From 3a29e1efb28e0a386e876101d3935ca7faa7cd87 Mon Sep 17 00:00:00 2001 From: pc-dell-liu <2904500451@qq.com> Date: Tue, 6 Nov 2018 22:43:31 +0800 Subject: [PATCH 01/26] =?UTF-8?q?ch12=E6=89=A9=E5=85=85=E5=86=85=E5=AE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../第十二章_网络搭建及训练.md | 61 ++++++++++++++----- 1 file changed, 45 insertions(+), 16 deletions(-) diff --git a/ch12_网络搭建及训练/第十二章_网络搭建及训练.md b/ch12_网络搭建及训练/第十二章_网络搭建及训练.md index 7a60ce7..06cfca4 100644 --- a/ch12_网络搭建及训练/第十二章_网络搭建及训练.md +++ b/ch12_网络搭建及训练/第十二章_网络搭建及训练.md @@ -1,24 +1,30 @@ -10.1 网络搭建有什么原则？ -10.1.1新手原则。 +###10.1 网络搭建有什么原则？ + +####10.1.1新手原则。 + 刚入门的新手不建议直接上来就开始搭建网络模型。比较建议的学习顺序如下： - 1.了解神经网络工作原理，熟悉基本概念及术语。 - 2.阅读经典网络模型论文+实现源码(深度学习框架视自己情况而定)。 - 3.找数据集动手跑一个网络，可以尝试更改已有的网络模型结构。 - 4.根据自己的项目需要设计网络。 -10.1.2深度优先原则。 -通常增加网络深度可以提高准确率，但同时会牺牲一些速度和内存。 +####10.1.2深度优先原则。 + +通常增加网络深度可以提高准确率，但同时会牺牲一些速度和内存。但深度不是盲目堆起来的，一定要在浅层网络有一定效果的基础上，增加深度。深度增加是为了增加模型的准确率，如果浅层都学不到东西，深了也没效果。 + +####10.1.3卷积核size一般为奇数。 -10.1.3卷积核size一般为奇数。 卷积核为奇数有以下好处： - 1 保证锚点刚好在中间，方便以 central pixel为标准进行滑动卷积，避免了位置信息发生偏移 。 - 2 保证在填充（Padding）时，在图像之间添加额外的零层，图像的两边仍然对称。 -10.1.4卷积核不是越大越好。 +####10.1.4卷积核不是越大越好。 + AlexNet中用到了一些非常大的卷积核，比如11×11、5×5卷积核，之前人们的观念是，卷积核越大，感受野越大，看到的图片信息越多，因此获得的特征越好。但是大的卷积核会导致计算量的暴增，不利于模型深度的增加，计算性能也会降低。于是在VGG、Inception网络中，利用2个3×3卷积核的组合比1个5×5卷积核的效果更佳，同时参数量（3×3×2+1=19<26=5×5×1+1）被降低，因此后来3×3卷积核被广泛应用在各种模型中。 -10.2 有哪些经典的网络模型值得我们去学习的？ +###10.2 有哪些经典的网络模型值得我们去学习的？ + 提起经典的网络模型就不得不提起计算机视觉领域的经典比赛：ILSVRC .其全称是 ImageNet Large Scale Visual Recognition Challenge.正是因为ILSVRC 2012挑战赛上的AlexNet横空出世，使得全球范围内掀起了一波深度学习热潮。这一年也被称作“深度学习元年”。而在历年ILSVRC比赛中每次刷新比赛记录的那些神经网络也成为了人们心中的经典，成为学术界与工业届竞相学习与复现的对象，并在此基础上展开新的研究。 @@ -40,7 +46,7 @@ AlexNet中用到了一些非常大的卷积核，比如11×11、5×5卷积核， >- 2 VGGNet 论文:[Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) -代码实现:[tensorflow]https://github.com/tensorflow/tensorflow/blob/361a82d73a50a800510674b3aaa20e4845e56434/tensorflow/contrib/slim/python/slim/nets/vgg.py) +代码实现:[tensorflow](https://github.com/tensorflow/tensorflow/blob/361a82d73a50a800510674b3aaa20e4845e56434/tensorflow/contrib/slim/python/slim/nets/vgg.py) 主要特点： >> - 1.网络结构更深。 >> - 2.普遍使用小卷积核。 @@ -64,35 +70,58 @@ AlexNet中用到了一些非常大的卷积核，比如11×11、5×5卷积核， >> 提出了feature recalibration，通过引入 attention 重新加权，可以得到抑制无效特征，提升有效特征的权重，并很容易地和现有网络结合，提升现有网络性能，而计算量不会增加太多。 **CV领域网络结构演进历程：** -![CV领域网络结构演进历程](http://wx2.sinaimg.cn/mw690/005B3ViFly1fwthh0jw58j30q80aldgw.jpg) +![CV领域网络结构演进历程](./img/ch12/网络结构演进.png) **ILSVRC挑战赛历年冠军:** -![ILSVRC挑战赛历年冠军](http://wx4.sinaimg.cn/mw690/005B3ViFly1fwswhzquw2j31810or78b.jpg) +![ILSVRC挑战赛历年冠军](./img/ch12/历年冠军.png) 此后，ILSVRC挑战赛的名次一直是衡量一个研究机构或企业技术水平的重要标尺。 ILSVRC 2017 已是最后一届举办.2018年起，将由WebVision竞赛（Challenge on Visual Understanding by Learning from Web Data）来接棒。因此，即使ILSVRC挑战赛停办了，但其对深度学习的深远影响和巨大贡献，将永载史册。 -10.3 网络训练有哪些技巧吗？ -10.3.1.合适的数据集。 +###10.3 网络训练有哪些技巧吗？ + +####10.3.1.合适的数据集。 + - 1 没有明显脏数据(可以极大避免Loss输出为NaN)。 - 2 样本数据分布均匀。 -10.3.2.合适的预处理方法。 +####10.3.2.合适的预处理方法。 + 关于数据预处理，在Batch Normalization未出现之前预处理的主要做法是减去均值，然后除去方差。在Batch Normalization出现之后，减均值除方差的做法已经没有必要了。对应的预处理方法主要是数据筛查、数据增强等。 -10.3.3.网络的初始化。 +####10.3.3.网络的初始化。 + 网络初始化最粗暴的做法是参数赋值为全0，这是绝对不可取的。因为如果所有的参数都是0，那么所有神经元的输出都将是相同的，那在back propagation的时候同一层内所有神经元的行为也是相同的，这可能会直接导致模型失效，无法收敛。吴恩达视频中介绍的方法是将网络权重初始化均值为0、方差为1符合的正态分布的随机数据。 -10.3.4.小规模数据试练。 +####10.3.4.小规模数据试练。 + 在正式开始训练之前，可以先用小规模数据进行试练。原因如下： - 1 可以验证自己的训练流程对否。 - 2 可以观察收敛速度，帮助调整学习速率。 - 3 查看GPU显存占用情况，最大化batch_size(前提是进行了batch normalization，只要显卡不爆，尽量挑大的)。 -10.3.5.设置合理Learning Rate。 +####10.3.5.设置合理Learning Rate。 + - 1 太大。Loss爆炸、输出NaN等。 - 2 太小。收敛速度过慢，训练时长大大延长。 - 3 可变的学习速率。比如当输出准确率到达某个阈值后，可以让Learning Rate减半继续训练。 +####10.3.6.损失函数 + +损失函数主要分为两大类:分类损失和回归损失 +>1.回归损失： +>> - 1 均方误差(MSE 二次损失 L2损失) +它是我们的目标变量与预测值变量差值平方。 +>> - 2 平均绝对误差(MAE L1损失) +它是我们的目标变量与预测值变量差值绝对值。 +>关于MSE与MAE的比较。MSE更容易解决问题，但是MAE对于异常值更加鲁棒。更多关于MAE和MSE的性能，可以参考[L1vs.L2 Loss Function](https://rishy.github.io/ml/2015/07/28/l1-vs-l2-loss/) + + +>2.分类损失： +>> - 1 交叉熵损失函数。 +是目前神经网络中最常用的分类目标损失函数。 +>> - 2 合页损失函数 +>>合页损失函数广泛在支持向量机中使用，有时也会在损失函数中使用。缺点:合页损失函数是对错误越大的样本施以更严重的惩罚，但是这样会导致损失函数对噪声敏感。 + From b47491ab8e46b20301ce5e06aab2f5e6d460c57d Mon Sep 17 00:00:00 2001 From: pc-dell-liu <2904500451@qq.com> Date: Tue, 6 Nov 2018 22:53:41 +0800 Subject: [PATCH 02/26] =?UTF-8?q?=E6=A0=BC=E5=BC=8F=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../第十二章_网络搭建及训练.md | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/ch12_网络搭建及训练/第十二章_网络搭建及训练.md b/ch12_网络搭建及训练/第十二章_网络搭建及训练.md index 06cfca4..e8ab814 100644 --- a/ch12_网络搭建及训练/第十二章_网络搭建及训练.md +++ b/ch12_网络搭建及训练/第十二章_网络搭建及训练.md @@ -1,6 +1,6 @@ -###10.1 网络搭建有什么原则？ +### 10.1 网络搭建有什么原则？ -####10.1.1新手原则。 +#### 10.1.1新手原则。 刚入门的新手不建议直接上来就开始搭建网络模型。比较建议的学习顺序如下： - 1.了解神经网络工作原理，熟悉基本概念及术语。 @@ -8,22 +8,22 @@ - 3.找数据集动手跑一个网络，可以尝试更改已有的网络模型结构。 - 4.根据自己的项目需要设计网络。 -####10.1.2深度优先原则。 +#### 10.1.2深度优先原则。 通常增加网络深度可以提高准确率，但同时会牺牲一些速度和内存。但深度不是盲目堆起来的，一定要在浅层网络有一定效果的基础上，增加深度。深度增加是为了增加模型的准确率，如果浅层都学不到东西，深了也没效果。 -####10.1.3卷积核size一般为奇数。 +#### 10.1.3卷积核size一般为奇数。 卷积核为奇数有以下好处： - 1 保证锚点刚好在中间，方便以 central pixel为标准进行滑动卷积，避免了位置信息发生偏移 。 - 2 保证在填充（Padding）时，在图像之间添加额外的零层，图像的两边仍然对称。 -####10.1.4卷积核不是越大越好。 +#### 10.1.4卷积核不是越大越好。 AlexNet中用到了一些非常大的卷积核，比如11×11、5×5卷积核，之前人们的观念是，卷积核越大，感受野越大，看到的图片信息越多，因此获得的特征越好。但是大的卷积核会导致计算量的暴增，不利于模型深度的增加，计算性能也会降低。于是在VGG、Inception网络中，利用2个3×3卷积核的组合比1个5×5卷积核的效果更佳，同时参数量（3×3×2+1=19<26=5×5×1+1）被降低，因此后来3×3卷积核被广泛应用在各种模型中。 -###10.2 有哪些经典的网络模型值得我们去学习的？ +### 10.2 有哪些经典的网络模型值得我们去学习的？ 提起经典的网络模型就不得不提起计算机视觉领域的经典比赛：ILSVRC .其全称是 ImageNet Large Scale Visual Recognition Challenge.正是因为ILSVRC 2012挑战赛上的AlexNet横空出世，使得全球范围内掀起了一波深度学习热潮。这一年也被称作“深度学习元年”。而在历年ILSVRC比赛中每次刷新比赛记录的那些神经网络也成为了人们心中的经典，成为学术界与工业届竞相学习与复现的对象，并在此基础上展开新的研究。 @@ -79,35 +79,35 @@ AlexNet中用到了一些非常大的卷积核，比如11×11、5×5卷积核， 此后，ILSVRC挑战赛的名次一直是衡量一个研究机构或企业技术水平的重要标尺。 ILSVRC 2017 已是最后一届举办.2018年起，将由WebVision竞赛（Challenge on Visual Understanding by Learning from Web Data）来接棒。因此，即使ILSVRC挑战赛停办了，但其对深度学习的深远影响和巨大贡献，将永载史册。 -###10.3 网络训练有哪些技巧吗？ +### 10.3 网络训练有哪些技巧吗？ -####10.3.1.合适的数据集。 +#### 10.3.1.合适的数据集。 - 1 没有明显脏数据(可以极大避免Loss输出为NaN)。 - 2 样本数据分布均匀。 -####10.3.2.合适的预处理方法。 +#### 10.3.2.合适的预处理方法。 关于数据预处理，在Batch Normalization未出现之前预处理的主要做法是减去均值，然后除去方差。在Batch Normalization出现之后，减均值除方差的做法已经没有必要了。对应的预处理方法主要是数据筛查、数据增强等。 -####10.3.3.网络的初始化。 +#### 10.3.3.网络的初始化。 网络初始化最粗暴的做法是参数赋值为全0，这是绝对不可取的。因为如果所有的参数都是0，那么所有神经元的输出都将是相同的，那在back propagation的时候同一层内所有神经元的行为也是相同的，这可能会直接导致模型失效，无法收敛。吴恩达视频中介绍的方法是将网络权重初始化均值为0、方差为1符合的正态分布的随机数据。 -####10.3.4.小规模数据试练。 +#### 10.3.4.小规模数据试练。 在正式开始训练之前，可以先用小规模数据进行试练。原因如下： - 1 可以验证自己的训练流程对否。 - 2 可以观察收敛速度，帮助调整学习速率。 - 3 查看GPU显存占用情况，最大化batch_size(前提是进行了batch normalization，只要显卡不爆，尽量挑大的)。 -####10.3.5.设置合理Learning Rate。 +#### 10.3.5.设置合理Learning Rate。 - 1 太大。Loss爆炸、输出NaN等。 - 2 太小。收敛速度过慢，训练时长大大延长。 - 3 可变的学习速率。比如当输出准确率到达某个阈值后，可以让Learning Rate减半继续训练。 -####10.3.6.损失函数 +#### 10.3.6.损失函数 损失函数主要分为两大类:分类损失和回归损失 >1.回归损失： From 474bedd8dfcef19c718714bc7a39f80dfe41d826 Mon Sep 17 00:00:00 2001 From: pc-dell-liu <2904500451@qq.com> Date: Tue, 6 Nov 2018 22:57:47 +0800 Subject: [PATCH 03/26] =?UTF-8?q?=E6=A0=BC=E5=BC=8F=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../第十二章_网络搭建及训练.md | 27 ++++++++++--------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/ch12_网络搭建及训练/第十二章_网络搭建及训练.md b/ch12_网络搭建及训练/第十二章_网络搭建及训练.md index e8ab814..0cee9a6 100644 --- a/ch12_网络搭建及训练/第十二章_网络搭建及训练.md +++ b/ch12_网络搭建及训练/第十二章_网络搭建及训练.md @@ -1,6 +1,7 @@ -### 10.1 网络搭建有什么原则？ +# 第十二章 网络搭建及训练。 +## 10.1 网络搭建有什么原则？ -#### 10.1.1新手原则。 +### 10.1.1新手原则。 刚入门的新手不建议直接上来就开始搭建网络模型。比较建议的学习顺序如下： - 1.了解神经网络工作原理，熟悉基本概念及术语。 @@ -8,22 +9,22 @@ - 3.找数据集动手跑一个网络，可以尝试更改已有的网络模型结构。 - 4.根据自己的项目需要设计网络。 -#### 10.1.2深度优先原则。 +### 10.1.2深度优先原则。 通常增加网络深度可以提高准确率，但同时会牺牲一些速度和内存。但深度不是盲目堆起来的，一定要在浅层网络有一定效果的基础上，增加深度。深度增加是为了增加模型的准确率，如果浅层都学不到东西，深了也没效果。 -#### 10.1.3卷积核size一般为奇数。 +### 10.1.3卷积核size一般为奇数。 卷积核为奇数有以下好处： - 1 保证锚点刚好在中间，方便以 central pixel为标准进行滑动卷积，避免了位置信息发生偏移 。 - 2 保证在填充（Padding）时，在图像之间添加额外的零层，图像的两边仍然对称。 -#### 10.1.4卷积核不是越大越好。 +### 10.1.4卷积核不是越大越好。 AlexNet中用到了一些非常大的卷积核，比如11×11、5×5卷积核，之前人们的观念是，卷积核越大，感受野越大，看到的图片信息越多，因此获得的特征越好。但是大的卷积核会导致计算量的暴增，不利于模型深度的增加，计算性能也会降低。于是在VGG、Inception网络中，利用2个3×3卷积核的组合比1个5×5卷积核的效果更佳，同时参数量（3×3×2+1=19<26=5×5×1+1）被降低，因此后来3×3卷积核被广泛应用在各种模型中。 -### 10.2 有哪些经典的网络模型值得我们去学习的？ +## 10.2 有哪些经典的网络模型值得我们去学习的？ 提起经典的网络模型就不得不提起计算机视觉领域的经典比赛：ILSVRC .其全称是 ImageNet Large Scale Visual Recognition Challenge.正是因为ILSVRC 2012挑战赛上的AlexNet横空出世，使得全球范围内掀起了一波深度学习热潮。这一年也被称作“深度学习元年”。而在历年ILSVRC比赛中每次刷新比赛记录的那些神经网络也成为了人们心中的经典，成为学术界与工业届竞相学习与复现的对象，并在此基础上展开新的研究。 @@ -79,35 +80,35 @@ AlexNet中用到了一些非常大的卷积核，比如11×11、5×5卷积核， 此后，ILSVRC挑战赛的名次一直是衡量一个研究机构或企业技术水平的重要标尺。 ILSVRC 2017 已是最后一届举办.2018年起，将由WebVision竞赛（Challenge on Visual Understanding by Learning from Web Data）来接棒。因此，即使ILSVRC挑战赛停办了，但其对深度学习的深远影响和巨大贡献，将永载史册。 -### 10.3 网络训练有哪些技巧吗？ +## 10.3 网络训练有哪些技巧吗？ -#### 10.3.1.合适的数据集。 +### 10.3.1.合适的数据集。 - 1 没有明显脏数据(可以极大避免Loss输出为NaN)。 - 2 样本数据分布均匀。 -#### 10.3.2.合适的预处理方法。 +### 10.3.2.合适的预处理方法。 关于数据预处理，在Batch Normalization未出现之前预处理的主要做法是减去均值，然后除去方差。在Batch Normalization出现之后，减均值除方差的做法已经没有必要了。对应的预处理方法主要是数据筛查、数据增强等。 -#### 10.3.3.网络的初始化。 +### 10.3.3.网络的初始化。 网络初始化最粗暴的做法是参数赋值为全0，这是绝对不可取的。因为如果所有的参数都是0，那么所有神经元的输出都将是相同的，那在back propagation的时候同一层内所有神经元的行为也是相同的，这可能会直接导致模型失效，无法收敛。吴恩达视频中介绍的方法是将网络权重初始化均值为0、方差为1符合的正态分布的随机数据。 -#### 10.3.4.小规模数据试练。 +### 10.3.4.小规模数据试练。 在正式开始训练之前，可以先用小规模数据进行试练。原因如下： - 1 可以验证自己的训练流程对否。 - 2 可以观察收敛速度，帮助调整学习速率。 - 3 查看GPU显存占用情况，最大化batch_size(前提是进行了batch normalization，只要显卡不爆，尽量挑大的)。 -#### 10.3.5.设置合理Learning Rate。 +### 10.3.5.设置合理Learning Rate。 - 1 太大。Loss爆炸、输出NaN等。 - 2 太小。收敛速度过慢，训练时长大大延长。 - 3 可变的学习速率。比如当输出准确率到达某个阈值后，可以让Learning Rate减半继续训练。 -#### 10.3.6.损失函数 +### 10.3.6.损失函数 损失函数主要分为两大类:分类损失和回归损失 >1.回归损失： From 9a9fb92421a773c53c1abeb21da62f818ac7f441 Mon Sep 17 00:00:00 2001 From: aa12356jm <624848668@qq.com> Date: Wed, 7 Nov 2018 11:13:05 +0800 Subject: [PATCH 04/26] =?UTF-8?q?Update=20=E7=AC=AC=E5=8D=81=E4=BA=94?= =?UTF-8?q?=E7=AB=A0=5F=E5=BC=82=E6=9E=84=E8=BF=90=E7=AE=97=E3=80=81GPU?= =?UTF-8?q?=E5=8F=8A=E6=A1=86=E6=9E=B6=E9=80=89=E5=9E=8B.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../第十五章_异构运算、GPU及框架选型.md | 72 ++++++++++++++++--- 1 file changed, 61 insertions(+), 11 deletions(-) diff --git a/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md b/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md index fefbd09..708f81a 100644 --- a/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md +++ b/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md @@ -77,19 +77,64 @@ Nvidia有面向个人用户（例如GTX系列）和企业用户（例如Tesla系 Nvidia一般每一两年发布一次新版本的GPU，例如2017年发布的是GTX 1000系列。每个系列中会有数个不同的型号，分别对应不同的性能。 ## 15.6 软件环境搭建 -深度学习其实就是指基于一套完整的软件系统来构建算法，训练模型，如何搭建一套完整的软件系统，比如操作系统的选择？安装环境中遇到的问题等等，本节做一个简单的总结。 +深度学习其实就是指基于一套完整的软件系统来构建算法，训练模型。如何搭建一套完整的软件系统，比如操作系统的选择？安装环境中遇到的问题等等，本节做一个简单的总结。 ### 15.6.1 操作系统选择？ -针对硬件厂商来说，比如NVIDIA，对各个操作系统的支持都是比较好的 ，比如windows10,linux系列，但是由于linux系统对专业技术人员是非常友好的，所以目前几乎所有的深度学习系统构建都是基于linux的，比较常用的系统如ubuuntu系列，centos系列等等。 +针对硬件厂商来说，比如NVIDIA，对各个操作系统的支持都是比较好的 ，比如windows系列,linux系列，但是由于linux系统对专业技术人员比较友好，所以目前几乎所有的深度学习系统构建都是基于linux的，比较常用的系统如ubuuntu系列，centos系列等等。 