diff --git a/mnist.py b/mnist.py
index 787499a7d708c442d55df2f899cbaa7f6581e93b..617fd18586b332c05e293b798d7b7dca13322080 100644
--- a/mnist.py
+++ b/mnist.py
@@ -118,8 +118,6 @@ if __name__ == '__main__':
         guard = null_guard()
 
     with guard:
-        # sgd = SGDOptimizer(learning_rate=1e-3)
-        sgd = MomentumOptimizer(learning_rate=1e-3, momentum=0.9)
         train_loader = fluid.io.xmap_readers(
             lambda b: [np.array([x[0] for x in b]).reshape(-1, 1, 28, 28),
                        np.array([x[1] for x in b]).reshape(-1, 1)],
@@ -131,6 +129,9 @@ if __name__ == '__main__':
             paddle.batch(paddle.dataset.mnist.test(),
                          batch_size=4, drop_last=True), 1, 1)
         model = MNIST()
+        sgd = MomentumOptimizer(learning_rate=1e-3, momentum=0.9,
+                                parameter_list=model.parameters())
+        # sgd = SGDOptimizer(learning_rate=1e-3)
         model.prepare(sgd, 'cross_entropy')
 
         for e in range(2):
diff --git a/model.py b/model.py
index 5cf0a647bf1592970b84238c1514933c218bd47c..58f5446fe35f5a901c30eb9e81c4d7775a913373 100644
--- a/model.py
+++ b/model.py
@@ -110,6 +110,9 @@ class StaticGraphAdapter(object):
         self.mode = 'test'
         return self._run(inputs, None, device, device_ids)
 
+    def parameters(self, *args, **kwargs):
+        return None
+
     def save(self, path):
         prog = self._progs.get('train', None)
         if prog is None or self.model._optimizer is None:
@@ -287,6 +290,9 @@ class DynamicGraphAdapter(object):
         inputs = to_list(inputs)
         return self.model.forward(*[to_variable(x) for x in inputs])
 
+    def parameters(self, *args, **kwargs):
+        return super(Model, self.model).parameters(*args, **kwargs)
+
     def save(self, path):
         params = self.model.state_dict()
         fluid.save_dygraph(params, path)
@@ -346,3 +352,6 @@ class Model(fluid.dygraph.Layer):
     def prepare(self, optimizer, loss_functions):
         self._optimizer = optimizer
         self._loss_functions = to_list(loss_functions)
+
+    def parameters(self, *args, **kwargs):
+        return self._adapter.parameters(*args, **kwargs)