diff --git a/.gitignore b/.gitignore index b7dfec34eb31252fcb42b3bab9f70e3a0676ba22..db437f8ac27fcfb4e18e4d71ec0f8b18c90fe1a3 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,6 @@ deprecated *~ pandoc.template -.DS_Store \ No newline at end of file +.DS_Store +.idea +py_env* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 655b823a76dd4b9b5be10c1a2b3d9c9cb11dc799..4060c19ca8b92e43aa66975f2375da34aaab377e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -31,12 +31,10 @@ description: Convert README.md into index.html and README.en.md into index.en.html entry: python pre-commit-hooks/convert_markdown_into_html.py language: system - files: \.md$ -- repo: local - hooks: - - id: convert-markdown-into-ipynb - name: convert-markdown-into-ipynb - description: Convert README.md into README.ipynb and README.en.md into README.en.ipynb - entry: ./pre-commit-hooks/convert_markdown_into_ipynb.sh - language: system - files: \.md$ + files: .+README(\.en)?\.md$ + - id: convert-markdown-into-ipynb + name: convert-markdown-into-ipynb + description: Convert README.md into README.ipynb and README.en.md into README.en.ipynb + entry: ./pre-commit-hooks/convert_markdown_into_ipynb.sh + language: system + files: .+README(\.en)?\.md$ diff --git a/.theme/PP_w.png b/.theme/PP_w.png new file mode 100644 index 0000000000000000000000000000000000000000..bc58b0b458135773fcde5ee941ea095e3d4d07a0 Binary files /dev/null and b/.theme/PP_w.png differ diff --git a/.tmpl/github-markdown.css b/.tmpl/github-markdown.css index 42e38eba8cf4a5bc93c256843fbd765dc80facea..97615ce2c76b02ce6b4f95e11a71b54f7c313014 100644 --- a/.tmpl/github-markdown.css +++ b/.tmpl/github-markdown.css @@ -188,7 +188,7 @@ } .markdown-body a { - color: #4078c0; + color: #597cf1; text-decoration: none; } diff --git a/.travis.yml b/.travis.yml index 3cf90df9582abf65371ddad5ca5eab8cb687dc77..b4b1e0f9ae819923373736317b77c75787d0d39b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,7 +16,7 @@ addons: - python2.7-dev - golang before_install: - - pip install virtualenv pre-commit + - pip install -U virtualenv pre-commit pip - GOPATH=/tmp/go go get -u github.com/wangkuiyi/ipynb/markdown-to-ipynb script: - travis/precommit.sh diff --git a/fit_a_line/index.en.html b/fit_a_line/index.en.html index 32f4146197d87c7feb459ebfcb784ee1f92f1c11..22d6aeaa7da7ef9277ca59ac29f8b0e62ba78fa3 100644 --- a/fit_a_line/index.en.html +++ b/fit_a_line/index.en.html @@ -35,7 +35,7 @@ -
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diff --git a/gan/index.html b/gan/index.html index 4cee22efeb2f27936b87292d7db23dd1f7cb1bad..b2d90bde37681294f74d1ac8a051e630c463c838 100644 --- a/gan/index.html +++ b/gan/index.html @@ -35,7 +35,7 @@ -
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diff --git a/image_caption/index.html b/image_caption/index.html index eb2c91f9e3a7aba340da33415b5a3fbb5ef6d32b..1cce2a25ce4ffbbabca7791673b824fbfa8e95a3 100644 --- a/image_caption/index.html +++ b/image_caption/index.html @@ -35,7 +35,7 @@ -
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diff --git a/image_classification/README.ipynb b/image_classification/README.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e542f32c1fedba5ccd12cc04ad053aa5df4a0dec --- /dev/null +++ b/image_classification/README.ipynb @@ -0,0 +1,877 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 图像分类\n", + "\n", + "本教程源代码目录在[book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。\n", + "\n", + "## 背景介绍\n", + "\n", + "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。\n", + "\n", + "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。\n", + "\n", + "\n", + "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。\n", + "\n", + "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。\n", + "\n", + "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。\n", + "\n", + "## 效果展示\n", + "\n", + "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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/dog_cat.png \" width=\"350\" \u003e\u003cbr/\u003e\n", + "图1. 通用图像分类展示\n", + "\u003c/p\u003e\n", + "\n", + "\n", + "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。\n", + "\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/flowers.png\" width=\"400\" \u003e\u003cbr/\u003e\n", + "图2. 细粒度图像分类展示\n", + "\u003c/p\u003e\n", + "\n", + "\n", + "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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/variations.png\" width=\"550\" \u003e\u003cbr/\u003e\n", + "图3. 