在构建系统的时候，如何选择合适的操作系是一个刚刚入门深度学习的工作者面临的问题，在这里给出几点建议： -（1）刚刚入门，熟悉windows系统，但是对linux和深度学习都不太熟，这个时候可以基于windows10等系统来做入门学习 -（2）简单了解linux的使用，不太懂深度学习相关知识，可以直接基于linux系统来搭建框架，跑一些开源的项目，慢慢研究 -（3）熟悉linux，毫无疑问，强烈推荐使用linux系统，安装软件简单，工作效率高 +（1）刚刚入门，熟悉windows系统，但是对linux和深度学习都不太熟，这个时候可以基于windows系列系统来做入门学习 +（2）简单了解linux的使用，不太懂深度学习相关知识，可以直接基于linux系统来搭建框架，跑一些开源的项目，慢慢深入研究学习 +（3）熟悉linux，不熟悉深度学习理论，毫无疑问，强烈推荐使用linux系统，安装软件简单，工作效率高 总之一句话，如果不熟悉linux，就先慢慢熟悉，最终还是要回归到linux系统来构建深度学习系统 -### 15.6.2 本机安装还是使用docker？ +### 15.6.2 常用基础软件安装？ +目前有众多深度学习框架可供大家使用，但是所有框架基本都有一个共同的特点，目前几乎都是基于nvidia的gpu来训练模型，要想更好的使用nvidia的gpu，cuda和cudnn就是必备的软件安装。 +1，安装cuda +上文中有关于cuda的介绍，这里只是简单介绍基于linux系统安装cuda的具体步骤，可以根据自己的需要安装cuda8.0或者cuda9.0，这两种版本的安装步骤基本一致，这里以最常用的ubuntu 16.04 lts版本为例： +（1）官网下载，地址 +cuda8.0 https://developer.nvidia.com/cuda-80-ga2-download-archive +cuda9.0 https://developer.nvidia.com/cuda-90-download-archive +进入网址之后选择对应的系统版本即可，如下图所示： +![cuda8.0](./img/ch15/cuda8.0.png) +![cuda9.0](./img/ch15/cuda9.0.png) -### 15.6.3 GPU驱动问题 +（2）命令行中进入到cuda所在的位置，授予运行权限： +cuda8.0: sudo chmod +x cuda_8.0.61_375.26_linux.run +cuda9.0: sudo chmod +x cuda_9.0.176_384.81_linux.run + +（3）执行命令安装cuda： +cuda8.0: sudo sh cuda_8.0.61_375.26_linux.run +cuda9.0: sudo sh cuda_9.0.176_384.81_linux.run +之后命令之后下面就是安装步骤，cuda8.0和cuda9.0几乎一致： +1） 首先出现cuda软件的版权说明，可以直接按q键跳过阅读 + +2） Do you accept the previously read EULA? +accept/decline/quit: **accept** + +3) Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? +(y)es/(n)o/(q)uit: **no** + +4） Install the CUDA 9.0 Toolkit? +(y)es/(n)o/(q)uit: **yes** + +5) Enter Toolkit Location + [ default is /usr/local/cuda-9.0 ]: 直接按enter键即可 + +6）Do you want to install a symbolic link at /usr/local/cuda? +(y)es/(n)o/(q)uit: **yes** + +7) Install the CUDA 9.0 Samples? +(y)es/(n)o/(q)uit: **yes** + +以上步骤基本就是cuda的安装步骤。 + + +2，安装cudnn +cudnn是nvidia的专门针对深度学习的加速库。。。 + +### 15.6.3 本机安装还是使用docker？ + +### 15.6.4 GPU驱动问题 ## 15.7 框架选择 @@ -102,6 +147,13 @@ Nvidia一般每一两年发布一次新版本的GPU，例如2017年发布的是G * Tensorflow * PyTorch +pytorch是Facebook于2017年才推出的深度学习框架，相对于其它框架，算是比较晚的了，但是这个同时也是优势，在设计的时候就会避免很多之前框架的问题，所以一经推出，就收到大家极大的欢迎 +优点：接口简洁且规范，和python无缝结合，代码设计优秀且易懂，社区非常活跃，官方修复bug及时 +缺点: 目前模型在工业界部署相对其它框架稍有劣势，不过后续的pytorch1.0版本应该会有很大改善，和caffe2合并后，caffe2的优秀的模型部署能力可以弥补这个不足 +相关资源链接： +（1）官网教程：https://pytorch.org/tutorials/ +（2）基于pytorch的开源项目汇总：https://github.com/bharathgs/Awesome-pytorch-list +（3） * Keras @@ -158,7 +210,7 @@ mxnet的最知名的优点就是其对多GPU的支持和扩展性强，其优秀 * Tensorflow -* PyTorch +* PyTorch ### 15.8.2 是不是可以分布式训练？ @@ -234,9 +286,7 @@ keras是一种高层编程接口，其可以选择不同的后端，比如tensor 缺点: 封装的太好了导致不理解其技术细节 3,pytorch: -pytorch是Facebook于2017年才推出的深度学习框架，相对于其它框架，算是比较晚的了，但是这个同时也是优势，在设计的时候就会避免很多之前框架的问题，所以一经推出，就收到大家极大的欢迎 -优点：接口简洁且规范，和python无缝结合，代码设计优秀且易懂，社区非常活跃，官方修复bug及时 -缺点: 目前模型在工业界部署相对其它框架稍有劣势，不过后续的pytorch1.0版本应该会有很大改善，和caffe2合并后，caffe2的优秀的模型部署能力可以弥补这个不足 + 4,caffe2: caffe2是在caffe之后的第二代版本，同属于Facebook。。。 From 1e03a46013a2e3443927cebbaaa74fc1bcc1b9ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=8D=E8=BF=B7=E8=8C=AB=EF=BC=8C=E4=B8=8D=E7=AD=89?= =?UTF-8?q?=E5=BE=85?= Date: Wed, 7 Nov 2018 12:53:23 +0800 Subject: [PATCH 05/26] update chap9 --- ch3_深度学习基础/第三章_深度学习基础.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch3_深度学习基础/第三章_深度学习基础.md b/ch3_深度学习基础/第三章_深度学习基础.md index 93ecb41..edab6dd 100644 --- a/ch3_深度学习基础/第三章_深度学习基础.md +++ b/ch3_深度学习基础/第三章_深度学习基础.md @@ -865,7 +865,7 @@ BN比较适用的场景是：每个mini-batch比较大，数据分布比较接 经过预训练最终能得到比较好的局部最优解。 -### 3.7.2 什么是模型微调f ine tuning +### 3.7.2 什么是模型微调fine tuning 用别人的参数、修改后的网络和自己的数据进行训练，使得参数适应自己的数据，这样一个过程，通常称之为微调（fine tuning). From c95c33a0294a59e887b18d29e0307f5714d3e120 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=8D=E8=BF=B7=E8=8C=AB=EF=BC=8C=E4=B8=8D=E7=AD=89?= =?UTF-8?q?=E5=BE=85?= Date: Wed, 7 Nov 2018 12:55:25 +0800 Subject: [PATCH 06/26] update chap9 --- ch9_图像分割/modify_log.txt | 3 +++ ch9_图像分割/第九章_图像分割.md | 9 ++++++--- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/ch9_图像分割/modify_log.txt b/ch9_图像分割/modify_log.txt index 242f677..f18ca4b 100644 --- a/ch9_图像分割/modify_log.txt +++ b/ch9_图像分割/modify_log.txt @@ -28,6 +28,9 @@ modify_log---->用来记录修改日志 4. 修改或者删除部分9.9.3公式、部分说法（可讨论），增加论文链接 5. 修改或者删除部分9.9.4公式、部分说法（可讨论），增加论文链接 +<----qjhuang-2018-11-7----> +1. 修改9.5答案部分说法（可讨论） + 其他---->待增加 2. 修改readme内容 3. 修改modify内容 diff --git a/ch9_图像分割/第九章_图像分割.md b/ch9_图像分割/第九章_图像分割.md index 4a406a7..4da48eb 100644 --- a/ch9_图像分割/第九章_图像分割.md +++ b/ch9_图像分割/第九章_图像分割.md @@ -368,11 +368,14 @@ RefineNet block的作用就是把不同resolution level的feature map进行融
   -Residual convolution unit就是普通的去除了BN的residual unit； +Residual convolution unit就是普通的去除了BN的residual unit； +    -Multi-resolution fusion是先对多输入的feature map都用一个卷积层进行adaptation(都化到最小的feature map的shape)，再上采样再做element-wise的相加。注意如果是像RefineNet-4那样的单输入block这一部分就直接pass了； +Multi-resolution fusion是先对多输入的feature map都用一个卷积层进行adaptation(都化到最小的feature map的shape)，再上采样再做element-wise的相加。注意如果是像RefineNet-4那样的单输入block这一部分就直接pass了； +    -Chained residual pooling 没太看懂怎么残差相加的(其中的ReLU非常重要)，之后再修改； +Chained residual pooling中的ReLU对接下来池化的有效性很重要，还可以使模型对学习率的变化没这么敏感。这个链式结构能从很大范围区域上获取背景context。另外，这个结构中大量使用了identity mapping这样的连接，无论长距离或者短距离的，这样的结构允许梯度从一个block直接向其他任一block传播。 +    Output convolutions就是输出前再加一个RCU。 From fae564932050e24af3e313fc90f72c950b60670b Mon Sep 17 00:00:00 2001 From: aa12356jm <624848668@qq.com> Date: Wed, 7 Nov 2018 13:34:12 +0800 Subject: [PATCH 07/26] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E5=9B=BE=E7=89=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 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zLe=}aGm=>feLk$C3n?=vB zCp2Y4ob9}FzO2dHq}{x_&H1NhNNr^7kImepyAk`}DQL-=`Q6ha|CYt=z4(u~9+G#g z?Qw=#%bqPZUEHDmQrqX#$B)e(#M%69WF>vo?0*|)UW$etOD|bF+#&w0aa+HuE%SHh dBxmjD-gadOPbN2KQ~I%E`VWjDRlhq${U4J}%+&w@ literal 0 HcmV?d00001 From 25b49ab2c43bb36d6ae76d3ab5a9526bb2184c5c Mon Sep 17 00:00:00 2001 From: aa12356jm <624848668@qq.com> Date: Wed, 7 Nov 2018 13:37:04 +0800 Subject: [PATCH 08/26] =?UTF-8?q?Update=20=E7=AC=AC=E5=8D=81=E4=BA=94?