From ffc5a132839c0984ab79141e342eb8d8bd464f38 Mon Sep 17 00:00:00 2001
From: fengjiayi <fengjiayi@baidu.com>
Date: Thu, 14 Sep 2017 10:45:08 -0700
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+<html>
+<head>
+  <script type="text/x-mathjax-config">
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+</div>
+
+<!-- This block will be replaced by each markdown file content. Please do not change lines below.-->
+<div id="markdown" style='display:none'>
+
+# 图像分类
+
+本教程源代码目录在[book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/book/blob/develop/README.cn.md#运行这本书)，更多内容请参考本教程的[视频课堂](http://bit.baidu.com/course/detail/id/168.html)。
+
+## 背景介绍
+
+图像相比文字能够提供更加生动、容易理解及更具艺术感的信息，是人们转递与交换信息的重要来源。在本教程中，我们专注于图像识别领域的一个重要问题，即图像分类。
+
+图像分类是根据图像的语义信息将不同类别图像区分开来，是计算机视觉中重要的基本问题，也是图像检测、图像分割、物体跟踪、行为分析等其他高层视觉任务的基础。图像分类在很多领域有广泛应用，包括安防领域的人脸识别和智能视频分析等，交通领域的交通场景识别，互联网领域基于内容的图像检索和相册自动归类，医学领域的图像识别等。
+
+
+一般来说，图像分类通过手工特征或特征学习方法对整个图像进行全部描述，然后使用分类器判别物体类别，因此如何提取图像的特征至关重要。在深度学习算法之前使用较多的是基于词袋(Bag of Words)模型的物体分类方法。词袋方法从自然语言处理中引入，即一句话可以用一个装了词的袋子表示其特征，袋子中的词为句子中的单词、短语或字。对于图像而言，词袋方法需要构建字典。最简单的词袋模型框架可以设计为**底层特征抽取**、**特征编码**、**分类器设计**三个过程。
+
+而基于深度学习的图像分类方法，可以通过有监督或无监督的方式**学习**层次化的特征描述，从而取代了手工设计或选择图像特征的工作。深度学习模型中的卷积神经网络(Convolution Neural Network, CNN)近年来在图像领域取得了惊人的成绩，CNN直接利用图像像素信息作为输入，最大程度上保留了输入图像的所有信息，通过卷积操作进行特征的提取和高层抽象，模型输出直接是图像识别的结果。这种基于"输入-输出"直接端到端的学习方法取得了非常好的效果，得到了广泛的应用。
+
+本教程主要介绍图像分类的深度学习模型，以及如何使用PaddlePaddle训练CNN模型。
+
+## 效果展示
+
+图像分类包括通用图像分类、细粒度图像分类等。图1展示了通用图像分类效果，即模型可以正确识别图像上的主要物体。
+
+<p align="center">
+<img src="image/dog_cat.png "  width="350" ><br/>
+图1. 通用图像分类展示
+</p>
+
+
+图2展示了细粒度图像分类-花卉识别的效果，要求模型可以正确识别花的类别。
+
+
+<p align="center">
+<img src="image/flowers.png" width="400" ><br/>
+图2. 细粒度图像分类展示
+</p>
+
+
+一个好的模型既要对不同类别识别正确，同时也应该能够对不同视角、光照、背景、变形或部分遮挡的图像正确识别(这里我们统一称作图像扰动)。图3展示了一些图像的扰动，较好的模型会像聪明的人类一样能够正确识别。
+
+<p align="center">
+<img src="image/variations.png" width="550" ><br/>
+图3. 扰动图片展示[22]
+</p>
+
+## 模型概览
+
+图像识别领域大量的研究成果都是建立在[PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/)、[ImageNet](http://image-net.org/)等公开的数据集上，很多图像识别算法通常在这些数据集上进行测试和比较。PASCAL VOC是2005年发起的一个视觉挑战赛，ImageNet是2010年发起的大规模视觉识别竞赛(ILSVRC)的数据集，在本章中我们基于这些竞赛的一些论文介绍图像分类模型。
+
+在2012年之前的传统图像分类方法可以用背景描述中提到的三步完成，但通常完整建立图像识别模型一般包括底层特征学习、特征编码、空间约束、分类器设计、模型融合等几个阶段。
+  1). **底层特征提取**: 通常从图像中按照固定步长、尺度提取大量局部特征描述。常用的局部特征包括SIFT(Scale-Invariant Feature Transform, 尺度不变特征转换) \[[1](#参考文献)\]、HOG(Histogram of Oriented Gradient, 方向梯度直方图) \[[2](#参考文献)\]、LBP(Local Bianray Pattern, 局部二值模式) \[[3](#参考文献)\] 等，一般也采用多种特征描述子，防止丢失过多的有用信息。
+  2). **特征编码**: 底层特征中包含了大量冗余与噪声，为了提高特征表达的鲁棒性，需要使用一种特征变换算法对底层特征进行编码，称作特征编码。常用的特征编码包括向量量化编码 \[[4](#参考文献)\]、稀疏编码 \[[5](#参考文献)\]、局部线性约束编码 \[[6](#参考文献)\]、Fisher向量编码 \[[7](#参考文献)\] 等。
+  3). **空间特征约束**: 特征编码之后一般会经过空间特征约束，也称作**特征汇聚**。特征汇聚是指在一个空间范围内，对每一维特征取最大值或者平均值，可以获得一定特征不变形的特征表达。金字塔特征匹配是一种常用的特征聚会方法，这种方法提出将图像均匀分块，在分块内做特征汇聚。
+  4). **通过分类器分类**: 经过前面步骤之后一张图像可以用一个固定维度的向量进行描述，接下来就是经过分类器对图像进行分类。通常使用的分类器包括SVM(Support Vector Machine, 支持向量机)、随机森林等。而使用核方法的SVM是最为广泛的分类器，在传统图像分类任务上性能很好。
+
+这种方法在PASCAL VOC竞赛中的图像分类算法中被广泛使用 \[[18](#参考文献)\]。[NEC实验室](http://www.nec-labs.com/)在ILSVRC2010中采用SIFT和LBP特征，两个非线性编码器以及SVM分类器获得图像分类的冠军 \[[8](#参考文献)\]。
+
+Alex Krizhevsky在2012年ILSVRC提出的CNN模型 \[[9](#参考文献)\] 取得了历史性的突破，效果大幅度超越传统方法，获得了ILSVRC2012冠军，该模型被称作AlexNet。这也是首次将深度学习用于大规模图像分类中。从AlexNet之后，涌现了一系列CNN模型，不断地在ImageNet上刷新成绩，如图4展示。随着模型变得越来越深以及精妙的结构设计，Top-5的错误率也越来越低，降到了3.5%附近。而在同样的ImageNet数据集上，人眼的辨识错误率大概在5.1%，也就是目前的深度学习模型的识别能力已经超过了人眼。
+
+<p align="center">
+<img src="image/ilsvrc.png" width="500" ><br/>
+图4. ILSVRC图像分类Top-5错误率
+</p>
+
+### CNN
+
+传统CNN包含卷积层、全连接层等组件，并采用softmax多类别分类器和多类交叉熵损失函数，一个典型的卷积神经网络如图5所示，我们先介绍用来构造CNN的常见组件。
+
+<p align="center">
+<img src="image/lenet.png"><br/>
+图5. CNN网络示例[20]
+</p>
+
+- 卷积层(convolution layer): 执行卷积操作提取底层到高层的特征，发掘出图片局部关联性质和空间不变性质。
+- 池化层(pooling layer): 执行降采样操作。通过取卷积输出特征图中局部区块的最大值(max-pooling)或者均值(avg-pooling)。降采样也是图像处理中常见的一种操作，可以过滤掉一些不重要的高频信息。
+- 全连接层(fully-connected layer，或者fc layer): 输入层到隐藏层的神经元是全部连接的。
+- 非线性变化: 卷积层、全连接层后面一般都会接非线性变化层，例如Sigmoid、Tanh、ReLu等来增强网络的表达能力，在CNN里最常使用的为ReLu激活函数。
+- Dropout \[[10](#参考文献)\] : 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。
+
+另外，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。如2015年Sergey Ioffe和Christian Szegedy提出了Batch Normalization (BN)算法 \[[14](#参考文献)\] 中，每个batch对网络中的每一层特征都做归一化，使得每层分布相对稳定。BN算法不仅起到一定的正则作用，而且弱化了一些超参数的设计。经过实验证明，BN算法加速了模型收敛过程，在后来较深的模型中被广泛使用。
+
+接下来我们主要介绍VGG，GoogleNet和ResNet网络结构。
+
+### VGG
+
+牛津大学VGG(Visual Geometry Group)组在2014年ILSVRC提出的模型被称作VGG模型 \[[11](#参考文献)\] 。该模型相比以往模型进一步加宽和加深了网络结构，它的核心是五组卷积操作，每两组之间做Max-Pooling空间降维。同一组内采用多次连续的3X3卷积，卷积核的数目由较浅组的64增多到最深组的512，同一组内的卷积核数目是一样的。卷积之后接两层全连接层，之后是分类层。由于每组内卷积层的不同，有11、13、16、19层这几种模型，下图展示一个16层的网络结构。VGG模型结构相对简洁，提出之后也有很多文章基于此模型进行研究，如在ImageNet上首次公开超过人眼识别的模型\[[19](#参考文献)\]就是借鉴VGG模型的结构。
+
+<p align="center">
+<img src="image/vgg16.png" width="750" ><br/>
+图6. 基于ImageNet的VGG16模型
+</p>
+
+### GoogleNet
+
+GoogleNet \[[12](#参考文献)\] 在2014年ILSVRC的获得了冠军，在介绍该模型之前我们先来了解NIN(Network in Network)模型 \[[13](#参考文献)\] 和Inception模块，因为GoogleNet模型由多组Inception模块组成，模型设计借鉴了NIN的一些思想。
+
+NIN模型主要有两个特点：1) 引入了多层感知卷积网络(Multi-Layer Perceptron Convolution, MLPconv)代替一层线性卷积网络。MLPconv是一个微小的多层卷积网络，即在线性卷积后面增加若干层1x1的卷积，这样可以提取出高度非线性特征。2) 传统的CNN最后几层一般都是全连接层，参数较多。而NIN模型设计最后一层卷积层包含类别维度大小的特征图，然后采用全局均值池化(Avg-Pooling)替代全连接层，得到类别维度大小的向量，再进行分类。这种替代全连接层的方式有利于减少参数。
+
+Inception模块如下图7所示，图(a)是最简单的设计，输出是3个卷积层和一个池化层的特征拼接。这种设计的缺点是池化层不会改变特征通道数，拼接后会导致特征的通道数较大，经过几层这样的模块堆积后，通道数会越来越大，导致参数和计算量也随之增大。为了改善这个缺点，图(b)引入3个1x1卷积层进行降维，所谓的降维就是减少通道数，同时如NIN模型中提到的1x1卷积也可以修正线性特征。
