diff --git a/ppocr/optimizer.py b/ppocr/optimizer.py
index fd315cd1319d4925e893705957a42f931a39076e..90d402a6da9bc3942f1c0118f43ed7d8dd1e3597 100644
--- a/ppocr/optimizer.py
+++ b/ppocr/optimizer.py
@@ -29,9 +29,17 @@ def cosine_decay_with_warmup(learning_rate,
                              step_each_epoch,
                              epochs=500,
                              warmup_minibatch=1000):
-    """Applies cosine decay to the learning rate.
+    """
+    Applies cosine decay to the learning rate.
     lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
     decrease lr for every mini-batch and start with warmup.
+    args:
+        learning_rate(float): initial learning rate
+        step_each_epoch (int): number of step for each epoch in training process
+        epochs(int): number of training epochs
+        warmup_minibatch(int): number of minibatch for warmup
+    return:
+        lr(tensor): learning rate tensor
     """
     global_step = _decay_step_counter()
     lr = fluid.layers.tensor.create_global_var(
@@ -65,6 +73,7 @@ def AdamDecay(params, parameter_list=None):
         params(dict): the super parameters
         parameter_list (list): list of Variable names to update to minimize loss
     return:
+        optimizer: a Adam optimizer instance
     """
     base_lr = params['base_lr']
     beta1 = params['beta1']
@@ -121,6 +130,7 @@ def RMSProp(params, parameter_list=None):
         params(dict): the super parameters
         parameter_list (list): list of Variable names to update to minimize loss
     return:
+        optimizer: a RMSProp optimizer instance
     """
     base_lr = params.get("base_lr", 0.001)
     l2_decay = params.get("l2_decay", 0.00005)