diff --git a/configs/vqa/re/layoutxlm.yml b/configs/vqa/re/layoutxlm.yml
index bb367f0e64db6b67a77a555c61049c6b580e23a2..8d0ffadf0607687c96b2e3ecdb9fae059af6bf51 100644
--- a/configs/vqa/re/layoutxlm.yml
+++ b/configs/vqa/re/layoutxlm.yml
@@ -36,7 +36,7 @@ Optimizer:
   lr:
     learning_rate: 0.00005
   regularizer:
-    name: Const
+    name: L2
     factor: 0.00000
     
 PostProcess:
diff --git a/configs/vqa/ser/layoutlm.yml b/configs/vqa/ser/layoutlm.yml
index a635fc7dbec1e1364aca85fe660c6fe44433da5b..f29153a2e9bea44f758a38558aa4c45a25f53213 100644
--- a/configs/vqa/ser/layoutlm.yml
+++ b/configs/vqa/ser/layoutlm.yml
@@ -38,7 +38,7 @@ Optimizer:
     epochs: *epoch_num
     warmup_epoch: 2
   regularizer:
-    name: Const
+    name: L2
     factor: 0.00000
     
 PostProcess:
diff --git a/configs/vqa/ser/layoutxlm.yml b/configs/vqa/ser/layoutxlm.yml
index 1c1eac2289384990fc914f85d9f2a9233cda7440..14041eb26246f396b15f3b11754bda36609a47b9 100644
--- a/configs/vqa/ser/layoutxlm.yml
+++ b/configs/vqa/ser/layoutxlm.yml
@@ -38,7 +38,7 @@ Optimizer:
     epochs: *epoch_num
     warmup_epoch: 2
   regularizer:
-    name: Const
+    name: L2
     factor: 0.00000
     
 PostProcess:
diff --git a/ppocr/optimizer/regularizer.py b/ppocr/optimizer/regularizer.py
index 83010e6544fb2a09c3fccdaa38f9f9da0c468fad..d48831fc2ea435f3fcb50161f5c778d9a1638452 100644
--- a/ppocr/optimizer/regularizer.py
+++ b/ppocr/optimizer/regularizer.py
@@ -29,39 +29,23 @@ class L1Decay(object):
 
     def __init__(self, factor=0.0):
         super(L1Decay, self).__init__()
-        self.regularization_coeff = factor
+        self.coeff = factor
 
     def __call__(self):
-        reg = paddle.regularizer.L1Decay(self.regularization_coeff)
+        reg = paddle.regularizer.L1Decay(self.coeff)
         return reg
 
 
 class L2Decay(object):
     """
-    L2 Weight Decay Regularization, which encourages the weights to be sparse.
+    L2 Weight Decay Regularization, which helps to prevent the model over-fitting.
     Args:
         factor(float): regularization coeff. Default:0.0.
     """
 
     def __init__(self, factor=0.0):
         super(L2Decay, self).__init__()
-        self.regularization_coeff = factor
+        self.coeff = factor
 
     def __call__(self):
-        reg = paddle.regularizer.L2Decay(self.regularization_coeff)
-        return reg
-
-
-class ConstDecay(object):
-    """
-    Const L2 Weight Decay Regularization, which encourages the weights to be sparse.
-    Args:
-        factor(float): regularization coeff. Default:0.0.
-    """
-
-    def __init__(self, factor=0.0):
-        super(ConstDecay, self).__init__()
-        self.regularization_coeff = factor
-
-    def __call__(self):
-        return self.regularization_coeff
+        return self.coeff
\ No newline at end of file