+
+<p align="center">
+<img src="image/inception.png" width="800" ><br/>
+图7. Inception模块
+</p>
+
+GoogleNet由多组Inception模块堆积而成。另外，在网络最后也没有采用传统的多层全连接层，而是像NIN网络一样采用了均值池化层；但与NIN不同的是，池化层后面接了一层到类别数映射的全连接层。除了这两个特点之外，由于网络中间层特征也很有判别性，GoogleNet在中间层添加了两个辅助分类器，在后向传播中增强梯度并且增强正则化，而整个网络的损失函数是这个三个分类器的损失加权求和。
+
+GoogleNet整体网络结构如图8所示，总共22层网络：开始由3层普通的卷积组成；接下来由三组子网络组成，第一组子网络包含2个Inception模块，第二组包含5个Inception模块，第三组包含2个Inception模块；然后接均值池化层、全连接层。
+
+<p align="center">
+<img src="image/googlenet.jpeg" ><br/>
+图8. GoogleNet[12]
+</p>
+
+
+上面介绍的是GoogleNet第一版模型(称作GoogleNet-v1)。GoogleNet-v2 \[[14](#参考文献)\] 引入BN层；GoogleNet-v3 \[[16](#参考文献)\] 对一些卷积层做了分解，进一步提高网络非线性能力和加深网络；GoogleNet-v4 \[[17](#参考文献)\] 引入下面要讲的ResNet设计思路。从v1到v4每一版的改进都会带来准确度的提升，介于篇幅，这里不再详细介绍v2到v4的结构。
+
+
+### ResNet
+
+ResNet(Residual Network) \[[15](#参考文献)\] 是2015年ImageNet图像分类、图像物体定位和图像物体检测比赛的冠军。针对训练卷积神经网络时加深网络导致准确度下降的问题，ResNet提出了采用残差学习。在已有设计思路(BN, 小卷积核，全卷积网络)的基础上，引入了残差模块。每个残差模块包含两条路径，其中一条路径是输入特征的直连通路，另一条路径对该特征做两到三次卷积操作得到该特征的残差，最后再将两条路径上的特征相加。
+
+残差模块如图9所示，左边是基本模块连接方式，由两个输出通道数相同的3x3卷积组成。右边是瓶颈模块(Bottleneck)连接方式，之所以称为瓶颈，是因为上面的1x1卷积用来降维(图示例即256->64)，下面的1x1卷积用来升维(图示例即64->256)，这样中间3x3卷积的输入和输出通道数都较小(图示例即64->64)。
+
+<p align="center">
+<img src="image/resnet_block.jpg" width="400"><br/>
+图9. 残差模块
+</p>
+
+图10展示了50、101、152层网络连接示意图，使用的是瓶颈模块。这三个模型的区别在于每组中残差模块的重复次数不同(见图右上角)。ResNet训练收敛较快，成功的训练了上百乃至近千层的卷积神经网络。
+
+<p align="center">
+<img src="image/resnet.png"><br/>
+图10. 基于ImageNet的ResNet模型
+</p>
+
+
+## 数据准备
+
+通用图像分类公开的标准数据集常用的有[CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html)、[ImageNet](http://image-net.org/)、[COCO](http://mscoco.org/)等，常用的细粒度图像分类数据集包括[CUB-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html)、[Stanford Dog](http://vision.stanford.edu/aditya86/ImageNetDogs/)、[Oxford-flowers](http://www.robots.ox.ac.uk/~vgg/data/flowers/)等。其中ImageNet数据集规模相对较大，如[模型概览](#模型概览)一章所讲，大量研究成果基于ImageNet。ImageNet数据从2010年来稍有变化，常用的是ImageNet-2012数据集，该数据集包含1000个类别：训练集包含1,281,167张图片，每个类别数据732至1300张不等，验证集包含50,000张图片，平均每个类别50张图片。
+
+由于ImageNet数据集较大，下载和训练较慢，为了方便大家学习，我们使用[CIFAR10](<https://www.cs.toronto.edu/~kriz/cifar.html>)数据集。CIFAR10数据集包含60,000张32x32的彩色图片，10个类别，每个类包含6,000张。其中50,000张图片作为训练集，10000张作为测试集。图11从每个类别中随机抽取了10张图片，展示了所有的类别。
+
+<p align="center">
+<img src="image/cifar.png" width="350"><br/>
+图11. CIFAR10数据集[21]
+</p>
+
+Paddle API提供了自动加载cifar数据集模块 `paddle.dataset.cifar`。
+
+通过输入`python train.py`，就可以开始训练模型了，以下小节将详细介绍`train.py`的相关内容。
+
+### 模型结构
+
+#### Paddle 初始化
+
+通过 `paddle.init`，初始化Paddle是否使用GPU，trainer的数目等等。
+
+```python
+import sys
+import paddle.v2 as paddle
+from vgg import vgg_bn_drop
+from resnet import resnet_cifar10
+
+# PaddlePaddle init
+paddle.init(use_gpu=False, trainer_count=1)
+```
+
+本教程中我们提供了VGG和ResNet两个模型的配置。
+
+#### VGG
+
+首先介绍VGG模型结构，由于CIFAR10图片大小和数量相比ImageNet数据小很多，因此这里的模型针对CIFAR10数据做了一定的适配。卷积部分引入了BN和Dropout操作。
+
+1. 定义数据输入及其维度
+
+    网络输入定义为 `data_layer` (数据层)，在图像分类中即为图像像素信息。CIFRAR10是RGB 3通道32x32大小的彩色图，因此输入数据大小为3072(3x32x32)，类别大小为10，即10分类。
+
+    ```python
+    datadim = 3 * 32 * 32
+    classdim = 10
+
+    image = paddle.layer.data(
+        name="image", type=paddle.data_type.dense_vector(datadim))
+    ```
+
+2. 定义VGG网络核心模块
+
+    ```python
+    net = vgg_bn_drop(image)
+    ```
+    VGG核心模块的输入是数据层，`vgg_bn_drop` 定义了16层VGG结构，每层卷积后面引入BN层和Dropout层，详细的定义如下：
+
+    ```python
+    def vgg_bn_drop(input):
+        def conv_block(ipt, num_filter, groups, dropouts, num_channels=None):
+            return paddle.networks.img_conv_group(
+                input=ipt,
+                num_channels=num_channels,
+                pool_size=2,
+                pool_stride=2,
+                conv_num_filter=[num_filter] * groups,
+                conv_filter_size=3,
+                conv_act=paddle.activation.Relu(),
+                conv_with_batchnorm=True,
+                conv_batchnorm_drop_rate=dropouts,
+                pool_type=paddle.pooling.Max())
+
+        conv1 = conv_block(input, 64, 2, [0.3, 0], 3)
+        conv2 = conv_block(conv1, 128, 2, [0.4, 0])
+        conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
+        conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
+        conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
+
+        drop = paddle.layer.dropout(input=conv5, dropout_rate=0.5)
+        fc1 = paddle.layer.fc(input=drop, size=512, act=paddle.activation.Linear())
+        bn = paddle.layer.batch_norm(
+            input=fc1,
+            act=paddle.activation.Relu(),
+            layer_attr=paddle.attr.Extra(drop_rate=0.5))
+        fc2 = paddle.layer.fc(input=bn, size=512, act=paddle.activation.Linear())
+        return fc2
+    ```
+
+    2.1. 首先定义了一组卷积网络，即conv_block。卷积核大小为3x3，池化窗口大小为2x2，窗口滑动大小为2，groups决定每组VGG模块是几次连续的卷积操作，dropouts指定Dropout操作的概率。所使用的`img_conv_group`是在`paddle.networks`中预定义的模块，由若干组 Conv->BN->ReLu->Dropout 和 一组 Pooling 组成。
+
+    2.2. 五组卷积操作，即 5个conv_block。 第一、二组采用两次连续的卷积操作。第三、四、五组采用三次连续的卷积操作。每组最后一个卷积后面Dropout概率为0，即不使用Dropout操作。
+
+    2.3. 最后接两层512维的全连接。
+
+3. 定义分类器
+
+    通过上面VGG网络提取高层特征，然后经过全连接层映射到类别维度大小的向量，再通过Softmax归一化得到每个类别的概率，也可称作分类器。
+
+    ```python
+    out = paddle.layer.fc(input=net,
+                          size=classdim,
+                          act=paddle.activation.Softmax())
+    ```
+
+4. 定义损失函数和网络输出
+
+    在有监督训练中需要输入图像对应的类别信息，同样通过`paddle.layer.data`来定义。训练中采用多类交叉熵作为损失函数，并作为网络的输出，预测阶段定义网络的输出为分类器得到的概率信息。
+
+    ```python
+    lbl = paddle.layer.data(
