diff --git a/demo/mnist/light_mnist.py b/demo/mnist/light_mnist.py
new file mode 100644
index 0000000000000000000000000000000000000000..271f73cd4d155f7c4c17eaf20de5c4ceef7905a7
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+++ b/demo/mnist/light_mnist.py
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+from paddle.trainer_config_helpers import *
+
+is_predict = get_config_arg("is_predict", bool, False)
+
+####################Data Configuration ##################
+
+if not is_predict:
+    data_dir = './data/'
+    define_py_data_sources2(
+        train_list=data_dir + 'train.list',
+        test_list=data_dir + 'test.list',
+        module='mnist_provider',
+        obj='process')
+
+######################Algorithm Configuration #############
+# settings(
+#    batch_size=128,
+#    learning_rate=0.1 / 128.0,
+#    learning_method=MomentumOptimizer(0.9),
+#    regularization=L2Regularization(0.0005 * 128))
+settings(
+    batch_size=50,
+    learning_rate=0.001,
+    learning_method=AdamOptimizer())
+
+#######################Network Configuration #############
+
+data_size = 1 * 28 * 28
+label_size = 10
+img = data_layer(name='pixel', size=data_size)
+
+# small_vgg is predined in trainer_config_helpers.network
+# predict = small_vgg(input_image=img, num_channels=1, num_classes=label_size)
+
+# light cnn
+def light_cnn(input_image, num_channels, num_classes):
+    def __light__(ipt, num_filter=128, times=1, conv_filter_size=3, dropouts=0, num_channels_=None):
+        return img_conv_group(
+            input=ipt,
+            num_channels=num_channels_,
+            pool_size=2,
+            pool_stride=2,
+            conv_padding=0,
+            conv_num_filter=[num_filter] * times,
+            conv_filter_size=conv_filter_size,
+            conv_act=ReluActivation(),
+            conv_with_batchnorm=True,
+            conv_batchnorm_drop_rate=dropouts,
+            pool_type=MaxPooling())
+
+    tmp = __light__(input_image, num_filter=128, num_channels_=num_channels)
+    tmp = __light__(tmp, num_filter=128)
+    tmp = __light__(tmp, num_filter=128)
+    tmp = __light__(tmp, num_filter=128, conv_filter_size=1)
+
+    #tmp = img_pool_layer(input=tmp, stride=2, pool_size=2, pool_type=MaxPooling())
+    #tmp = dropout_layer(input=tmp, dropout_rate=0.5)
+    tmp = fc_layer(input=tmp, size = num_classes, act=SoftmaxActivation())
+    # tmp = fc_layer(input=tmp, size=512, layer_attr=ExtraAttr(drop_rate=0.5), act=LinearActivation())
+    # tmp = batch_norm_layer(input=tmp, act=ReluActivation())
+    # return fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
+    return tmp
+
+predict = light_cnn(input_image=img, num_channels=1, num_classes=label_size)
+
+if not is_predict:
+    lbl = data_layer(name="label", size=label_size)
+    inputs(img, lbl)
+    outputs(classification_cost(input=predict, label=lbl))
+else:
+    outputs(predict)