扰动图片展示[22]\n", + "\u003c/p\u003e\n", + "\n", + "## 模型概览\n", + "\n", + "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。\n", + "\n", + "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。\n", + " 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。\n", + " 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。\n", + " 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。\n", + " 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。\n", + "\n", + "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)\\]。\n", + "\n", + "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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/ilsvrc.png\" width=\"500\" \u003e\u003cbr/\u003e\n", + "图4. ILSVRC图像分类Top-5错误率\n", + "\u003c/p\u003e\n", + "\n", + "### CNN\n", + "\n", + "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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/lenet.png\"\u003e\u003cbr/\u003e\n", + "图5. CNN网络示例[20]\n", + "\u003c/p\u003e\n", + "\n", + "- 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。\n", + "- 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。\n", + "- 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。\n", + "- 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。\n", + "- 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。\n", + "\n", + "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。\n", + "\n", + "接下来我们主要介绍VGG，GoogleNet和ResNet网络结构。\n", + "\n", + "### VGG\n", + "\n", + "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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/vgg16.png\" width=\"750\" \u003e\u003cbr/\u003e\n", + "图6. 基于ImageNet的VGG16模型\n", + "\u003c/p\u003e\n", + "\n", + "### GoogleNet\n", + "\n", + "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。\n", + "\n", + "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。\n", + "\n", + "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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/inception.png\" width=\"800\" \u003e\u003cbr/\u003e\n", + "图7. Inception模块\n", + "\u003c/p\u003e\n", + "\n", + "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。\n", + "\n", + "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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/googlenet.jpeg\" \u003e\u003cbr/\u003e\n", + "图8. GoogleNet[12]\n", + "\u003c/p\u003e\n", + "\n", + "\n", + "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。\n", + "\n", + "\n", + "### ResNet\n", + "\n", + "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。\n", + "\n", + "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-\u003e64)，下面的1x1卷积用来升维(图示例即64-\u003e256)，这样中间3x3卷积的输入和输出通道数都较小(图示例即64-\u003e64)。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/resnet_block.jpg\" width=\"400\"\u003e\u003cbr/\u003e\n", + "图9. 残差模块\n", + "\u003c/p\u003e\n", + "\n", + "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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/resnet.png\"\u003e\u003cbr/\u003e\n", + "图10. 基于ImageNet的ResNet模型\n", + "\u003c/p\u003e\n", + "\n", + "\n", + "## 数据准备\n", + "\n", + "通用图像分类公开的标准数据集常用的有[CIFAR](\u003chttps://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。\n", + "\n", + "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](\u003chttps://www.cs.toronto.edu/~kriz/cifar.html\u003e)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。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/cifar.png\" width=\"350\"\u003e\u003cbr/\u003e\n", + "图11. CIFAR10数据集[21]\n", + "\u003c/p\u003e\n", + "\n", + "Paddle API提供了自动加载cifar数据集模块 `paddle.dataset.cifar`。\n", + "\n", + "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。\n", + "\n", + "### 模型结构\n", + "\n", + "#### Paddle 初始化\n", + "\n", + "通过 `paddle.init`，初始化Paddle是否使用GPU，trainer的数目等等。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "import sys\n", + "import paddle.v2 as paddle\n", + "from vgg import vgg_bn_drop\n", + "from resnet import resnet_cifar10\n", + "\n", + "# PaddlePaddle init\n", + "paddle.init(use_gpu=False, trainer_count=1)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "本教程中我们提供了VGG和ResNet两个模型的配置。\n", + "\n", + "#### VGG\n", + "\n", + "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。\n", + "\n", + "1. 