+        name="label", type=paddle.data_type.integer_value(classdim))
+    cost = paddle.layer.classification_cost(input=out, label=lbl)
+    ```
+
+### ResNet
+
+ResNet模型的第1、3、4步和VGG模型相同，这里不再介绍。主要介绍第2步即CIFAR10数据集上ResNet核心模块。
+
+```python
+net = resnet_cifar10(image, depth=56)
+```
+
+先介绍`resnet_cifar10`中的一些基本函数，再介绍网络连接过程。
+
+  - `conv_bn_layer` : 带BN的卷积层。
+  - `shortcut` : 残差模块的"直连"路径，"直连"实际分两种形式：残差模块输入和输出特征通道数不等时，采用1x1卷积的升维操作；残差模块输入和输出通道相等时，采用直连操作。
+  - `basicblock` : 一个基础残差模块，即图9左边所示，由两组3x3卷积组成的路径和一条"直连"路径组成。
+  - `bottleneck` : 一个瓶颈残差模块，即图9右边所示，由上下1x1卷积和中间3x3卷积组成的路径和一条"直连"路径组成。
+  - `layer_warp` : 一组残差模块，由若干个残差模块堆积而成。每组中第一个残差模块滑动窗口大小与其他可以不同，以用来减少特征图在垂直和水平方向的大小。
+
+```python
+def conv_bn_layer(input,
+                  ch_out,
+                  filter_size,
+                  stride,
+                  padding,
+                  active_type=paddle.activation.Relu(),
+                  ch_in=None):
+    tmp = paddle.layer.img_conv(
+        input=input,
+        filter_size=filter_size,
+        num_channels=ch_in,
+        num_filters=ch_out,
+        stride=stride,
+        padding=padding,
+        act=paddle.activation.Linear(),
+        bias_attr=False)
+    return paddle.layer.batch_norm(input=tmp, act=active_type)
+
+def shortcut(ipt, n_in, n_out, stride):
+    if n_in != n_out:
+        return conv_bn_layer(ipt, n_out, 1, stride, 0,
+                             paddle.activation.Linear())
+    else:
+        return ipt
+
+def basicblock(ipt, ch_out, stride):
+    ch_in = ch_out * 2
+    tmp = conv_bn_layer(ipt, ch_out, 3, stride, 1)
+    tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, paddle.activation.Linear())
+    short = shortcut(ipt, ch_in, ch_out, stride)
+    return paddle.layer.addto(input=[tmp, short], act=paddle.activation.Relu())
+
+def layer_warp(block_func, ipt, features, count, stride):
+    tmp = block_func(ipt, features, stride)
+    for i in range(1, count):
+        tmp = block_func(tmp, features, 1)
+    return tmp
+```
+
+`resnet_cifar10` 的连接结构主要有以下几个过程。
+
+1. 底层输入连接一层 `conv_bn_layer`，即带BN的卷积层。
+2. 然后连接3组残差模块即下面配置3组 `layer_warp` ，每组采用图 10 左边残差模块组成。
+3. 最后对网络做均值池化并返回该层。
+
+注意：除过第一层卷积层和最后一层全连接层之外，要求三组 `layer_warp` 总的含参层数能够被6整除，即 `resnet_cifar10` 的 depth 要满足 $(depth - 2) % 6 == 0$ 。
+
+```python
+def resnet_cifar10(ipt, depth=32):
+    # depth should be one of 20, 32, 44, 56, 110, 1202
+    assert (depth - 2) % 6 == 0
+    n = (depth - 2) / 6
+    nStages = {16, 64, 128}
+    conv1 = conv_bn_layer(
+        ipt, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1)
+    res1 = layer_warp(basicblock, conv1, 16, n, 1)
+    res2 = layer_warp(basicblock, res1, 32, n, 2)
+    res3 = layer_warp(basicblock, res2, 64, n, 2)
+    pool = paddle.layer.img_pool(
+        input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg())
+    return pool
+```
+
+## 训练模型
+
+### 定义参数
+
+首先依据模型配置的`cost`定义模型参数。
+
+```python
+# Create parameters
+parameters = paddle.parameters.create(cost)
+```
+
+可以打印参数名字，如果在网络配置中没有指定名字，则默认生成。
+
+```python
+print parameters.keys()
+```
+
+### 构造训练(Trainer)
+
+根据网络拓扑结构和模型参数来构造出trainer用来训练，在构造时还需指定优化方法，这里使用最基本的Momentum方法，同时设定了学习率、正则等。
+
+```python
+# Create optimizer
+momentum_optimizer = paddle.optimizer.Momentum(
+    momentum=0.9,
+    regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
+    learning_rate=0.1 / 128.0,
+    learning_rate_decay_a=0.1,
+    learning_rate_decay_b=50000 * 100,
+    learning_rate_schedule='discexp')
+
+# Create trainer
+trainer = paddle.trainer.SGD(cost=cost,
+                             parameters=parameters,
+                             update_equation=momentum_optimizer)
+```
+
+通过 `learning_rate_decay_a` (简写$a$） 、`learning_rate_decay_b` (简写$b$) 和 `learning_rate_schedule` 指定学习率调整策略，这里采用离散指数的方式调节学习率，计算公式如下， $n$ 代表已经处理过的累计总样本数，$lr_{0}$ 即为 `settings` 里设置的 `learning_rate`。
+
+$$  lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
+
+
+### 训练
+
+cifar.train10()每次产生一条样本，在完成shuffle和batch之后，作为训练的输入。
+
+```python
+reader=paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.cifar.train10(), buf_size=50000),
+        batch_size=128)
+```
+
+通过`feeding`来指定每一个数据和`paddle.layer.data`的对应关系。例如: `cifar.train10()`产生数据的第0列对应image层的特征。
+
+```python
+feeding={'image': 0,
+         'label': 1}
+```
+
+可以使用`event_handler`回调函数来观察训练过程，或进行测试等, 该回调函数是`trainer.train`函数里设定。
+
+`event_handler_plot`可以用来利用回调数据来打点画图:
+
+![png](./image/train_and_test.png)
+
+```python
+from paddle.v2.plot import Ploter
+
+train_title = "Train cost"
+test_title = "Test cost"
+cost_ploter = Ploter(train_title, test_title)
+
+step = 0
+def event_handler_plot(event):
+    global step
+    if isinstance(event, paddle.event.EndIteration):
+        if step % 1 == 0:
+            cost_ploter.append(train_title, step, event.cost)
+            cost_ploter.plot()
+        step += 1
+    if isinstance(event, paddle.event.EndPass):
+
+        result = trainer.test(
+            reader=paddle.batch(
+                paddle.dataset.cifar.test10(), batch_size=128),
+            feeding=feeding)
+        cost_ploter.append(test_title, step, result.cost)
+```
+
+`event_handler` 用来在训练过程中输出文本日志
+
+```python
+# End batch and end pass event handler
+def event_handler(event):
+    if isinstance(event, paddle.event.EndIteration):
+        if event.batch_id % 100 == 0:
+            print "\nPass %d, Batch %d, Cost %f, %s" % (
+                event.pass_id, event.batch_id, event.cost, event.metrics)
+        else:
+            sys.stdout.write('.')