= =?UTF-8?q?=E7=AB=A0=5F=E5=BC=82=E6=9E=84=E8=BF=90=E7=AE=97=E3=80=81GPU?= =?UTF-8?q?=E5=8F=8A=E6=A1=86=E6=9E=B6=E9=80=89=E5=9E=8B.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md b/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md index 708f81a..4087b88 100644 --- a/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md +++ b/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md @@ -94,8 +94,9 @@ Nvidia一般每一两年发布一次新版本的GPU，例如2017年发布的是G （1）官网下载，地址 cuda8.0 https://developer.nvidia.com/cuda-80-ga2-download-archive cuda9.0 https://developer.nvidia.com/cuda-90-download-archive -进入网址之后选择对应的系统版本即可，如下图所示： +进入网址之后选择对应的系统版本即可，如下图所示： ![cuda8.0](./img/ch15/cuda8.0.png) + ![cuda9.0](./img/ch15/cuda9.0.png) （2）命令行中进入到cuda所在的位置，授予运行权限： From bd690571eec9052ca86f88fd922bb1c41b711fed Mon Sep 17 00:00:00 2001 From: aa12356jm <624848668@qq.com> Date: Wed, 7 Nov 2018 13:42:31 +0800 Subject: [PATCH 09/26] =?UTF-8?q?Update=20=E7=AC=AC=E5=8D=81=E4=BA=94?= =?UTF-8?q?=E7=AB=A0=5F=E5=BC=82=E6=9E=84=E8=BF=90=E7=AE=97=E3=80=81GPU?= =?UTF-8?q?=E5=8F=8A=E6=A1=86=E6=9E=B6=E9=80=89=E5=9E=8B.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../第十五章_异构运算、GPU及框架选型.md | 54 ++++++++++--------- 1 file changed, 29 insertions(+), 25 deletions(-) diff --git a/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md b/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md index 4087b88..6ab5a51 100644 --- a/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md +++ b/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md @@ -99,39 +99,43 @@ cuda9.0 https://developer.nvidia.com/cuda-90-download-archive ![cuda9.0](./img/ch15/cuda9.0.png) -（2）命令行中进入到cuda所在的位置，授予运行权限： -cuda8.0: sudo chmod +x cuda_8.0.61_375.26_linux.run -cuda9.0: sudo chmod +x cuda_9.0.176_384.81_linux.run +（2）命令行中进入到cuda所在的位置，授予运行权限： +cuda8.0: sudo chmod +x cuda_8.0.61_375.26_linux.run +cuda9.0: sudo chmod +x cuda_9.0.176_384.81_linux.run -（3）执行命令安装cuda： -cuda8.0: sudo sh cuda_8.0.61_375.26_linux.run -cuda9.0: sudo sh cuda_9.0.176_384.81_linux.run -之后命令之后下面就是安装步骤，cuda8.0和cuda9.0几乎一致： -1） 首先出现cuda软件的版权说明，可以直接按q键跳过阅读 +（3）执行命令安装cuda： +cuda8.0: sudo sh cuda_8.0.61_375.26_linux.run +cuda9.0: sudo sh cuda_9.0.176_384.81_linux.run +之后命令之后下面就是安装步骤，cuda8.0和cuda9.0几乎一致： + 1） 首先出现cuda软件的版权说明，可以直接按q键跳过阅读 + -2） Do you accept the previously read EULA? -accept/decline/quit: **accept** + 2） Do you accept the previously read EULA? + accept/decline/quit: **accept** -3) Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? -(y)es/(n)o/(q)uit: **no** + 3) Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? + (y)es/(n)o/(q)uit: **no** + + + 4） Install the CUDA 9.0 Toolkit? + (y)es/(n)o/(q)uit: **yes** + -4） Install the CUDA 9.0 Toolkit? -(y)es/(n)o/(q)uit: **yes** + 5) Enter Toolkit Location + [ default is /usr/local/cuda-9.0 ]: 直接按enter键即可 -5) Enter Toolkit Location - [ default is /usr/local/cuda-9.0 ]: 直接按enter键即可 + 6）Do you want to install a symbolic link at /usr/local/cuda? + (y)es/(n)o/(q)uit: **yes** -6）Do you want to install a symbolic link at /usr/local/cuda? -(y)es/(n)o/(q)uit: **yes** - -7) Install the CUDA 9.0 Samples? -(y)es/(n)o/(q)uit: **yes** - -以上步骤基本就是cuda的安装步骤。 + 7) Install the CUDA 9.0 Samples? + (y)es/(n)o/(q)uit: **yes** + +以上步骤基本就是cuda的安装步骤。 -2，安装cudnn -cudnn是nvidia的专门针对深度学习的加速库。。。 +2，安装cudnn +cudnn是nvidia的专门针对深度学习的加速库。。。 + ### 15.6.3 本机安装还是使用docker？ From 3bbae33e57aa611e0c543a52d1baff8390231304 Mon Sep 17 00:00:00 2001 From: aa12356jm <624848668@qq.com> Date: Wed, 7 Nov 2018 13:44:11 +0800 Subject: [PATCH 10/26] =?UTF-8?q?Update=20=E7=AC=AC=E5=8D=81=E4=BA=94?= =?UTF-8?q?=E7=AB=A0=5F=E5=BC=82=E6=9E=84=E8=BF=90=E7=AE=97=E3=80=81GPU?= =?UTF-8?q?=E5=8F=8A=E6=A1=86=E6=9E=B6=E9=80=89=E5=9E=8B.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../第十五章_异构运算、GPU及框架选型.md | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md b/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md index 6ab5a51..c248391 100644 --- a/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md +++ b/ch15_GPU和框架选型/第十五章_异构运算、GPU及框架选型.md @@ -113,21 +113,19 @@ cuda9.0: sudo sh cuda_9.0.176_384.81_linux.run 2） Do you accept the previously read EULA? accept/decline/quit: **accept** - 3) Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? + 3） Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81? (y)es/(n)o/(q)uit: **no** - - + 4） Install the CUDA 9.0 Toolkit? (y)es/(n)o/(q)uit: **yes** - - 5) Enter Toolkit Location + 5） Enter Toolkit Location [ default is /usr/local/cuda-9.0 ]: 直接按enter键即可 - 6）Do you want to install a symbolic link at /usr/local/cuda? - (y)es/(n)o/(q)uit: **yes** + 6） Do you want to install a symbolic link at /usr/local/cuda? + (y)es/(n)o/(q)uit: **yes** - 7) Install the CUDA 9.0 Samples? + 7） Install the CUDA 9.0 Samples? (y)es/(n)o/(q)uit: **yes** 以上步骤基本就是cuda的安装步骤。 From 48ee3a3cd8e726d5ca6c13102425164d19c05a39 Mon Sep 17 00:00:00 2001 From: Lixiaodan Date: Tue, 6 Nov 2018 21:48:11 -0800 Subject: [PATCH 12/26] chapter for rnns has been modified carefully. --- .../第六章_循环神经网络(RNN).md | 100 +++++++++++------- 1 file changed, 60 insertions(+), 40 deletions(-) diff --git a/ch6_循环神经网络(RNN)/第六章_循环神经网络(RNN).md b/ch6_循环神经网络(RNN)/第六章_循环神经网络(RNN).md index 33675c7..c301cff 100644 --- a/ch6_循环神经网络(RNN)/第六章_循环神经网络(RNN).md +++ b/ch6_循环神经网络(RNN)/第六章_循环神经网络(RNN).md @@ -1,5 +1,10 @@ # 第六章 循环神经网络(RNN) + Markdown Revision 2; + Date: 2018/11/04 + Editor: 李骁丹-杜克大学 + Contact: xiaodan.li@duke.edu + Markdown Revision 1; Date: 2018/10/26 Editor: 杨国峰-中国农业科学院 @@ -9,14 +14,17 @@ http://blog.csdn.net/heyongluoyao8/article/details/48636251 ## 6.1 RNNs和FNNs有什么区别？ -不同于传统的前馈神经网络(FNNs)，RNNs引入了定向循环，能够处理那些输入之间前后关联的问题。**定向循环结构如下图所示**： +1. 不同于传统的前馈神经网络(FNNs)，RNNs引入了定向循环，能够处理输入之间前后关联问题。 +2. RNNs可以记忆之前步骤的训练信息。 +**定向循环结构如下图所示**： ![](./img/ch6/figure_6.1_1.jpg) ## 6.2 RNNs典型特点？ -RNNs的目的使用来处理序列数据。在传统的神经网络模型中，是从输入层到隐含层再到输出层，层与层之间是全连接的，每层之间的节点是无连接的。但是这种普通的神经网络对于很多问题却无能无力。例如，你要预测句子的下一个单词是什么，一般需要用到前面的单词，因为一个句子中前后单词并不是独立的。 -RNNs之所以称为循环神经网路，即一个序列当前的输出与前面的输出也有关。具体的表现形式为网络会对前面的信息进行记忆并应用于当前输出的计算中，即隐藏层之间的节点不再无连接而是有连接的，并且隐藏层的输入不仅包括输入层的输出还包括上一时刻隐藏层的输出。理论上，RNNs能够对任何长度的序列数据进行处理。但是在实践中，为了降低复杂性往往假设当前的状态只与前面的几个状态相关，**下图便是一个典型的RNNs**： +1. RNNs主要用于处理序列数据。对于传统神经网络模型，从输入层到隐含层再到输出层，层与层之间一般为全连接，每层之间神经元是无连接的。但是传统神经网络无法处理数据间的前后关联问题。例如，为了预测句子的下一个单词，一般需要该词之前的语义信息，因为一个句子中前后单词是存在语义联系的。 +2. RNNs中当前单元的输出与上一步输出也有关，因此称之为循环神经网络。具体的表现形式为当前单元（cell）会对上一步的信息进行储存并应用于当前输出的计算中。隐藏层之间的节点连接起来，并且隐藏层当前输出由当前时刻输入向量和上一时刻隐藏层状态共同决定。 +3. 理论上，RNNs能够对任何长度的序列数据进行处理。但是在实践中，为了降低复杂度往往假设当前的状态只与上一时刻状态相关，**下图便是一个典型的RNNs**： ![](./img/ch6/figure_6.2_1.png) @@ -30,21 +38,22 @@ RNNs之所以称为循环神经网路，即一个序列当前的输出与前面 **图中信息传递特点：** -1. 有一条单向流动的信息流是从输入单元到达隐藏单元； -2. 与此同时，另一条单向流动的信息流从隐藏单元到达输出单元； -3. 在某些情况下，RNNs会打破后者的限制，引导信息从输出单元返回隐藏单元，这些被称为“Back Projections”； -4. 在某些情况下，隐藏层的输入还包括上一隐藏层的状态，即隐藏层内的节点可以自连也可以互连。 +1. 一条单向流动的信息流是从输入单元到隐藏单元。 +2. 一条单向流动的信息流从隐藏单元到输出单元。 +3. 在某些情况下，RNNs会打破后者的限制，引导信息从输出单元返回隐藏单元，这些被称为“Back Projections”。 +4. 在某些情况下，隐藏层的输入还包括上一时刻隐藏层的状态，即隐藏层内的节点可以自连也可以互连。 +5. 当前单元（cell）输出是由当前时刻输入和上一时刻隐藏层状态共同决定。 -## 6.3RNNs能干什么？ +## 6.3 RNNs能干什么？ -RNNs已经被在实践中证明对NLP是非常成功的。如词向量表达、语句合法性检查、词性标注等。在RNNs中，目前使用最广泛最成功的模型便是LSTMs(Long Short-Term Memory，长短时记忆模型)模型，该模型通常比 vanilla RNNs 能够更好地对长短时依赖进行表达，该模型相对于一般的RNNs，只是在隐藏层做了改变。 +RNNs在自然语言处理领域取得了巨大成功，如词向量表达、语句合法性检查、词性标注等。在RNNs及其变型中，目前使用最广泛最成功的模型是LSTMs(Long Short-Term Memory，长短时记忆模型)模型，该模型相比于RNNs，能够更好地对长短时依赖进行描述。 ## 6.4 RNNs在NLP中典型应用？ **（1）语言模型与文本生成(Language Modeling and Generating Text)** -给一个单词序列，需要根据前面的单词预测每一个单词的可能性。语言模型能够一个语句正确的可能性，这是机器翻译的一部分，往往可能性越大，语句越正确。另一种应用便是使用生成模型预测下一个单词的概率，从而生成新的文本根据输出概率的采样。 +给定一组单词序列，需要根据前面单词预测每个单词出现的可能性。语言模型能够评估某个语句正确的可能性，可能性越大，语句越正确。另一种应用便是使用生成模型预测下一个单词的出现概率，从而利用输出概率的采样生成新的文本。 **（2）机器翻译(Machine Translation)** @@ -52,74 +61,85 @@ RNNs已经被在实践中证明对NLP是非常成功的。如词向量表达、 **（3）语音识别(Speech Recognition)** -语音识别是指给一段声波的声音信号，预测该声波对应的某种指定源语言的语句以及该语句的概率值。 +语音识别是指给定一段声波的声音信号，预测该声波对应的某种指定源语言语句以及计算该语句的概率值。 **（4）图像描述生成 (Generating Image Descriptions)** -和卷积神经网络(convolutional Neural Networks, CNNs)一样，RNNs已经在对无标图像描述自动生成中得到应用。将CNNs与RNNs结合进行图像描述自动生成。 +同卷积神经网络(convolutional Neural Networks, CNNs)一样，RNNs已经在对无标图像描述自动生成中得到应用。CNNs与RNNs结合也被应用于图像描述自动生成。 ![](./img/ch6/figure_6.4_1.png) ## 6.5 RNNs训练和传统ANN训练异同点？ -**相同点**：同样使用BP误差反向传播算法。 +**相同点**： +1. RNNs与传统ANN都使用BP（Back Propagation）误差反向传播算法。 **不同点**： -1. 如果将RNNs进行网络展开，那么参数W,U,V是共享的，而传统神经网络却不是的。 -2. 在使用梯度下降算法中，每一步的输出不仅依赖当前步的网络，并且还以来前面若干步网络的状态。 +1. RNNs网络参数W,U,V是共享的，而传统神经网络各层参数间没有直接联系。 +2. 对于RNNs，在使用梯度下降算法中，每一步的输出不仅依赖当前步的网络，还依赖于之前若干步的网络状态。 -## 6.6常见的RNNs扩展和改进模型 +## 6.6 常见的RNNs扩展和改进模型 -### 6.6.1Simple RNNs(SRNs) +### 6.6.1 Simple RNNs(SRNs) -SRNs是RNNs的一种特例，它是一个三层网络，并且在隐藏层增加了上下文单元，下图中的**y**便是隐藏层，**u**便是上下文单元。上下文单元节点与隐藏层中的节点的连接是固定(谁与谁连接)的，并且权值也是固定的(值是多少)，其实是一个上下文节点与隐藏层节点一一对应，并且值是确定的。在每一步中，使用标准的前向反馈进行传播，然后使用学习算法进行学习。上下文每一个节点保存其连接的隐藏层节点的上一步的输出，即保存上文，并作用于当前步对应的隐藏层节点的状态，即隐藏层的输入由输入层的输出与上一步的自己的状态所决定的。因此SRNs能够解决标准的多层感知机(MLP)无法解决的对序列数据进行预测的任务。 +1. SRNs是RNNs的一种特例，它是一个三层网络，其在隐藏层增加了上下文单元。下图中的**y**是隐藏层，**u**是上下文单元。上下文单元节点与隐藏层中节点的连接是固定的，并且权值也是固定的。上下文节点与隐藏层节点一一对应，并且值是确定的。 +2. 在每一步中，使用标准的前向反馈进行传播，然后使用学习算法进行学习。上下文每一个节点保存其连接隐藏层节点上一步输出，即保存上文，并作用于当前步对应的隐藏层节点状态，即隐藏层的输入由输入层的输出与上一步的自身状态所决定。因此SRNs能够解决标准多层感知机(MLP)无法解决的对序列数据进行预测的问题。 **SRNs网络结构如下图所示**： ![](./img/ch6/figure_6.6.1_1.png) -### 6.6.2Bidirectional RNNs +### 6.6.2 Bidirectional RNNs -Bidirectional RNNs(双向网络)的改进之处便是，假设当前的输出(第t步的输出)不仅仅与前面的序列有关，并且还与后面的序列有关。例如：预测一个语句中缺失的词语那么就需要根据上下文来进行预测。Bidirectional RNNs是一个相对较简单的RNNs，是由两个RNNs上下叠加在一起组成的。输出由这两个RNNs的隐藏层的状态决定的。**如下图所示**： +Bidirectional RNNs(双向网络)将两层RNNs叠加在一起，当前时刻输出(第t步的输出)不仅仅与之前序列有关，还与之后序列有关。例如：为了预测一个语句中的缺失词语，就需要该词汇的上下文信息。Bidirectional RNNs是一个相对较简单的RNNs，是由两个RNNs上下叠加在一起组成的。输出由前向RNNs和后向RNNs共同决定。**如下图所示**： ![](./img/ch6/figure_6.6.2_1.png) -### 6.6.3Deep(Bidirectional) RNNs +### 6.6.3 Deep RNNs -Deep(Bidirectional)RNNs与Bidirectional RNNs相似，只是对于每一步的输入有多层网络。这样，该网络便有更强大的表达与学习能力，但是复杂性也提高了，同时需要更多的训练数据。**Deep(Bidirectional)RNNs的结构如下图所示**： +Deep RNNs与Bidirectional RNNs相似，其也是又多层RNNs叠加，因此每一步的输入有了多层网络。该网络具有更强大的表达与学习能力，但是复杂性也随之提高，同时需要更多的训练数据。**Deep RNNs的结构如下图所示**： ![](./img/ch6/figure_6.6.3_1.png) -### 6.6.4Echo State Networks（ESNs） -ESNs(回声状态网络)虽然也是一种RNNs，但是它与传统的RNNs相差很大。 +### 6.6.4 Echo State Networks（ESNs） +ESNs(回声状态网络)虽然也是一种RNNs，但它与传统的RNNs相差较大。 **ESNs具有三个特点**： -（1）它的核心结构时一个随机生成、且保持不变的储备池(Reservoir)，储备池是大规模的、随机生成的、稀疏连接(SD通常保持1%～5%，SD表示储备池中互相连接的神经元占总的神经元个数N的比例)的循环结构； +1. 它的核心结构为一个随机生成、且保持不变的储备池(Reservoir)。储备池是大规模随机生成稀疏连接(SD通常保持1%～5%，SD表示储备池中互相连接的神经元占总神经元个数N的比例)的循环结构； -（2）其储备池到输出层的权值矩阵是唯一需要调整的部分； +2. 从储备池到输出层的权值矩阵是唯一需要调整的部分； -（3）简单的线性回归就可完成网络的训练。 +3. 简单的线性回归便能够完成网络训练； -从结构上讲，ESNs是一种特殊类型的循环神经网络，其基本思想是：使用大规模随机连接的循环网络取代经典神经网络中的中间层，从而简化网络的训练过程。因此ESNs的关键是中间的储备池。网络中的参数包括：W为储备池中节点的连接权值矩阵，Win为输入层到储备池之间的连接权值矩阵，表明储备池中的神经元之间是连接的，Wback为输出层到储备池之间的反馈连接权值矩阵，表明储备池会有输出层来的反馈，Wout为输入层、储备池、输出层到输出层的连接权值矩阵，表明输出层不仅与储备池连接，还与输入层和自己连接。Woutbias表示输出层的偏置项。 -对于ESNs，关键是储备池的四个参数，如储备池内部连接权谱半径SR(SR=λmax=max{|W的特征指|}，只有SR <1时，ESNs才能具有回声状态属性)、储备池规模N(即储备池中神经元的个数)、储备池输入单元尺度IS(IS为储备池的输入信号连接到储备池内部神经元之前需要相乘的一个尺度因子)、储备池稀疏程度SD(即为储备池中互相连接的神经元个数占储备池神经元总个数的比例)。对于IS，如果需要处理的任务的非线性越强，那么输入单元尺度越大。该原则的本质就是通过输入单元尺度IS，将输入变换到神经元激活函数相应的范围(神经元激活函数的不同输入范围，其非线性程度不同)。 +从结构上讲，ESNs是一种特殊类型的循环神经网络，其基本思想是：使用大规模随机连接的循环网络取代经典神经网络中的中间层，从而简化网络的训练过程。因此ESNs的关键是储备池。 +网络中的参数包括： +W - 储备池中节点间连接权值矩阵； +Win - 输入层到储备池之间连接权值矩阵，表明储备池中的神经元之间是相互连接； +Wback - 输出层到储备池之间的反馈连接权值矩阵，表明储备池会有输出层来的反馈； +Wout - 输入层、储备池、输出层到输出层的连接权值矩阵，表明输出层不仅与储备池连接，还与输入层和自己连接。 +Woutbias - 输出层的偏置项。 +对于ESNs，关键是储备池的四个参数，如储备池内部连接权谱半径SR(SR=λmax=max{|W的特征指|}，只有SR <1时，ESNs才能具有回声状态属性)、储备池规模N(即储备池中神经元的个数)、储备池输入单元尺度IS(IS为储备池的输入信号连接到储备池内部神经元之前需要相乘的一个尺度因子)、储备池稀疏程度SD(即为储备池中互相连接的神经元个数占储备池神经元总个数的比例)。对于IS，待处理任务的非线性越强，输入单元尺度越大。该原则本质就是通过输入单元尺度IS，将输入变换到神经元激活函数相应的范围(神经元激活函数的不同输入范围，其非线性程度不同)。 **ESNs的结构如下图所示**： ![](./img/ch6/figure_6.6.4_1.png) ![](./img/ch6/figure_6.6.4_2.png) ![](./img/ch6/figure_6.6.4_3.png) -### 6.6.5Gated Recurrent Unit Recurrent Neural Networks -GRUs也是一般的RNNs的改良版本，主要是从以下**两个方面**进行改进。 +### 6.6.5 Gated Recurrent Unit Recurrent Neural Networks +GRUs是一般的RNNs的变型版本，其主要是从以下**两个方面**进行改进。 -**一是**，序列中不同的位置处的单词(已单词举例)对当前的隐藏层的状态的影响不同，越前面的影响越小，即每个前面状态对当前的影响进行了距离加权，距离越远，权值越小。 +**一是**，序列中不同单词处（以语句为例）的数据对当前隐藏层状态的影响不同，越前面的影响越小，即每个之前状态对当前的影响进行了距离加权，距离越远，权值越小。 -**二是**，在产生误差error时，误差可能是由某一个或者几个单词而引发的，所以应当仅仅对对应的单词weight进行更新。GRUs的结构如下图所示。GRUs首先根据当前输入单词向量word vector已经前一个隐藏层的状态hidden state计算出update gate和reset gate。再根据reset gate、当前word vector以及前一个hidden state计算新的记忆单元内容(new memory content)。当reset gate为1的时候，new memory content忽略之前的所有memory content，最终的memory是之前的hidden state与new memory content的结合。 +**二是**，在产生误差error时，其可能是由之前某一个或者几个单词共同造成，所以应当对对应的单词weight进行更新。GRUs的结构如下图所示。GRUs首先根据当前输入单词向量word vector以及前一个隐藏层状态hidden state计算出update gate和reset gate。再根据reset gate、当前word vector以及前一个hidden state计算新的记忆单元内容(new memory content)。当reset gate为1的时候，new memory content忽略之前所有memory content，最终的memory是由之前的hidden state与new memory content一起决定。 ![](./img/ch6/figure_6.6.5_1.png) -### 6.6.6LSTM Netwoorks -LSTMs与GRUs类似，目前非常流行。它与一般的RNNs结构本质上并没有什么不同，只是使用了不同的函数去去计算隐藏层的状态。在LSTMs中，i结构被称为cells，可以把cells看作是黑盒用以保存当前输入xt之前的保存的状态ht−1，这些cells更加一定的条件决定哪些cell抑制哪些cell兴奋。它们结合前面的状态、当前的记忆与当前的输入。已经证明，该网络结构在对长序列依赖问题中非常有效。**LSTMs的网络结构如下图所示**。 +### 6.6.6 LSTM Netwoorks +1. LSTMs是当前一种非常流行的深度学习模型。为了解决RNNs存在的长时记忆问题，LSTMs利用了之前更多步的训练信息。 +2. LSTMs与一般的RNNs结构本质上并没有太大区别，只是使用了不同函数控制隐藏层的状态。 +3. 在LSTMs中，基本结构被称为cell，可以把cell看作是黑盒用以保存当前输入之前Xt的隐藏层状态ht−1。 +4. LSTMs有三种类型的门：遗忘门（forget gate）, 输入门（input gate）以及输出门（output gate）。遗忘门（forget gate）是用来决定 哪个cells的状态将被丢弃掉。输入门（input gate）决定哪些cells会被更新. 输出门（output gate）控制了结果输出. 因此当前输出依赖于cells状态以及门的过滤条件。实践证明，LSTMs可以有效地解决长序列依赖问题。**LSTMs的网络结构如下图所示**。 ![](./img/ch6/figure_6.6.6_1.png) @@ -127,15 +147,15 @@ LSTMs与GRUs的区别如图所示： ![](./img/ch6/figure_6.6.6_2.png) -从上图可以看出，它们之间非常相像，**不同在于**： +从上图可以看出，二者结构十分相似，**不同在于**： -（1）new memory的计算方法都是根据之前的state及input进行计算，但是GRUs中有一个reset gate控制之前state的进入量，而在LSTMs里没有这个gate； +（1）new memory都是根据之前state及input进行计算，但是GRUs中有一个reset gate控制之前state的进入量，而在LSTMs里没有类似gate； -（2）产生新的state的方式不同，LSTMs有两个不同的gate，分别是forget gate (f gate)和input gate(i gate)，而GRUs只有一个update gate(z gate)； +（2）产生新的state的方式不同，LSTMs有两个不同的gate，分别是forget gate (f gate)和input gate(i gate)，而GRUs只有一种update gate(z gate)； -（3）LSTMs对新产生的state又一个output gate(o gate)可以调节大小，而GRUs直接输出无任何调节。 +（3）LSTMs对新产生的state可以通过output gate(o gate)进行调节，而GRUs对输出无任何调节。 -### 6.6.7Clockwork RNNs(CW-RNNs) +### 6.6.7 Clockwork RNNs(CW-RNNs) CW-RNNs是较新的一种RNNs模型，其论文发表于2014年Beijing ICML。 CW-RNNs也是一个RNNs的改良版本，是一种使用时钟频率来驱动的RNNs。它将隐藏层分为几个块(组，Group/Module)，每一组按照自己规定的时钟频率对输入进行处理。并且为了降低标准的RNNs的复杂性，CW-RNNs减少了参数的数目，提高了网络性能，加速了网络的训练。CW-RNNs通过不同的隐藏层模块工作在不同的时钟频率下来解决长时间依赖问题。将时钟时间进行离散化，然后在不同的时间点，不同的隐藏层组在工作。因此，所有的隐藏层组在每一步不会都同时工作，这样便会加快网络的训练。并且，时钟周期小的组的神经元的不会连接到时钟周期大的组的神经元，只会周期大的连接到周期小的(认为组与组之间的连接是有向的就好了，代表信息的传递是有向的)，周期大的速度慢，周期小的速度快，那么便是速度慢的连速度快的，反之则不成立。现在还不明白不要紧，下面会进行讲解。 From 6c852fd974056296116b076ecd2129923efb0ef5 Mon Sep 17 00:00:00 2001 From: Limber Cheng Date: Wed, 7 Nov 2018 21:50:27 +0800 Subject: [PATCH 13/26] :sparkles: Fix some errors, add "Can GAN do ..." --- .../第七章_生成对抗网络(GAN).md | 28 ++++++++++++++----- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/ch7_生成对抗网络(GAN)/第七章_生成对抗网络(GAN).md b/ch7_生成对抗网络(GAN)/第七章_生成对抗网络(GAN).md index b032d5b..0fd800a 100644 --- a/ch7_生成对抗网络(GAN)/第七章_生成对抗网络(GAN).md +++ b/ch7_生成对抗网络(GAN)/第七章_生成对抗网络(GAN).md @@ -1,15 +1,19 @@ -# 第七章 生成对抗网络(GAN) +# 第七章_生成对抗网络(GAN) -## GAN生成概率的本质是什么？ +## GAN的「生成」的本质是什么？ GAN的形式是：两个网络，G（Generator）和D（Discriminator）。Generator是一个生成图片的网络，它接收一个随机的噪声z，记做G(z)。Discriminator是一个判别网络，判别一张图片是不是“真实的”。它的输入是x，x代表一张图片，输出D（x）代表x为真实图片的概率，如果为1，就代表100%是真实的图片，而输出为0，就代表不可能是真实的图片。 -GAN*生成*能力是学习*分布*，引入的latent variable的noise使习得的概率分布进行偏移。因此在训练GAN的时候，latent variable**不能**引入均匀分布（uniform distribution)，因为均匀分布的数据的引入并不会改变概率分布。 +GAN*生成*能力是*学习分布*，引入的latent variable的noise使习得的概率分布进行偏移。因此在训练GAN的时候，latent variable**不能**引入均匀分布（uniform distribution)，因为均匀分布的数据的引入并不会改变概率分布。 + +## GAN能做数据增广吗？ +GAN能够从一个模型引入一个随机数之后「生成」无限的output，用GAN来做数据增广似乎很有吸引力并且是一个极清晰的一个insight。然而，纵观整个GAN的训练过程，Generator习得分布再引入一个Distribution(Gaussian或其他)的噪声以「骗过」Discriminator，并且无论是KL Divergence或是Wasserstein Divergence，本质还是信息衡量的手段（在本章中其余部分介绍），能「骗过」Discriminator的Generator一定是能在引入一个Distribution的噪声的情况下最好的结合已有信息。 + +训练好的GAN应该能够很好的使用已有的数据的信息（特征或分布），现在问题来了，这些信息本来就包含在数据里面，有必要把信息丢到Generator学习使得的结果加上噪声作为训练模型的输入吗？ ## VAE与GAN有什么不同？ 1. VAE可以直接用在离散型数据。 2. VAE整个训练流程只靠一个假设的loss函数和KL Divergence逼近真实分布。GAN没有假设单个loss函数, 而是让判别器D和生成器G互相博弈，以期得到Nash Equilibrium。 - ## 有哪些优秀的GAN？ ### DCGAN @@ -22,7 +26,7 @@ WGAN及其延伸是被讨论的最多的部分，原文连发两文，第一篇( **KL/JS Divergence为什么不好用？Wasserstein Divergence牛逼在哪里？** -**KL Divergence**是两个概率分布P和Q差别的**非对称性**的度量。KL Divergence是用来度量使用基于Q的编码来编码来自P的样本平均所需的额外的位元数。 而**JS Divergence**是KL Divergence的升级版，解决的是**对称性**的问题。即：JS Divergence是对称的。 +**KL Divergence**是两个概率分布P和Q差别的**非对称性**的度量。