定义数据输入及其维度\n", + "\n", + " 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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + " datadim = 3 * 32 * 32\n", + " classdim = 10\n", + "\n", + " image = paddle.layer.data(\n", + " name=\"image\", type=paddle.data_type.dense_vector(datadim))\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "2. 定义VGG网络核心模块\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + " net = vgg_bn_drop(image)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " 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：\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + " def vgg_bn_drop(input):\n", + " def conv_block(ipt, num_filter, groups, dropouts, num_channels=None):\n", + " return paddle.networks.img_conv_group(\n", + " input=ipt,\n", + " num_channels=num_channels,\n", + " pool_size=2,\n", + " pool_stride=2,\n", + " conv_num_filter=[num_filter] * groups,\n", + " conv_filter_size=3,\n", + " conv_act=paddle.activation.Relu(),\n", + " conv_with_batchnorm=True,\n", + " conv_batchnorm_drop_rate=dropouts,\n", + " pool_type=paddle.pooling.Max())\n", + "\n", + " conv1 = conv_block(input, 64, 2, [0.3, 0], 3)\n", + " conv2 = conv_block(conv1, 128, 2, [0.4, 0])\n", + " conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])\n", + " conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])\n", + " conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])\n", + "\n", + " drop = paddle.layer.dropout(input=conv5, dropout_rate=0.5)\n", + " fc1 = paddle.layer.fc(input=drop, size=512, act=paddle.activation.Linear())\n", + " bn = paddle.layer.batch_norm(\n", + " input=fc1,\n", + " act=paddle.activation.Relu(),\n", + " layer_attr=paddle.attr.Extra(drop_rate=0.5))\n", + " fc2 = paddle.layer.fc(input=bn, size=512, act=paddle.activation.Linear())\n", + " return fc2\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " 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-\u003eBN-\u003eReLu-\u003eDropout` 和 一组 `Pooling` 组成，\n", + "\n", + " 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。\n", + "\n", + " 2.3. 最后接两层512维的全连接。\n", + "\n", + "3. 定义分类器\n", + "\n", + " 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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + " out = paddle.layer.fc(input=net,\n", + " size=classdim,\n", + " act=paddle.activation.Softmax())\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "4. 定义损失函数和网络输出\n", + "\n", + " 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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + " lbl = paddle.layer.data(\n", + " name=\"label\", type=paddle.data_type.integer_value(classdim))\n", + " cost = paddle.layer.classification_cost(input=out, label=lbl)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### ResNet\n", + "\n", + "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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "net = resnet_cifar10(image, depth=56)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "先介绍`resnet_cifar10`中的一些基本函数，再介绍网络连接过程。\n", + "\n", + " - `conv_bn_layer` : 带BN的卷积层。\n", + " - `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。\n", + " - `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。\n", + " - `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。\n", + " - `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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "def conv_bn_layer(input,\n", + " ch_out,\n", + " filter_size,\n", + " stride,\n", + " padding,\n", + " active_type=paddle.activation.Relu(),\n", + " ch_in=None):\n", + " tmp = paddle.layer.img_conv(\n", + " input=input,\n", + " filter_size=filter_size,\n", + " num_channels=ch_in,\n", + " num_filters=ch_out,\n", + " stride=stride,\n", + " padding=padding,\n", + " act=paddle.activation.Linear(),\n", + " bias_attr=False)\n", + " return paddle.layer.batch_norm(input=tmp, act=active_type)\n", + "\n", + "def shortcut(ipt, n_in, n_out, stride):\n", + " if n_in != n_out:\n", + " return conv_bn_layer(ipt, n_out, 1, stride, 0,\n", + " paddle.activation.Linear())\n", + " else:\n", + " return ipt\n", + "\n", + "def basicblock(ipt, ch_out, stride):\n", + " ch_in = ch_out * 2\n", + " tmp = conv_bn_layer(ipt, ch_out, 3, stride, 1)\n", + " tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, paddle.activation.Linear())\n", + " short = shortcut(ipt, ch_in, ch_out, stride)\n", + " return paddle.layer.addto(input=[tmp, short], act=paddle.activation.Relu())\n", + "\n", + "def layer_warp(block_func, ipt, features, count, stride):\n", + " tmp = block_func(ipt, features, stride)\n", + " for i in range(1, count):\n", + " tmp = block_func(tmp, features, 1)\n", + " return tmp\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "`resnet_cifar10` 的连接结构主要有以下几个过程。