+            sys.stdout.flush()
+    if isinstance(event, paddle.event.EndPass):
+        # save parameters
+        with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
+            parameters.to_tar(f)
+
+        result = trainer.test(
+            reader=paddle.batch(
+                paddle.dataset.cifar.test10(), batch_size=128),
+            feeding=feeding)
+        print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+```
+
+通过`trainer.train`函数训练:
+
+```python
+trainer.train(
+    reader=reader,
+    num_passes=200,
+    event_handler=event_handler_plot,
+    feeding=feeding)
+```
+
+一轮训练log示例如下所示，经过1个pass， 训练集上平均error为0.6875 ，测试集上平均error为0.8852 。
+
+```text
+Pass 0, Batch 0, Cost 2.473182, {'classification_error_evaluator': 0.9140625}
+...................................................................................................
+Pass 0, Batch 100, Cost 1.913076, {'classification_error_evaluator': 0.78125}
+...................................................................................................
+Pass 0, Batch 200, Cost 1.783041, {'classification_error_evaluator': 0.7421875}
+...................................................................................................
+Pass 0, Batch 300, Cost 1.668833, {'classification_error_evaluator': 0.6875}
+..........................................................................................
+Test with Pass 0, {'classification_error_evaluator': 0.885200023651123}
+```
+
+图12是训练的分类错误率曲线图，运行到第200个pass后基本收敛，最终得到测试集上分类错误率为8.54%。
+
+<p align="center">
+<img src="image/plot.png" width="400" ><br/>
+图12. CIFAR10数据集上VGG模型的分类错误率
+</p>
+
+## 应用模型
+
+可以使用训练好的模型对图片进行分类，下面程序展示了如何使用`paddle.infer`接口进行推断，可以打开注释，更改加载的模型。
+
+```python
+from PIL import Image
+import numpy as np
+import os
+def load_image(file):
+    im = Image.open(file)
+    im = im.resize((32, 32), Image.ANTIALIAS)
+    im = np.array(im).astype(np.float32)
+    # PIL打开图片存储顺序为H(高度)，W(宽度)，C(通道)。
+    # PaddlePaddle要求数据顺序为CHW，所以需要转换顺序。
+    im = im.transpose((2, 0, 1)) # CHW
+    # CIFAR训练图片通道顺序为B(蓝),G(绿),R(红),
+    # 而PIL打开图片默认通道顺序为RGB,因为需要交换通道。
+    im = im[(2, 1, 0),:,:] # BGR
+    im = im.flatten()
+    im = im / 255.0
+    return im
+
+test_data = []
+cur_dir = os.getcwd()
+test_data.append((load_image(cur_dir + '/image/dog.png'),))
+
+# with open('params_pass_50.tar', 'r') as f:
+#    parameters = paddle.parameters.Parameters.from_tar(f)
+
+probs = paddle.infer(
+    output_layer=out, parameters=parameters, input=test_data)
+lab = np.argsort(-probs) # probs and lab are the results of one batch data
+print "Label of image/dog.png is: %d" % lab[0][0]
+```
+
+
+## 总结
+
+传统图像分类方法由多个阶段构成，框架较为复杂，而端到端的CNN模型结构可一步到位，而且大幅度提升了分类准确率。本文我们首先介绍VGG、GoogleNet、ResNet三个经典的模型；然后基于CIFAR10数据集，介绍如何使用PaddlePaddle配置和训练CNN模型，尤其是VGG和ResNet模型；最后介绍如何使用PaddlePaddle的API接口对图片进行预测和特征提取。对于其他数据集比如ImageNet，配置和训练流程是同样的，大家可以自行进行实验。
+
+
+## 参考文献
+
+[1] D. G. Lowe, [Distinctive image features from scale-invariant keypoints](http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf). IJCV, 60(2):91-110, 2004.
+
+[2] N. Dalal, B. Triggs, [Histograms of Oriented Gradients for Human Detection](http://vision.stanford.edu/teaching/cs231b_spring1213/papers/CVPR05_DalalTriggs.pdf), Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
+
+[3] Ahonen, T., Hadid, A., and Pietikinen, M. (2006). [Face description with local binary patterns: Application to face recognition](http://ieeexplore.ieee.org/document/1717463/). PAMI, 28.
+
+[4] J. Sivic, A. Zisserman, [Video Google: A Text Retrieval Approach to Object Matching in Videos](http://www.robots.ox.ac.uk/~vgg/publications/papers/sivic03.pdf), Proc. Ninth Int'l Conf. Computer Vision, pp. 1470-1478, 2003.
+
+[5] B. Olshausen, D. Field, [Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1?](http://redwood.psych.cornell.edu/papers/olshausen_field_1997.pdf), Vision Research, vol. 37, pp. 3311-3325, 1997.
+
+[6] Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., and Gong, Y. (2010). [Locality-constrained Linear Coding for image classification](http://ieeexplore.ieee.org/abstract/document/5540018/). In CVPR.
+
+[7] Perronnin, F., Sánchez, J., & Mensink, T. (2010). [Improving the fisher kernel for large-scale image classification](http://dl.acm.org/citation.cfm?id=1888101). In ECCV (4).
+
+[8] Lin, Y., Lv, F., Cao, L., Zhu, S., Yang, M., Cour, T., Yu, K., and Huang, T. (2011). [Large-scale image clas- sification: Fast feature extraction and SVM training](http://ieeexplore.ieee.org/document/5995477/). In CVPR.
+
+[9] Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). [ImageNet classification with deep convolutional neu- ral networks](http://www.cs.toronto.edu/~kriz/imagenet_classification_with_deep_convolutional.pdf). In NIPS.
+
+[10] G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salakhutdinov. [Improving neural networks by preventing co-adaptation of feature detectors](https://arxiv.org/abs/1207.0580). arXiv preprint arXiv:1207.0580, 2012.
+
+[11] K. Chatfield, K. Simonyan, A. Vedaldi, A. Zisserman. [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://arxiv.org/abs/1405.3531). BMVC, 2014。
+
+[12] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., [Going deeper with convolutions](https://arxiv.org/abs/1409.4842). In: CVPR. (2015)
+
+[13] Lin, M., Chen, Q., and Yan, S. [Network in network](https://arxiv.org/abs/1312.4400). In Proc. ICLR, 2014.
+
+[14] S. Ioffe and C. Szegedy. [Batch normalization: Accelerating deep network training by reducing internal covariate shift](https://arxiv.org/abs/1502.03167). In ICML, 2015.
+
+[15] K. He, X. Zhang, S. Ren, J. Sun. [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385). CVPR 2016.
+
+[16] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z. [Rethinking the incep-tion architecture for computer vision](https://arxiv.org/abs/1512.00567). In: CVPR. (2016).