KL Divergence是用来度量使用基于Q的编码来编码来自P的样本平均所需的额外的位元数（即分布的平移量）。 而**JS Divergence**是KL Divergence的升级版，解决的是**对称性**的问题。即：JS Divergence是对称的。并且由于KL Divergence不具有很好的对称性，将KL Divergence考虑成距离可能是站不住脚的，并且可以由KL Divergence的公式中看出来，平移量$\to 0$的时候，KL Divergence直接炸了。 KL Divergence: $$D_{KL}(P||Q)=-\sum_{x\in X}P(x) log\frac{1}{P(x)}+\sum_{x\in X}p(x)log\frac{1}{Q(x)}=\sum_{x\in X}p(x)log\frac{P(x)}{Q(x)}$$ @@ -40,9 +44,14 @@ $$JS(P_1||P_2)=\frac{1}{2}KL(P_1||\frac{P_1+P_2}{2})$$ 3. 对更新后的权重强制截断到一定范围内，比如[-0.01，0.01]，以满足lipschitz连续性条件。 4. 论文中也推荐使用SGD，RMSprop等优化器，不要基于使用动量的优化算法，比如adam。 -然而，就实际而言，最优的选择其实应该是 +然而，由于D和G其实是各自有一个loss的，G和D是可以**用不同的优化器**的。个人认为Best Practice是G用SGD或RMSprop，而D用Adam。 -**如何理解Wass距离？** +很期待未来有专门针对寻找均衡态的优化方法。 + +**WGAN-GP的改进有哪些？** + +**如何理解Wasserstein距离？** +Wasserstein距离与optimal transport有一些关系，并且从数学上想很好的理解需要一定的测度论的知识。 ### condition GAN @@ -58,6 +67,9 @@ $$L^{infoGAN}_{G}=L^{GAN}_G-\lambda L_1(c,c')$$ ### StarGAN 目前Image-to-Image Translation做的最好的GAN。 + +## Self-Attention GAN + ## GAN训练有什么难点？ 由于GAN的收敛要求**两个网络（D&G）同时达到一个均衡**， @@ -71,6 +83,8 @@ GAN是一种半监督学习模型，对训练集不需要太多有标签的数 Instance Norm比Batch Norm的效果要更好。 +使用逆卷积来生成图片会比用全连接层效果好，全连接层会有较多的噪点，逆卷积层效果清晰。 + ## GAN如何解决NLP问题 GAN只适用于连续型数据的生成，对于离散型数据效果不佳，因此假如NLP方法直接应用的是character-wise的方案，Gradient based的GAN是无法将梯度Back propagation（BP）给生成网络的，因此从训练结果上看，GAN中G的表现长期被D压着打。 From a299f66d946b317f6219ed467d3e129a66e5073b Mon Sep 17 00:00:00 2001 From: guoxiaofeng Date: Wed, 7 Nov 2018 22:24:49 +0800 Subject: [PATCH 14/26] add chapter7.1.1 --- ch7_生成对抗网络(GAN)/7.1.1_what_gan.md | 38 ++++++++++++++++++ .../img/ch7/7.1-gan_structure.png | Bin 0 -> 213503 bytes 2 files changed, 38 insertions(+) create mode 100644 ch7_生成对抗网络(GAN)/7.1.1_what_gan.md create mode 100644 ch7_生成对抗网络(GAN)/img/ch7/7.1-gan_structure.png diff --git a/ch7_生成对抗网络(GAN)/7.1.1_what_gan.md b/ch7_生成对抗网络(GAN)/7.1.1_what_gan.md new file mode 100644 index 0000000..6972a08 --- /dev/null +++ b/ch7_生成对抗网络(GAN)/7.1.1_what_gan.md @@ -0,0 +1,38 @@ +# 什么是生成对抗网络 +## GAN的通俗化介绍 + +生成对抗网络(GAN, Generative adversarial network)自从2014年被Ian Goodfellow提出以来，掀起来了一股研究热潮。GAN由生成器和判别器组成，生成器负责生成样本，判别器负责判断生成器生成的样本是否为真。生成器要尽可能迷惑判别器，而判别器要尽可能区分生成器生成的样本和真实样本。 + +在GAN的原作[1]中，作者将生成器比喻为印假钞票的犯罪分子，判别器则类比为警察。犯罪分子努力让钞票看起来逼真，警察则不断提升对于假钞的辨识能力。二者互相博弈，随着时间的进行，都会越来越强。 + +# GAN的形式化表达 +上述例子只是简要介绍了一下GAN的思想，下面对于GAN做一个形式化的，更加具体的定义。通常情况下，无论是生成器还是判别器，我们都可以用神经网络来实现。那么，我们可以把通俗化的定义用下面这个模型来表示： +![GAN网络结构](/images/7.1-gan_structure.png) + +上述模型左边是生成器G，其输入是$$z$$，对于原始的GAN，$$z$$是由高斯分布随机采样得到的噪声。噪声$$z$$通过生成器得到了生成的假样本。 + +生成的假样本与真实样本放到一起，被随机抽取送入到判别器D，由判别器去区分输入的样本是生成的假样本还是真实的样本。整个过程简单明了，生成对抗网络中的“生成对抗”主要体现在生成器和判别器之间的对抗。 + +# GAN的目标函数 +对于上述神经网络模型，如果想要学习其参数，首先需要一个目标函数。GAN的目标函数定义如下： + +$$\mathop {\min }\limits_G \mathop {\max }\limits_D V(D,G) = {{\rm E}_{x\sim{p_{data}}(x)}}[\log D(x)] + {{\rm E}_{z\sim{p_z}(z)}}[\log (1 - D(G(z)))]$$ + +这个目标函数可以分为两个部分来理解： + +判别器的优化通过$$\mathop {\max}\limits_D V(D,G)$$实现，$$V(D,G)$$为判别器的目标函数，其第一项$${{\rm E}_{x\sim{p_{data}}(x)}}[\log D(x)]$$表示对于从真实数据分布 中采用的样本 ,其被判别器判定为真实样本概率的数学期望。对于真实数据分布 中采样的样本，其预测为正样本的概率当然是越接近1越好。因此希望最大化这一项。第二项$${{\rm E}_{z\sim{p_z}(z)}}[\log (1 - D(G(z)))]$$表示：对于从噪声P_z(z)分布当中采样得到的样本经过生成器生成之后得到的生成图片，然后送入判别器，其预测概率的负对数的期望，这个值自然是越大越好，这个值越大， 越接近0，也就代表判别器越好。 + +生成器的优化通过$$\mathop {\min }\limits_G({\mathop {\max }\limits_D V(D,G)})$$实现。注意，生成器的目标不是$$\mathop {\min }\limits_GV(D,G)$$，即生成器**不是最小化判别器的目标函数**，生成器最小化的是**判别器目标函数的最大值**，判别器目标函数的最大值代表的是真实数据分布与生成数据分布的JS散度(详情可以参阅附录的推导)，JS散度可以度量分布的相似性，两个分布越接近，JS散度越小。 + + +# GAN的目标函数和交叉熵 +判别器目标函数写成离散形式即为$$V(D,G)=-\frac{1}{m}\sum_{i=1}^{i=m}logD(x^i)-\frac{1}{m}\sum_{i=1}^{i=m}log(1-D(\tilde{x}^i))$$ +可以看出，这个目标函数和交叉熵是一致的，即**判别器的目标是最小化交叉熵损失，生成器的目标是最小化生成数据分布和真实数据分布的JS散度** + + + + + +------------------- +[1]: Goodfellow, Ian, et al. "Generative adversarial nets." 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@@ -630,6 +629,7 @@ UC Berkeley的Deepak Pathak使用了一个具有图像级别标记的训练数    该方法把训练过程看作是有线性限制条件的最优化过程： + $$ \underset{\theta ,P}{minimize}\qquad D(P(X)||Q(X|\theta ))\\ From b45df0c5eed9f8ba01845fffd0c887975bfb6307 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=8D=E8=BF=B7=E8=8C=AB=EF=BC=8C=E4=B8=8D=E7=AD=89?= =?UTF-8?q?=E5=BE=85?= Date: Fri, 9 Nov 2018 19:03:11 +0800 Subject: [PATCH 24/26] little mmodify --- ch9_图像分割/第九章_图像分割.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ch9_图像分割/第九章_图像分割.md b/ch9_图像分割/第九章_图像分割.md index 9636d82..e97ba8d 100644 --- a/ch9_图像分割/第九章_图像分割.md +++ b/ch9_图像分割/第九章_图像分割.md @@ -603,12 +603,14 @@ Why K个mask？通过对每个 Class 对应一个Mask可以有效避免类间竞    ScribbleSup分为两步，第一步将像素的类别信息从scribbles传播到其他未标记的像素，自动完成所有的训练图像的标记工作； 第二步使用这些标记图像训练CNN。在第一步中，该方法先生成super-pxels, 然后基于graph cut的方法对所有的super-pixel进行标记。
+    Graph Cut的能量函数为： $$ \sum_{i}\psi _i\left(y_i|X,S\right)+\sum_{i,j}\psi_{ij}\left(y_i,y_j,X\right) $$ +    在这个graph中，每个super-pixel是graph中的一个节点，相接壤的super-pixel之间有一条连接的边。这个能量函数中的一元项包括两种情况，一个是来自于scribble的，一个是来自CNN对该super-pixel预测的概率。整个最优化过程实际上是求graph cut能量函数和CNN参数联合最优值的过程： @@ -632,9 +634,8 @@ UC Berkeley的Deepak Pathak使用了一个具有图像级别标记的训练数 $$ \underset{\theta ,P}{minimize}\qquad D(P(X)||Q(X|\theta ))\\ - subject\to\qquad A\overrightarrow{P}\geqslant\overrightarrow{b},\sum_{X}^{ }P(X)=1 -$$ +$$    其中的线性限制条件来自于训练数据上的标记，例如一幅图像中前景类别像素个数期望值的上界或者下界（物体大小）、某个类别的像素个数在某图像中为0，或者至少为1等。该目标函数可以转化为为一个loss function，然后通过SGD进行训练。 @@ -667,7 +668,7 @@ $$ 论文地址：[Learning to Segment Under Various Forms of Weak Supervision (CVPR 2015)](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Xu_Learning_to_Segment_2015_CVPR_paper.pdf)    -Wisconsin-Madison大学的Jia Xu提出了一个统一的框架来处理各种不同类型的弱标记：图像级别的标记、bounding box和部分像素标记如scribbles。该方法把所有的训练图像分成共计 个super-pixel，对每个super-pixel提取一个 维特征向量。