\n", + "\n", + "1. 底层输入连接一层 `conv_bn_layer`，即带BN的卷积层。\n", + "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。\n", + "3. 最后对网络做均值池化并返回该层。\n", + "\n", + "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 $(depth - 2) % 6 == 0$ 。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "def resnet_cifar10(ipt, depth=32):\n", + " # depth should be one of 20, 32, 44, 56, 110, 1202\n", + " assert (depth - 2) % 6 == 0\n", + " n = (depth - 2) / 6\n", + " nStages = {16, 64, 128}\n", + " conv1 = conv_bn_layer(\n", + " ipt, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1)\n", + " res1 = layer_warp(basicblock, conv1, 16, n, 1)\n", + " res2 = layer_warp(basicblock, res1, 32, n, 2)\n", + " res3 = layer_warp(basicblock, res2, 64, n, 2)\n", + " pool = paddle.layer.img_pool(\n", + " input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg())\n", + " return pool\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## 训练模型\n", + "\n", + "### 定义参数\n", + "\n", + "首先依据模型配置的`cost`定义模型参数。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "# Create parameters\n", + "parameters = paddle.parameters.create(cost)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "print parameters.keys()\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "### 构造训练(Trainer)\n", + "\n", + "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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "# Create optimizer\n", + "momentum_optimizer = paddle.optimizer.Momentum(\n", + " momentum=0.9,\n", + " regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),\n", + " learning_rate=0.1 / 128.0,\n", + " learning_rate_decay_a=0.1,\n", + " learning_rate_decay_b=50000 * 100,\n", + " learning_rate_schedule='discexp',\n", + " batch_size=128)\n", + "\n", + "# Create trainer\n", + "trainer = paddle.trainer.SGD(cost=cost,\n", + " parameters=parameters,\n", + " update_equation=momentum_optimizer)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "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`。\n", + "\n", + "$$ lr = lr_{0} * a^ {\\lfloor \\frac{n}{ b}\\rfloor} $$\n", + "\n", + "\n", + "### 训练\n", + "\n", + "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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "reader=paddle.batch(\n", + " paddle.reader.shuffle(\n", + " paddle.dataset.cifar.train10(), buf_size=50000),\n", + " batch_size=128)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "feeding={'image': 0,\n", + " 'label': 1}\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "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。\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "# End batch and end pass event handler\n", + "def event_handler(event):\n", + " if isinstance(event, paddle.event.EndIteration):\n", + " if event.batch_id % 100 == 0:\n", + " print \"\\nPass %d, Batch %d, Cost %f, %s\" % (\n", + " event.pass_id, event.batch_id, event.cost, event.metrics)\n", + " else:\n", + " sys.stdout.write('.')\n", + " sys.stdout.flush()\n", + " if isinstance(event, paddle.event.EndPass):\n", + " result = trainer.test(\n", + " reader=paddle.batch(\n", + " paddle.dataset.cifar.test10(), batch_size=128),\n", + " feeding=feeding)\n", + " print \"\\nTest with Pass %d, %s\" % (event.pass_id, result.metrics)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "通过`trainer.train`函数训练:\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "editable": true + }, + "source": [ + "trainer.train(\n", + " reader=reader,\n", + " num_passes=200,\n", + " event_handler=event_handler,\n", + " feeding=feeding)\n" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "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 。\n", + "\n", + "```text\n", + "Pass 0, Batch 0, Cost 2.473182, {'classification_error_evaluator': 0.9140625}\n", + "...................................................................................................\n", + "Pass 0, Batch 100, Cost 1.913076, {'classification_error_evaluator': 0.78125}\n", + "...................................................................................................