+
+[17] Szegedy, C., Ioffe, S., Vanhoucke, V. [Inception-v4, inception-resnet and the impact of residual connections on learning](https://arxiv.org/abs/1602.07261). arXiv:1602.07261 (2016).
+
+[18] Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J. and Zisserman, A. [The Pascal Visual Object Classes Challenge: A Retrospective]((http://link.springer.com/article/10.1007/s11263-014-0733-5)). International Journal of Computer Vision, 111(1), 98-136, 2015.
+
+[19] He, K., Zhang, X., Ren, S., and Sun, J. [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://arxiv.org/abs/1502.01852). ArXiv e-prints, February 2015.
+
+[20] http://deeplearning.net/tutorial/lenet.html
+
+[21] https://www.cs.toronto.edu/~kriz/cifar.html
+
+[22] http://cs231n.github.io/classification/
+
+<br/>
+<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="知识共享许可协议" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br /><span xmlns:dct="http://purl.org/dc/terms/" href="http://purl.org/dc/dcmitype/Text" property="dct:title" rel="dct:type">本教程</span> 由 <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a> 创作，采用 <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">知识共享 署名-相同方式共享 4.0 国际 许可协议</a>进行许可。
+
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+
+Image Classification
+=======================
+
+The source code for this chapter is at [book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification). First-time users, please refer to PaddlePaddle [Installation Tutorial](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book) for installation instructions.
+
+## Background
+
+Compared to words, images provide much more vivid and easier to understand information with an artistic sense. They are an important source for people to express and exchange ideas. In this chapter, we focus on one of the essential problems in image recognition -- image classification.
+
+Image classification is the task of distinguishing images in different categories based on their semantic meaning. It is a core problem in computer vision and is also the foundation of other higher level computer vision tasks such as object detection, image segmentation, object tracking, action recognition, etc. Image classification has applications in many areas such as face recognition, intelligent video analysis in security systems, traffic scene recognition in transportation systems, content-based image retrieval and automatic photo indexing in web services, image classification in medicine, etc.
+
+To classify an image we first encode the entire image using handcrafted or learned features and then determine the category using a classifier. Thus, feature extraction plays an important role in image classification. Prior to deep learning the BoW(Bag of Words) model was the most widely used method for classifying an image as well as an object. The BoW technique was introduced in Natural Language Processing where a training sentence is represented as a bag of words. In the context of image classification, the BoW model requires constructing a dictionary. The simplest BoW framework can be designed with three steps: **feature extraction**, **feature encoding** and **classifier design**.
+
+Using Deep learning, image classification can be framed as a supervised or unsupervised learning problem that uses hierarchical features automatically without any need for manually crafted features from the image. In recent years, Convolutional Neural Networks (CNNs) have made significant progress in image classification. CNNs use raw image pixels as input, extract low-level and high-level abstract features through convolution operations, and directly output the classification results from the model. This style of end-to-end learning has lead to not only increased performance but also wider adoption various applications.
+
+In this chapter, we introduce deep-learning-based image classification methods and explain how to train a CNN model using PaddlePaddle.
+
+## Demonstration
+
+An image can be classified by a general as well as fine-grained image classifier.
+
+
+Figure 1 shows the results of a general image classifier -- the trained model can correctly recognize the main objects in the images.
+
+<p align="center">
+<img src="image/dog_cat.png "  width="350" ><br/>
+Figure 1. General image classification
+</p>
+
+
+Figure 2 shows the results of a fine-grained image classifier. This task of flower recognition requires correctly recognizing of the flower's categories.
+
+<p align="center">
+<img src="image/flowers.png" width="400" ><br/>
+Figure 2. Fine-grained image classification
+</p>
+
+
+A good model should recognize objects of different categories correctly. The results of such a model should not vary due to viewpoint variation, illumination conditions, object distortion or occlusion.
+Figure 3 shows some images with various disturbances. A good model should classify these images correctly like humans.
+
+<p align="center">
+<img src="image/variations_en.png" width="550" ><br/>
+Figure 3. Disturbed images [22]
+</p>
+
+## Model Overview
+
+A large amount of research in image classification is built upon public datasets such as [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/), [ImageNet](http://image-net.org/) etc. Many image classification algorithms are usually evaluated and compared on these datasets. PASCAL VOC is a computer vision competition started in 2005, and ImageNet is a dataset for Large Scale Visual Recognition Challenge (ILSVRC) started in 2010. In this chapter, we introduce some image classification models from the submissions to these competitions.
+
+Before 2012, traditional image classification was accomplished with the three steps described in the background section. A complete model construction usually involves the following stages: low-level feature extraction, feature encoding, spatial constraint or feature clustering, classifier design, model ensemble.
+
+  1). **Low-level feature extraction**: This step extracts large amounts of local features according to fixed strides and scales. Popular local features include Scale-Invariant Feature Transform (SIFT)[1], Histogram of Oriented Gradient(HOG)[2], Local Binary Pattern(LBP)[3], etc. A common practice is to employ multiple feature descriptors in order to avoid missing a lot of information.
+
+  2). **Feature encoding**: Low-level features contain a large amount of redundancy and noise. In order to improve the robustness of features, it is necessary to employ a feature transformation to encode low-level features. This is called feature encoding. Common feature encoding methods include vector quantization [4], sparse coding [5], locality-constrained linear coding [6], Fisher vector encoding [7], etc.
+
+  3). **Spatial constraint**: Spatial constraint or feature clustering is usually adopted after feature encoding for extracting the maximum or average of each dimension in the spatial domain. Pyramid feature matching--a popular feature clustering method--divides an image uniformly into patches and performs feature clustering in each patch.
+
+  4). **Classification**: In the above steps an image can be described by a vector of fixed dimension. Then a classifier can be used to classify the image into categories. Common classifiers include Support Vector Machine(SVM), random forest etc. Kernel SVM is the most popular classifier and has achieved very good performance in traditional image classification tasks.
+
+This method has been used widely as image classification algorithm in PASCAL VOC [18]. NEC Labs(http://www.nec-labs.com/) won the championship by employing SIFT and LBP features, two non-linear encoders and SVM in ILSVRC 2010 [8].
+
+The CNN model--AlexNet proposed by Alex Krizhevsky et al.[9], made a breakthrough in ILSVRC 2012. It dramatically outperformed traditional methods and won the ILSVRC championship in 2012. This was also the first time that a deep learning method was used for large-scale image classification. Since AlexNet, a series of CNN models have been proposed that have advanced the state of the art steadily on Imagenet as shown in Figure 4. With deeper and more sophisticated architectures, Top-5 error rate is getting lower and lower (to around 3.5%). The error rate of human raters on the same Imagenet dataset is 5.1%, which means that the image classification capability of a deep learning model has surpassed human raters.
+
+<p align="center">
+<img src="image/ilsvrc.png" width="500" ><br/>
+Figure 4. Top-5 error rates on ILSVRC image classification
+</p>
+
+### CNN
+
+Traditional CNNs consist of convolutional and fully-connected layers and use the softmax multi-category classifier with the cross-entropy loss function. Figure 5 shows a typical CNN. We first introduce the common components of a CNN.
+
+<p align="center">
+<img src="image/lenet_en.png"><br/>
+Figure 5. A CNN example [20]
+</p>
+
+- convolutional layer: this layer uses the convolution operation to extract (low-level and high-level) features and to discover local correlation and spatial invariance.
+
+- pooling layer: this layer down samples feature maps by extracting local max (max-pooling) or average (avg-pooling) value of each patch in the feature map. Down-sampling is a common operation in image processing and is used to filter out high-frequency information.
+
+- fully-connected layer: this layer fully connects neurons between two adjacent layers.
+
+- non-linear activation: Convolutional and fully-connected layers are usually followed by some non-linear activation layers. Non-linearities enhance the expression capability of the network. Some examples of non-linear activation functions are Sigmoid, Tanh and ReLU. ReLU is the most commonly used activation function in CNN.
+
+- Dropout [10]: At each training stage, individual nodes are dropped out of the network with a certain probability. This improves the network's ability to generalize and avoids overfitting.
+
+Parameter updates at each layer during training causes input layer distributions to change and in turn requires hyper-parameters to be careful tuned. In 2015, Sergey Ioffe and Christian Szegedy proposed a Batch Normalization (BN) algorithm [14], which normalizes the features of each batch in a layer, and enables relatively stable distribution in each layer. Not only does BN algorithm act as a regularizer, but also reduces the need for careful hyper-parameter design. Experiments demonstrate that BN algorithm accelerates the training convergence and has been widely used in later deeper models.
