds to use when discretizing 
+                             the roc curve. Default 200.
+
+    Returns:
+        Variable: A scalar representing the current AUC.
+
+    Examples:
+        .. code-block:: python
+        
+        # network is a binary classification model and label the ground truth
+        prediction = network(image, is_infer=True)
+        auc_out=fluid.layers.auc(input=prediction, label=label)
+    """
+
     warnings.warn(
         "This interface not recommended, fluid.layers.auc compute the auc at every minibatch, \
         but can not aggregate them and get the pass AUC, because pass \