\n", + "Pass 0, Batch 200, Cost 1.783041, {'classification_error_evaluator': 0.7421875}\n", + "...................................................................................................\n", + "Pass 0, Batch 300, Cost 1.668833, {'classification_error_evaluator': 0.6875}\n", + "..........................................................................................\n", + "Test with Pass 0, {'classification_error_evaluator': 0.885200023651123}\n", + "```\n", + "\n", + "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%。\n", + "\n", + "\u003cp align=\"center\"\u003e\n", + "\u003cimg src=\"image/plot.png\" width=\"400\" \u003e\u003cbr/\u003e\n", + "图12. CIFAR10数据集上VGG模型的分类错误率\n", + "\u003c/p\u003e\n", + "\n", + "\n", + "## 总结\n", + "\n", + "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。\n", + "\n", + "\n", + "## 参考文献\n", + "\n", + "[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.\n", + "\n", + "[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.\n", + "\n", + "[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.\n", + "\n", + "[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.\n", + "\n", + "[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.\n", + "\n", + "[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.\n", + "\n", + "[7] Perronnin, F., Sánchez, J., \u0026 Mensink, T. (2010). [Improving the fisher kernel for large-scale image classification](http://dl.acm.org/citation.cfm?id=1888101). In ECCV (4).\n", + "\n", + "[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.\n", + "\n", + "[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.\n", + "\n", + "[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.\n", + "\n", + "[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。\n", + "\n", + "[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)\n", + "\n", + "[13] Lin, M., Chen, Q., and Yan, S. [Network in network](https://arxiv.org/abs/1312.4400). In Proc. ICLR, 2014.\n", + "\n", + "[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.\n", + "\n", + "[15] K. He, X. Zhang, S. Ren, J. Sun. [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385). CVPR 2016.\n", + "\n", + "[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).\n", + "\n", + "[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).\n", + "\n", + "[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.\n", + "\n", + "[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.\n", + "\n", + "[20] http://deeplearning.net/tutorial/lenet.html\n", + "\n", + "[21] https://www.cs.toronto.edu/~kriz/cifar.html\n", + "\n", + "[22] http://cs231n.github.io/classification/\n", + "\n", + "\u003cbr/\u003e\n", + "\u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"\u003e\u003cimg alt=\"知识共享许可协议\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /\u003e\u003c/a\u003e\u003cbr /\u003e\u003cspan xmlns:dct=\"http://purl.org/dc/terms/\" href=\"http://purl.org/dc/dcmitype/Text\" property=\"dct:title\" rel=\"dct:type\"\u003e本教程\u003c/span\u003e 由 \u003ca xmlns:cc=\"http://creativecommons.org/ns#\" href=\"http://book.paddlepaddle.org\" property=\"cc:attributionName\" rel=\"cc:attributionURL\"\u003ePaddlePaddle\u003c/a\u003e 创作，采用 \u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"\u003e知识共享 署名-非商业性使用-相同方式共享 4.0 国际 许可协议\u003c/a\u003e进行许可。\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/image_classification/README.md b/image_classification/README.md index dd09f7994fa06739601aa7efbfb4a4699d7cea52..c32924b86fa58c04df2ee3766fe87860949ed4da 100644 --- a/image_classification/README.md +++ b/image_classification/README.md @@ -1,5 +1,4 @@ -图像分类 -======= +# 图像分类 本教程源代码目录在[book/image_classification](https://github.com/PaddlePaddle/book/tree/develop/image_classification)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。 diff --git a/image_classification/index.en.html b/image_classification/index.en.html index 457b54851ef970f228662ff5a7e52485c64d8647..b26f8724298ac8b3bbb99896beb9b47f05d861ee 100644 --- a/image_classification/index.en.html +++ b/image_classification/index.en.html @@ -35,7 +35,7 @@ -
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diff --git a/image_classification/index.html b/image_classification/index.html index 21446e9413af4cddf98fed5f392c5aaa406479be..00efecac57fd0d375785a7d74c7c4c40791ce7d4 100644 --- a/image_classification/index.html +++ b/image_classification/index.html @@ -35,13 +35,12 @@ -
+