+
+In the following sections, we will introduce the following network architectures - VGG, GoogleNet and ResNets.
+
+### VGG
+
+The Oxford Visual Geometry Group (VGG) proposed the VGG network in ILSVRC 2014 [11]. This model is deeper and wider than previous neural architectures. It consists of five main groups of convolution operations. Adjacent convolution groups are connected via max-pooling layers. Each group contains a series of 3x3 convolutional layers (i.e. kernels). The number of convolution kernels stays the same within the group and increases from 64 in the first group to 512 in the last one. The total number of learnable layers could be 11, 13, 16, or 19 depending on the number of convolutional layers in each group. Figure 6 illustrates a 16-layer VGG. The neural architecture of VGG is relatively simple and has been adopted by many papers such as the first one that surpassed human-level performance on ImageNet [19].
+
+<p align="center">
+<img src="image/vgg16.png" width="750" ><br/>
+Figure 6. VGG16 model for ImageNet
+</p>
+
+### GoogleNet
+
+GoogleNet [12] won the ILSVRC championship in 2014. GoogleNet borrowed some ideas from the Network in Network(NIN) model [13] and is built on the Inception blocks. Let us first familiarize ourselves with these first.
+
+The two main characteristics of the NIN model are:
+
+1) A single-layer convolutional network is replaced with a Multi-Layer Perceptron Convolution (MLPconv). MLPconv is a tiny multi-layer convolutional network. It enhances non-linearity by adding several 1x1 convolutional layers after linear ones.
+
+2) In traditional CNNs, the last fewer layers are usually fully-connected with a large number of parameters. In contrast, NIN replaces all fully-connected layers with convolutional layers with feature maps of the same size as the category dimension and a global average pooling. This replacement of fully-connected layers significantly reduces the number of parameters.
+
+Figure 7 depicts two Inception blocks. Figure 7(a) is the simplest design. The output is a concatenation of features from three convolutional layers and one pooling layer. The disadvantage of this design is that the pooling layer does not change the number of filters and leads to an increase in the number of outputs. After several of such blocks, the number of outputs and parameters become larger and larger and lead to higher computation complexity. To overcome this drawback, the Inception block in Figure 7(b) employs three 1x1 convolutional layers. These reduce dimensions or the number of channels but improve the non-linearity of the network.
+
+<p align="center">
+<img src="image/inception_en.png" width="800" ><br/>
+Figure 7. Inception block
+</p>
+
+GoogleNet consists of multiple stacked Inception blocks followed by an avg-pooling layer as in NIN instead of traditional fully connected layers. The difference between GoogleNet and NIN is that GoogleNet adds a fully connected layer after avg-pooling layer to output a vector of category size. Besides these two characteristics, the features from middle layers of a GoogleNet are also very discriminative. Therefore, GoogeleNet inserts two auxiliary classifiers in the model for enhancing gradient and regularization when doing backpropagation. The loss function of the whole network is the weighted sum of these three classifiers.
+
+Figure 8 illustrates the neural architecture of a GoogleNet which consists of 22 layers: it starts with three regular convolutional layers followed by three groups of sub-networks -- the first group contains two Inception blocks, the second one five, and the third one two. It ends up with an average pooling and a fully-connected layer.
+
+<p align="center">
+<img src="image/googlenet.jpeg" ><br/>
+Figure 8. GoogleNet[12]
+</p>
+
+The above model is the first version of GoogleNet or GoogelNet-v1. GoogleNet-v2 [14] introduced BN layer; GoogleNet-v3 [16] further split some convolutional layers, which increases non-linearity and network depth; GoogelNet-v4 [17] leads to the design idea of ResNet which will be introduced in the next section. The evolution from v1 to v4 improved the accuracy rate consistently. We will not go into details of the neural architectures of v2 to v4.
+
+### ResNet
+
+Residual Network(ResNet)[15] won the 2015 championship on three ImageNet competitions -- image classification, object localization, and object detection. The main challenge in training deeper networks is that accuracy degrades with network depth. The authors of ResNet proposed a residual learning approach to ease the difficulty of training deeper networks. Based on the design ideas of BN, small convolutional kernels, full convolutional network, ResNets reformulate the layers as residual blocks, with each block containing two branches, one directly connecting input to the output, the other performing two to three convolutions and calculating the residual function with reference to the layer inputs. The outputs of these two branches are then added up.
+
+Figure 9 illustrates the ResNet architecture. To the left is the basic building block, it consists of two 3x3 convolutional layers of the same channels. To the right is a Bottleneck block. The bottleneck is a 1x1 convolutional layer used to reduce dimension from 256 to 64. The other 1x1 convolutional layer is used to increase dimension from 64 to 256. Thus, the number of input and output channels of the middle 3x3 convolutional layer is 64, which is relatively small.
+
+<p align="center">
+<img src="image/resnet_block.jpg" width="400"><br/>
+Figure 9. Residual block
+</p>
+
+Figure 10 illustrates ResNets with 50, 101, 152 layers, respectively. All three networks use bottleneck blocks of different numbers of repetitions. ResNet converges very fast and can be trained with hundreds or thousands of layers.
+
+<p align="center">
+<img src="image/resnet.png"><br/>
+Figure 10. ResNet model for ImageNet
+</p>
+
+
+## Dataset
+
+Commonly used public datasets for image classification are [CIFAR](https://www.cs.toronto.edu/~kriz/cifar.html), [ImageNet](http://image-net.org/), [COCO](http://mscoco.org/), etc. Those used for fine-grained image classification are [CUB-200-2011](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html), [Stanford Dog](http://vision.stanford.edu/aditya86/ImageNetDogs/), [Oxford-flowers](http://www.robots.ox.ac.uk/~vgg/data/flowers/), etc. Among these, the ImageNet dataset is the largest. Most research results are reported on ImageNet as mentioned in the Model Overview section. Since 2010, the ImageNet dataset has gone through some changes. The commonly used ImageNet-2012 dataset contains 1000 categories. There are 1,281,167 training images, ranging from 732 to 1200 images per category, and 50,000 validation images with 50 images per category in average.
+
+Since ImageNet is too large to be downloaded and trained efficiently, we use [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) in this tutorial. The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. Figure 11 shows all the classes in CIFAR-10 as well as 10 images randomly sampled from each category.
+
+<p align="center">
+<img src="image/cifar.png" width="350"><br/>
+Figure 11. CIFAR10 dataset[21]
+</p>
+
+ `paddle.datasets` package encapsulates multiple public datasets, including `cifar`, `imdb`, `mnist`, `moivelens` and `wmt14`, etc. There's no need to manually download and preprocess CIFAR-10.
+
+After issuing a command `python train.py`, training will start immediately. The following sections describe the details:
+
+## Model Structure
+
+### Initialize PaddlePaddle
+
+We must import and initialize PaddlePaddle (enable/disable GPU, set the number of trainers, etc).
+
+```python
+import sys
+import paddle.v2 as paddle
+from vgg import vgg_bn_drop
+from resnet import resnet_cifar10
+
+# PaddlePaddle init
+paddle.init(use_gpu=False, trainer_count=1)
+```
+
+As mentioned in section [Model Overview](#model-overview), here we provide the implementations of the VGG and ResNet models.
+
+### VGG
+
+First, we use a VGG network. Since the image size and amount of CIFAR10 are relatively small comparing to ImageNet, we use a small version of VGG network for CIFAR10. Convolution groups incorporate BN and dropout operations.
+
+1. Define input data and its dimension
+
+    The input to the network is defined as `paddle.layer.data`, or image pixels in the context of image classification. The images in CIFAR10 are 32x32 color images of three channels. Therefore, the size of the input data is 3072 (3x32x32), and the number of categories is 10.
