follow helin's comments

demo/mnist/light_mnist.py

+# 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 paddle.trainer_config_helpers import *
 
 is_predict = get_config_arg("is_predict", bool, False)
@@ -13,11 +27,6 @@ if not is_predict:
         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 #############
@@ -26,11 +35,10 @@ 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
+# A shallower cnn model: [CNN, BN, ReLU, Max-Pooling] x4 + FC x1
+# Easier to train for mnist dataset and quite efficient
+# Final performance is close to deeper ones on tasks such as digital and character classification 
 def light_cnn(input_image, num_channels, num_classes):
     def __light__(ipt,
                   num_filter=128,