diff --git a/python/paddle/fluid/imperative/learning_rate_scheduler.py b/python/paddle/fluid/imperative/learning_rate_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..5393090cde5b2755ea3e71a46ec911e1b12e7e91
--- /dev/null
+++ b/python/paddle/fluid/imperative/learning_rate_scheduler.py
@@ -0,0 +1,68 @@
+# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+from .. import layers
+from .. import unique_name
+
+__all__ = [
+    'ExponentialDecay', 'NaturalExpDecay', 'InverseTimeDecay',
+    'PolynomialDecay', 'PiecewiseDecay', 'NoamDecay'
+]
+
+
+class LearningRateDecay(object):
+    """
+    Base class of learning rate decay
+    """
+
+    def __init__(self, step, dtype='float32'):
+        self.step = step
+        self.dtype = dtype
+
+    def __call__(self):
+        lr = self.step()
+        if isinstance(lr, float):
+            lr = self._create_lr_var(lr)
+        self.step += 1
+        return lr
+
+    def create_lr_var(lr):
+        lr = layers.create_global_var(
+            name=unique_name.generate("learning_rate"),
+            shape=[1],
+            value=float(lr),
+            dtype=self.dtype,
+            persistable=True)
+
+    def step(self):
+        raise NotImplementedError()
+
+
+class PiecewiseDecay(object):
+    def __init__(self, boundaries, values, step, dtype='float32'):
+        super(PiecewiseDecay, self).__init__(step, dtype)
+        self.boundaries = boundaries
+        self.values = values
+
+        self.vars = []
+        for value in values:
+            self.vars.append(self.create_lr_var(value))
+
+    def step(self):
+        for i in range(len(boundaries)):
+            if self.step <= boundaries[i]:
+                return self.vars[i]
+        return self.vars[len(values) - 1]
diff --git a/python/paddle/fluid/layers/learning_rate_scheduler.py b/python/paddle/fluid/layers/learning_rate_scheduler.py
index 2f489e43db1eeaa89e72d3946b904a71a91ef236..521e4ceb60bb15bd7f74346907f7d8f8ee4effef 100644
--- a/python/paddle/fluid/layers/learning_rate_scheduler.py
+++ b/python/paddle/fluid/layers/learning_rate_scheduler.py
@@ -29,6 +29,7 @@ from . import tensor
 from ..initializer import init_on_cpu
 from ..framework import default_main_program, Parameter, unique_name, name_scope
 from ..imperative import base as imperative_base
+from ..imperative import learning_rate_scheduler as imperate_lr
 
 __all__ = [
     'exponential_decay', 'natural_exp_decay', 'inverse_time_decay',
@@ -279,7 +280,7 @@ def piecewise_decay(boundaries, values):
             raise ValueError("len(values) - len(boundaries) should be 1")
 
         if imperative_base.enabled():
-            decay = imperative.PiecewiseDecay(boundaries, values, 0)
+            decay = imperate_lr.PiecewiseDecay(boundaries, values, 0)
             return decay
         else:
             global_step = _decay_step_counter()