+
+    ```python
+    datadim = 3 * 32 * 32
+    classdim = 10
+    image = paddle.layer.data(
+        name="image", type=paddle.data_type.dense_vector(datadim))
+    ```
+
+2. Define VGG main module
+
+    ```python
+    net = vgg_bn_drop(image)
+    ```
+    The input to VGG main module is from the data layer. `vgg_bn_drop` defines a 16-layer VGG network, with each convolutional layer followed by BN and dropout layers. Here is the definition in detail:
+
+    ```python
+    def vgg_bn_drop(input):
+        def conv_block(ipt, num_filter, groups, dropouts, num_channels=None):
+            return paddle.networks.img_conv_group(
+                input=ipt,
+                num_channels=num_channels,
+                pool_size=2,
+                pool_stride=2,
+                conv_num_filter=[num_filter] * groups,
+                conv_filter_size=3,
+                conv_act=paddle.activation.Relu(),
+                conv_with_batchnorm=True,
+                conv_batchnorm_drop_rate=dropouts,
+                pool_type=paddle.pooling.Max())
+
+        conv1 = conv_block(input, 64, 2, [0.3, 0], 3)
+        conv2 = conv_block(conv1, 128, 2, [0.4, 0])
+        conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
+        conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
+        conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
+
+        drop = paddle.layer.dropout(input=conv5, dropout_rate=0.5)
+        fc1 = paddle.layer.fc(input=drop, size=512, act=paddle.activation.Linear())
+        bn = paddle.layer.batch_norm(
+            input=fc1,
+            act=paddle.activation.Relu(),
+            layer_attr=paddle.attr.Extra(drop_rate=0.5))
+        fc2 = paddle.layer.fc(input=bn, size=512, act=paddle.activation.Linear())
+        return fc2
+    ```
+
+    2.1. First, define a convolution block or conv_block. The default convolution kernel is 3x3, and the default pooling size is 2x2 with stride 2. Dropout specifies the probability in dropout operation. Function `img_conv_group` is defined in `paddle.networks` consisting of a series of `Conv->BN->ReLu->Dropout` and a `Pooling`.
+
+    2.2. Five groups of convolutions. The first two groups perform two convolutions, while the last three groups perform three convolutions. The dropout rate of the last convolution in each group is set to 0, which means there is no dropout for this layer.
+
+    2.3. The last two layers are fully-connected layers of dimension 512.
+
+3. Define Classifier
+
+    The above VGG network extracts high-level features and maps them to a vector of the same size as the categories. Softmax function or classifier is then used for calculating the probability of the image belonging to each category.
+
+    ```python
+    out = paddle.layer.fc(input=net,
+                          size=classdim,
+                          act=paddle.activation.Softmax())
+    ```
+
+4. Define Loss Function and Outputs
+
+    In the context of supervised learning, labels of training images are defined in `paddle.layer.data` as well. During training, the cross-entropy loss function is used and the loss is the output of the network. During testing, the outputs are the probabilities calculated in the classifier.
+
+    ```python
+    lbl = paddle.layer.data(
+        name="label", type=paddle.data_type.integer_value(classdim))
+    cost = paddle.layer.classification_cost(input=out, label=lbl)
+    ```
+
+### ResNet
+
+The first, third and fourth steps of a ResNet are the same as a VGG. The second one is the main module.
+
+```python
+net = resnet_cifar10(image, depth=56)
+```
+
+Here are some basic functions used in `resnet_cifar10`:
+
+  - `conv_bn_layer` : convolutional layer followed by BN.
+  - `shortcut` : the shortcut branch in a residual block. There are two kinds of shortcuts: 1x1 convolution used when the number of channels between input and output is different; direct connection used otherwise.
+
+  - `basicblock` : a basic residual module as shown in the left of Figure 9, it consists of two sequential 3x3 convolutions and one "shortcut" branch.
+  - `bottleneck` : a bottleneck module as shown in the right of Figure 9, it consists of two 1x1 convolutions with one 3x3 convolution in between branch and a "shortcut" branch.
+  - `layer_warp` : a group of residual modules consisting of several stacking blocks. In each group, the sliding window size of the first residual block could be different from the rest of blocks, in order to reduce the size of feature maps along horizontal and vertical directions.
+
+```python
+def conv_bn_layer(input,
+                  ch_out,
+                  filter_size,
+                  stride,
+                  padding,
+                  active_type=paddle.activation.Relu(),
+                  ch_in=None):
+    tmp = paddle.layer.img_conv(
+        input=input,
+        filter_size=filter_size,
+        num_channels=ch_in,
+        num_filters=ch_out,
+        stride=stride,
+        padding=padding,
+        act=paddle.activation.Linear(),
+        bias_attr=False)
+    return paddle.layer.batch_norm(input=tmp, act=active_type)
+
+def shortcut(ipt, n_in, n_out, stride):
+    if n_in != n_out:
+        return conv_bn_layer(ipt, n_out, 1, stride, 0,
+                             paddle.activation.Linear())
+    else:
+        return ipt
+
+def basicblock(ipt, ch_out, stride):
+    ch_in = ch_out * 2
+    tmp = conv_bn_layer(ipt, ch_out, 3, stride, 1)
+    tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, paddle.activation.Linear())
+    short = shortcut(ipt, ch_in, ch_out, stride)
+    return paddle.layer.addto(input=[tmp, short], act=paddle.activation.Relu())
+
+def layer_warp(block_func, ipt, features, count, stride):
+    tmp = block_func(ipt, features, stride)
+    for i in range(1, count):
+        tmp = block_func(tmp, features, 1)
+    return tmp
+```
+
+The following are the components of `resnet_cifar10`:
+
+1. The lowest level is `conv_bn_layer`.
+2. The middle level consists of three `layer_warp`, each of which uses the left residual block in Figure 9.
+3. The last level is average pooling layer.
+
+Note: besides the first convolutional layer and the last fully-connected layer, the total number of layers in three `layer_warp` should be dividable by 6, that is the depth of `resnet_cifar10` should satisfy $(depth - 2) % 6 == 0$.
+
+```python
+def resnet_cifar10(ipt, depth=32):
+    # depth should be one of 20, 32, 44, 56, 110, 1202
+    assert (depth - 2) % 6 == 0
+    n = (depth - 2) / 6
+    nStages = {16, 64, 128}
+    conv1 = conv_bn_layer(
+        ipt, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1)
+    res1 = layer_warp(basicblock, conv1, 16, n, 1)
+    res2 = layer_warp(basicblock, res1, 32, n, 2)
+    res3 = layer_warp(basicblock, res2, 64, n, 2)
+    pool = paddle.layer.img_pool(
+        input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg())
+    return pool
+```
+
+## Model Training
+
+### Define Parameters
+
+First, we create the model parameters according to the previous model configuration `cost`.
+
+```python
+# Create parameters
+parameters = paddle.parameters.create(cost)
+```
+
+### Create Trainer
+
+Before creating a training module, it is necessary to set the algorithm.
+Here we specify `Momentum` optimization algorithm via `paddle.optimizer`.
+
+```python
+# Create optimizer
+momentum_optimizer = paddle.optimizer.Momentum(
+    momentum=0.9,
+    regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
+    learning_rate=0.1 / 128.0,
+    learning_rate_decay_a=0.1,
+    learning_rate_decay_b=50000 * 100,
+    learning_rate_schedule='discexp')
+
+# Create trainer
+trainer = paddle.trainer.SGD(cost=cost,
+                             parameters=parameters,
+                             update_equation=momentum_optimizer)
+```
+
+The learning rate adjustment policy can be defined with variables `learning_rate_decay_a`($a$), `learning_rate_decay_b`($b$) and `learning_rate_schedule`. In this example, discrete exponential method is used for adjusting learning rate. The formula is as follows,
+$$  lr = lr_{0} * a^ {\lfloor \frac{n}{ b}\rfloor} $$
+where $n$ is the number of processed samples, $lr_{0}$ is the learning_rate.
+
+### Training
+
+`cifar.train10()` will yield records during each pass, after shuffling, a batch input is generated for training.
+
+```python
+reader=paddle.batch(
+    paddle.reader.shuffle(
+        paddle.dataset.cifar.train10(), buf_size=50000),
+        batch_size=128)
+```
+
+`feeding` is devoted to specifying the correspondence between each yield record and `paddle.layer.data`. For instance,
+ the first column of data generated by `cifar.train10()` corresponds to image layer's feature.
+
+```python
+feeding={'image': 0,
+         'label': 1}
+```
+
+Callback function `event_handler` will be called during training when a pre-defined event happens.
+
+`event_handler_plot`is used to plot a figure like below：
+
+![png](./image/train_and_test.png)
+
+```python
+from paddle.v2.plot import Ploter
+
+train_title = "Train cost"
+test_title = "Test cost"
+cost_ploter = Ploter(train_title, test_title)
+
+step = 0
+def event_handler_plot(event):
+    global step
+    if isinstance(event, paddle.event.EndIteration):
+        if step % 1 == 0:
+            cost_ploter.append(train_title, step, event.cost)
+            cost_ploter.plot()
+        step += 1
+    if isinstance(event, paddle.event.EndPass):
+        result = trainer.test(
+            reader=paddle.batch(
+                paddle.dataset.cifar.test10(), batch_size=128),
+            feeding=feeding)
+        cost_ploter.append(test_title, step, result.cost)
+```
+
+`event_handler` is used to plot some text data when training.
