diff --git a/ppcls/optimizer/learning_rate.py b/ppcls/optimizer/learning_rate.py
index 0227ae43e0f5c99894dcaeb1b51425fbdc9a8c82..87c08e747b52f91f555a5787390d5d55c617278f 100644
--- a/ppcls/optimizer/learning_rate.py
+++ b/ppcls/optimizer/learning_rate.py
@@ -112,35 +112,19 @@ class CosineWarmup(object):
         self.lr = lr
         self.step_each_epoch = step_each_epoch
         self.epochs = epochs
-        self.warmup_epoch = fluid.layers.fill_constant(
-            shape=[1],
-            value=float(warmup_epoch),
-            dtype='float32',
-            force_cpu=True)
+        self.warmup_epoch = warmup_epoch
 
     def __call__(self):
-        global_step = _decay_step_counter()
-        learning_rate = fluid.layers.tensor.create_global_var(
-            shape=[1],
-            value=0.0,
-            dtype='float32',
-            persistable=True,
-            name="learning_rate")
-        epoch = ops.floor(global_step / self.step_each_epoch)
-        with fluid.layers.control_flow.Switch() as switch:
-            with switch.case(epoch < self.warmup_epoch):
-                decayed_lr = self.lr * \
-                    (global_step / (self.step_each_epoch * self.warmup_epoch))
-                fluid.layers.tensor.assign(
-                    input=decayed_lr, output=learning_rate)
-            with switch.default():
-                current_step = global_step - self.warmup_epoch * self.step_each_epoch
-                total_step = (
-                    self.epochs - self.warmup_epoch) * self.step_each_epoch
-                decayed_lr = self.lr * \
-                    (ops.cos(current_step * math.pi / total_step) + 1) / 2
-                fluid.layers.tensor.assign(
-                    input=decayed_lr, output=learning_rate)
+        learning_rate = fluid.layers.cosine_decay(
+            learning_rate=self.lr,
+            step_each_epoch=self.step_each_epoch,
+            epochs=self.epochs)
+
+        learning_rate = fluid.layers.linear_lr_warmup(
+            learning_rate,
+            warmup_steps=self.warmup_epoch * self.step_each_epoch,
+            start_lr=0.0,
+            end_lr=self.lr)
 
         return learning_rate
 
@@ -169,37 +153,22 @@ class ExponentialWarmup(object):
         super(ExponentialWarmup, self).__init__()
         self.lr = lr
         self.step_each_epoch = step_each_epoch
-        self.decay_epochs = decay_epochs * self.step_each_epoch
+        self.decay_epochs = decay_epochs
         self.decay_rate = decay_rate
-        self.warmup_epoch = fluid.layers.fill_constant(
-            shape=[1],
-            value=float(warmup_epoch),
-            dtype='float32',
-            force_cpu=True)
+        self.warmup_epoch = warmup_epoch
 
     def __call__(self):
-        global_step = _decay_step_counter()
-        learning_rate = fluid.layers.tensor.create_global_var(
-            shape=[1],
-            value=0.0,
-            dtype='float32',
-            persistable=True,
-            name="learning_rate")
-
-        epoch = ops.floor(global_step / self.step_each_epoch)
-        with fluid.layers.control_flow.Switch() as switch:
-            with switch.case(epoch < self.warmup_epoch):
-                decayed_lr = self.lr * \
-                    (global_step / (self.step_each_epoch * self.warmup_epoch))
-                fluid.layers.tensor.assign(
-                    input=decayed_lr, output=learning_rate)
-            with switch.default():
-                rest_step = global_step - self.warmup_epoch * self.step_each_epoch
-                div_res = ops.floor(rest_step / self.decay_epochs)
-
-                decayed_lr = self.lr * (self.decay_rate**div_res)
-                fluid.layers.tensor.assign(
-                    input=decayed_lr, output=learning_rate)
+        learning_rate = fluid.layers.exponential_decay(
+            learning_rate=self.lr,
+            decay_steps=self.decay_epochs * self.step_each_epoch,
+            decay_rate=self.decay_rate,
+            staircase=False)
+
+        learning_rate = fluid.layers.linear_lr_warmup(
+            learning_rate,
+            warmup_steps=self.warmup_epoch * self.step_each_epoch,
+            start_lr=0.0,
+            end_lr=self.lr)
 
         return learning_rate