fix code style and copyright

image_classification/README.md

@@ -67,7 +67,7 @@ Alex Krizhevsky在2012年ILSVRC提出的CNN模型 \[[9](#参考文献)\] 取得
 - Droupout \[[10](#参考文献)\] : 在模型训练阶段随机让一些隐层节点权重不工作，提高网络的泛化能力
 ，一定程度上防止过拟合。
 
-除过上面这些基本的组件外，另一个非常值得一提是2015年提出的Batch Normalization(BN)算法 \[[14](#参考文献)\]，作者指出在训练过程中由于每层参数不断更新，会导致下一次输入分布发生变化，这样导致训练过程需要精心设计超参数。而BN算法是每个batch对网络中的每一层的特征做归一化，使得每层分布相对稳定，起到一定的正则作用，同时也弱化了超参数的设计，经过实验证明，BN加速了训练过程。BN在后来较深的模型中被广泛使用。
+传统CNN包含卷积层、全连接层等组件，采用softmax多类别分类器和多类交叉熵损失函数。另一个非常值得一提是2015年提出的Batch Normalization(BN)算法 \[[14](#参考文献)\]，作者指出在训练过程中由于每层参数不断更新，会导致下一次输入分布发生变化，这样导致训练过程需要精心设计超参数。而BN算法是每个batch对网络中的每一层的特征做归一化，使得每层分布相对稳定，起到一定的正则作用，同时也弱化了超参数的设计，经过实验证明，BN加速了训练过程。BN在后来较深的模型中被广泛使用。
 
 接下来我们主要介绍VGG，GooleNet和ResNet网络结构。
 
@@ -481,17 +481,10 @@ python classify.py --job=extract --model=output/pass-00299 --data=image/dog.png
 
 [13] Lin, M., Chen, Q., and Yan, S. Network in network. In Proc. ICLR, 2014.
 
-[14] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep
-network training by reducing internal covariate shift. In ICML, 2015.
- 
+[14] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015.
+
 [15] K. He, X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. CVPR 2016.
 
 [16] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the incep-tion architecture for computer vision. In: CVPR. (2016).
 
 [17] Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv:1602.07261 (2016).
-
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- 

image_classification/classify.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+# 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.

image_classification/data/cifar10.py

@@ -1,3 +1,17 @@
+# 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.
+
 import os
 import numpy as np
 import cPickle
@@ -7,22 +21,24 @@
 HEIGHT = 32
 WIDTH = 32
 
+
 def create_mean(dataset):
     if not os.path.isfile("mean.meta"):
         mean = np.zeros(CHANNEL * HEIGHT * WIDTH)
         num = 0
         for f in dataset:
             batch = np.load(f)
-            mean +=  batch['data'].sum(0)
+            mean += batch['data'].sum(0)
             num += len(batch['data'])
         mean /= num
         print mean.size
         data = {"mean": mean, "size": mean.size}
-        cPickle.dump(data, open("mean.meta", 'w'), protocol=cPickle.HIGHEST_PROTOCOL)
+        cPickle.dump(
+            data, open("mean.meta", 'w'), protocol=cPickle.HIGHEST_PROTOCOL)
 
 
 def create_data():
-    train_set = [DATA + "/data_batch_%d" % (i + 1) for i in xrange(0,5)]
+    train_set = [DATA + "/data_batch_%d" % (i + 1) for i in xrange(0, 5)]
     test_set = [DATA + "/test_batch"]
 
     # create mean values
@@ -39,5 +55,6 @@ def create_data():
         open("test.txt", "w").write("\n".join(test))
         open("test.list", "w").write("\n".join(["data/test.txt"]))
 
+
 if __name__ == '__main__':
     create_data()

image_classification/data/get_data.sh

@@ -1,4 +1,4 @@
-# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
+# 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.
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 set -e
+
 wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
 tar zxf cifar-10-python.tar.gz
 rm cifar-10-python.tar.gz