+
+```python
+# event handler to track training and testing process
+def event_handler(event):
+    if isinstance(event, paddle.event.EndIteration):
+        if event.batch_id % 100 == 0:
+            print "\nPass %d, Batch %d, Cost %f, %s" % (
+                event.pass_id, event.batch_id, event.cost, event.metrics)
+        else:
+            sys.stdout.write('.')
+            sys.stdout.flush()
+    if isinstance(event, paddle.event.EndPass):
+        # save parameters
+        with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
+            parameters.to_tar(f)
+
+        result = trainer.test(
+            reader=paddle.batch(
+                paddle.dataset.cifar.test10(), batch_size=128),
+            feeding=feeding)
+        print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+```
+
+Finally, we can invoke `trainer.train` to start training:
+
+```python
+trainer.train(
+    reader=reader,
+    num_passes=200,
+    event_handler=event_handler_plot,
+    feeding=feeding)
+```
+
+Here is an example log after training for one pass. The average error rates are 0.6875 on the training set and 0.8852 on the validation set.
+
+```text
+Pass 0, Batch 0, Cost 2.473182, {'classification_error_evaluator': 0.9140625}
+...................................................................................................
+Pass 0, Batch 100, Cost 1.913076, {'classification_error_evaluator': 0.78125}
+...................................................................................................
+Pass 0, Batch 200, Cost 1.783041, {'classification_error_evaluator': 0.7421875}
+...................................................................................................
+Pass 0, Batch 300, Cost 1.668833, {'classification_error_evaluator': 0.6875}
+..........................................................................................
+Test with Pass 0, {'classification_error_evaluator': 0.885200023651123}
+```
+
+Figure 12 shows the curve of training error rate, which indicates it converges at Pass 200 with error rate 8.54%.
+<p align="center">
+<img src="image/plot_en.png" width="400" ><br/>
+Figure 12. The error rate of VGG model on CIFAR10
+</p>
+
+
+
+## Application
+
+After training is done, users can use the trained model to classify images. The following code shows how to infer through `paddle.infer` interface. You can remove the comments to change the model name.
+
+```python
+from PIL import Image
+import numpy as np
+import os
+def load_image(file):
+    im = Image.open(file)
+    im = im.resize((32, 32), Image.ANTIALIAS)
+    im = np.array(im).astype(np.float32)
+    # The storage order of the loaded image is W(widht),
+    # H(height), C(channel). PaddlePaddle requires
+    # the CHW order, so transpose them.
+    im = im.transpose((2, 0, 1)) # CHW
+    # In the training phase, the channel order of CIFAR
+    # image is B(Blue), G(green), R(Red). But PIL open
+    # image in RGB mode. It must swap the channel order.
+    im = im[(2, 1, 0),:,:] # BGR
+    im = im.flatten()
+    im = im / 255.0
+    return im
+test_data = []
+cur_dir = os.getcwd()
+test_data.append((load_image(cur_dir + '/image/dog.png'),))
+
+# users can remove the comments and change the model name
+# with open('params_pass_50.tar', 'r') as f:
+#    parameters = paddle.parameters.Parameters.from_tar(f)
+
+probs = paddle.infer(
+    output_layer=out, parameters=parameters, input=test_data)
+lab = np.argsort(-probs) # probs and lab are the results of one batch data
+print "Label of image/dog.png is: %d" % lab[0][0]
+```
+
+
+## Conclusion
+
+Traditional image classification methods have complicated frameworks that involve multiple stages of processing. In contrast, CNN models can be trained end-to-end with a significant increase in classification accuracy. In this chapter, we introduced three models -- VGG, GoogleNet, ResNet and provided PaddlePaddle config files for training VGG and ResNet on CIFAR10. We also explained how to perform prediction and feature extraction using the PaddlePaddle API. For other datasets such as ImageNet, the procedure for config and training are the same and you are welcome to give it a try.
+
+
+## Reference
+
+[1] D. G. Lowe, [Distinctive image features from scale-invariant keypoints](http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf). IJCV, 60(2):91-110, 2004.
+
+[2] N. Dalal, B. Triggs, [Histograms of Oriented Gradients for Human Detection](http://vision.stanford.edu/teaching/cs231b_spring1213/papers/CVPR05_DalalTriggs.pdf), Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
+
+[3] Ahonen, T., Hadid, A., and Pietikinen, M. (2006). [Face description with local binary patterns: Application to face recognition](http://ieeexplore.ieee.org/document/1717463/). PAMI, 28.
+
+[4] J. Sivic, A. Zisserman, [Video Google: A Text Retrieval Approach to Object Matching in Videos](http://www.robots.ox.ac.uk/~vgg/publications/papers/sivic03.pdf), Proc. Ninth Int'l Conf. Computer Vision, pp. 1470-1478, 2003.
+
+[5] B. Olshausen, D. Field, [Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1?](http://redwood.psych.cornell.edu/papers/olshausen_field_1997.pdf), Vision Research, vol. 37, pp. 3311-3325, 1997.
+
+[6] Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., and Gong, Y. (2010). [Locality-constrained Linear Coding for image classification](http://ieeexplore.ieee.org/abstract/document/5540018/). In CVPR.
+
+[7] Perronnin, F., Sánchez, J., & Mensink, T. (2010). [Improving the fisher kernel for large-scale image classification](http://dl.acm.org/citation.cfm?id=1888101). In ECCV (4).
+
+[8] Lin, Y., Lv, F., Cao, L., Zhu, S., Yang, M., Cour, T., Yu, K., and Huang, T. (2011). [Large-scale image clas- sification: Fast feature extraction and SVM training](http://ieeexplore.ieee.org/document/5995477/). In CVPR.
+
+[9] Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). [ImageNet classification with deep convolutional neu- ral networks](http://www.cs.toronto.edu/~kriz/imagenet_classification_with_deep_convolutional.pdf). In NIPS.
+
+[10] G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. Salakhutdinov. [Improving neural networks by preventing co-adaptation of feature detectors](https://arxiv.org/abs/1207.0580). arXiv preprint arXiv:1207.0580, 2012.
+
+[11] K. Chatfield, K. Simonyan, A. Vedaldi, A. Zisserman. [Return of the Devil in the Details: Delving Deep into Convolutional Nets](https://arxiv.org/abs/1405.3531). BMVC, 2014。
+
+[12] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., [Going deeper with convolutions](https://arxiv.org/abs/1409.4842). In: CVPR. (2015)
+
+[13] Lin, M., Chen, Q., and Yan, S. [Network in network](https://arxiv.org/abs/1312.4400). In Proc. ICLR, 2014.
+
+[14] S. Ioffe and C. Szegedy. [Batch normalization: Accelerating deep network training by reducing internal covariate shift](https://arxiv.org/abs/1502.03167). In ICML, 2015.
+
+[15] K. He, X. Zhang, S. Ren, J. Sun. [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385). CVPR 2016.
+
+[16] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z. [Rethinking the incep-tion architecture for computer vision](https://arxiv.org/abs/1512.00567). In: CVPR. (2016).
+
+[17] Szegedy, C., Ioffe, S., Vanhoucke, V. [Inception-v4, inception-resnet and the impact of residual connections on learning](https://arxiv.org/abs/1602.07261). arXiv:1602.07261 (2016).
+
+[18] Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J. and Zisserman, A. [The Pascal Visual Object Classes Challenge: A Retrospective]((http://link.springer.com/article/10.1007/s11263-014-0733-5)). International Journal of Computer Vision, 111(1), 98-136, 2015.
+
+[19] He, K., Zhang, X., Ren, S., and Sun, J. [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://arxiv.org/abs/1502.01852). ArXiv e-prints, February 2015.
+
+[20] http://deeplearning.net/tutorial/lenet.html
+
+[21] https://www.cs.toronto.edu/~kriz/cifar.html
+
+[22] http://cs231n.github.io/classification/
+
+<br/>
+This tutorial is contributed by <a xmlns:cc="http://creativecommons.org/ns#" href="http://book.paddlepaddle.org" property="cc:attributionName" rel="cc:attributionURL">PaddlePaddle</a>, and licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+
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