因为不知道每个super-pixel所属的类别，相当于无监督学习，因此该方法对所有的super-pixel做聚类，使用的是最大间隔聚类方法(max-margin clustering, MMC),该过程的最优化目标函数是： +Wisconsin-Madison大学的Jia Xu提出了一个统一的框架来处理各种不同类型的弱标记：图像级别的标记、bounding box和部分像素标记如scribbles。该方法把所有的训练图像分成共计$n$个super-pixel，对每个super-pixel提取一个$d$维特征向量。因为不知道每个super-pixel所属的类别，相当于无监督学习，因此该方法对所有的super-pixel做聚类，使用的是最大间隔聚类方法(max-margin clustering, MMC),该过程的最优化目标函数是： $$ \underset{W,H}{min} \qquad \frac{1}{2}tr\left ( W^TW \right ) + \lambda\sum_{p=1}^{n}\sum_{c=1}^{C}\xi \left ( w_c;x_p;h_p^c \right) From 5e07e1f036a9ca2c2ca8e8bd15f47beccebfd3db Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=8D=E8=BF=B7=E8=8C=AB=EF=BC=8C=E4=B8=8D=E7=AD=89?= =?UTF-8?q?=E5=BE=85?= Date: Fri, 9 Nov 2018 19:07:03 +0800 Subject: [PATCH 25/26] little mmodify --- ch9_图像分割/第九章_图像分割.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ch9_图像分割/第九章_图像分割.md b/ch9_图像分割/第九章_图像分割.md index e97ba8d..52cf436 100644 --- a/ch9_图像分割/第九章_图像分割.md +++ b/ch9_图像分割/第九章_图像分割.md @@ -617,8 +617,9 @@ $$ $$ \sum_{i}\psi _i^{scr}\left(y_i|X,S\right)+\sum _i-logP\left(y_i| X,\theta\right)+\sum_{i,j}\psi _{ij}\left(y_i,y_j|X\right) $$ +    -上式的最优化是通过交替求$Y$和$\theta$的最优值来实现的。文章中发现通过三次迭代就能得到比较好的结果。 +上式的最优化是通过交替求 $Y$ 和 $\theta$ 的最优值来实现的。文章中发现通过三次迭代就能得到比较好的结果。
**9.9.2 图像级别标记** From eea50e9e616ce81502ba1fd7c1ceacb6254d4ece Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=8D=E8=BF=B7=E8=8C=AB=EF=BC=8C=E4=B8=8D=E7=AD=89?= =?UTF-8?q?=E5=BE=85?= Date: Fri, 9 Nov 2018 19:09:36 +0800 Subject: [PATCH 26/26] little mmodify --- ch9_图像分割/第九章_图像分割.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch9_图像分割/第九章_图像分割.md b/ch9_图像分割/第九章_图像分割.md index 52cf436..0fd722d 100644 --- a/ch9_图像分割/第九章_图像分割.md +++ b/ch9_图像分割/第九章_图像分割.md @@ -635,7 +635,7 @@ UC Berkeley的Deepak Pathak使用了一个具有图像级别标记的训练数 $$ \underset{\theta ,P}{minimize}\qquad D(P(X)||Q(X|\theta ))\\ -subject\to\qquad A\overrightarrow{P}\geqslant\overrightarrow{b},\sum_{X}^{ }P(X)=1 +subject\to\qquad A\overrightarrow{P} \geqslant \overrightarrow{b},\sum_{X}^{ }P(X)=1 $$   

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i#{bja|K~6LC2ffLio+e1p?~l0QBl%VESER`^nU;p>38Y? literal 0 HcmV?d00001 From de7e9b37a2edaeaba7f8bfe371e2073387ebd5ea Mon Sep 17 00:00:00 2001 From: scutan90 <31844692+scutan90@users.noreply.github.com> Date: Thu, 8 Nov 2018 09:29:10 +0800 Subject: [PATCH 15/26] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index ee0d3a7..5da3908 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # 1. 版权声明 -请尊重作者的知识产权，版权所有，翻版必究。 -请大家一起维护自己的劳动成果，进行监督。 +请尊重作者的知识产权，版权所有，翻版必究。 未经许可，严禁转发内容！ +请大家一起维护自己的劳动成果，进行监督。 未经许可， 严禁转发内容！ 　　　 2018.6.27 TanJiyong # 2. 概述 From 7e8a2406fc11221e389173d9df266e2ebfc0318e Mon Sep 17 00:00:00 2001 From: Lixiaodan Date: Wed, 7 Nov 2018 20:23:18 -0800 Subject: [PATCH 16/26] add more models related to RNNs --- .../第六章_循环神经网络(RNN).md | 45 ++++++++++++------- 1 file changed, 28 insertions(+), 17 deletions(-) diff --git a/ch6_循环神经网络(RNN)/第六章_循环神经网络(RNN).md b/ch6_循环神经网络(RNN)/第六章_循环神经网络(RNN).md index c301cff..7f41126 100644 --- a/ch6_循环神经网络(RNN)/第六章_循环神经网络(RNN).md +++ b/ch6_循环神经网络(RNN)/第六章_循环神经网络(RNN).md @@ -1,7 +1,7 @@ # 第六章 循环神经网络(RNN) Markdown Revision 2; - Date: 2018/11/04 + Date: 2018/11/07 Editor: 李骁丹-杜克大学 Contact: xiaodan.li@duke.edu @@ -22,9 +22,9 @@ http://blog.csdn.net/heyongluoyao8/article/details/48636251 ## 6.2 RNNs典型特点？ -1. RNNs主要用于处理序列数据。对于传统神经网络模型，从输入层到隐含层再到输出层，层与层之间一般为全连接，每层之间神经元是无连接的。但是传统神经网络无法处理数据间的前后关联问题。例如，为了预测句子的下一个单词，一般需要该词之前的语义信息，因为一个句子中前后单词是存在语义联系的。 -2. RNNs中当前单元的输出与上一步输出也有关，因此称之为循环神经网络。具体的表现形式为当前单元（cell）会对上一步的信息进行储存并应用于当前输出的计算中。隐藏层之间的节点连接起来，并且隐藏层当前输出由当前时刻输入向量和上一时刻隐藏层状态共同决定。 -3. 理论上，RNNs能够对任何长度的序列数据进行处理。但是在实践中，为了降低复杂度往往假设当前的状态只与上一时刻状态相关，**下图便是一个典型的RNNs**： +1. RNNs主要用于处理序列数据。对于传统神经网络模型，从输入层到隐含层再到输出层，层与层之间一般为全连接，每层之间神经元是无连接的。但是传统神经网络无法处理数据间的前后关联问题。例如，为了预测句子的下一个单词，一般需要该词之前的语义信息。这是因为一个句子中前后单词是存在语义联系的。 +2. RNNs中当前单元的输出与之前步骤输出也有关，因此称之为循环神经网络。具体的表现形式为当前单元（cell）会对之前步骤信息进行储存并应用于当前输出的计算中。隐藏层之间的节点连接起来，隐藏层当前输出由当前时刻输入向量和之前时刻隐藏层状态共同决定。 +3. 理论上，RNNs能够对任何长度序列数据进行处理。但是在实践中，为了降低复杂度往往假设当前的状态只与之前某几个时刻状态相关，**下图便是一个典型的RNNs**： ![](./img/ch6/figure_6.2_1.png) @@ -114,10 +114,10 @@ ESNs(回声状态网络)虽然也是一种RNNs，但它与传统的RNNs相差较 从结构上讲，ESNs是一种特殊类型的循环神经网络，其基本思想是：使用大规模随机连接的循环网络取代经典神经网络中的中间层，从而简化网络的训练过程。因此ESNs的关键是储备池。 网络中的参数包括： -W - 储备池中节点间连接权值矩阵； -Win - 输入层到储备池之间连接权值矩阵，表明储备池中的神经元之间是相互连接； -Wback - 输出层到储备池之间的反馈连接权值矩阵，表明储备池会有输出层来的反馈； -Wout - 输入层、储备池、输出层到输出层的连接权值矩阵，表明输出层不仅与储备池连接，还与输入层和自己连接。 +（1）W - 储备池中节点间连接权值矩阵； +（2）Win - 输入层到储备池之间连接权值矩阵，表明储备池中的神经元之间是相互连接； +（3）Wback - 输出层到储备池之间的反馈连接权值矩阵，表明储备池会有输出层来的反馈； +（4）Wout - 输入层、储备池、输出层到输出层的连接权值矩阵，表明输出层不仅与储备池连接，还与输入层和自己连接。 Woutbias - 输出层的偏置项。 对于ESNs，关键是储备池的四个参数，如储备池内部连接权谱半径SR(SR=λmax=max{|W的特征指|}，只有SR <1时，ESNs才能具有回声状态属性)、储备池规模N(即储备池中神经元的个数)、储备池输入单元尺度IS(IS为储备池的输入信号连接到储备池内部神经元之前需要相乘的一个尺度因子)、储备池稀疏程度SD(即为储备池中互相连接的神经元个数占储备池神经元总个数的比例)。对于IS，待处理任务的非线性越强，输入单元尺度越大。该原则本质就是通过输入单元尺度IS，将输入变换到神经元激活函数相应的范围(神经元激活函数的不同输入范围，其非线性程度不同)。 **ESNs的结构如下图所示**： @@ -129,9 +129,9 @@ Woutbias - 输出层的偏置项。 ### 6.6.5 Gated Recurrent Unit Recurrent Neural Networks GRUs是一般的RNNs的变型版本，其主要是从以下**两个方面**进行改进。 -**一是**，序列中不同单词处（以语句为例）的数据对当前隐藏层状态的影响不同，越前面的影响越小，即每个之前状态对当前的影响进行了距离加权，距离越远，权值越小。 +1. 序列中不同单词处（以语句为例）的数据对当前隐藏层状态的影响不同，越前面的影响越小，即每个之前状态对当前的影响进行了距离加权，距离越远，权值越小。 -**二是**，在产生误差error时，其可能是由之前某一个或者几个单词共同造成，所以应当对对应的单词weight进行更新。GRUs的结构如下图所示。GRUs首先根据当前输入单词向量word vector以及前一个隐藏层状态hidden state计算出update gate和reset gate。再根据reset gate、当前word vector以及前一个hidden state计算新的记忆单元内容(new memory content)。当reset gate为1的时候，new memory content忽略之前所有memory content，最终的memory是由之前的hidden state与new memory content一起决定。 +2. 在产生误差error时，其可能是由之前某一个或者几个单词共同造成，所以应当对对应的单词weight进行更新。GRUs的结构如下图所示。GRUs首先根据当前输入单词向量word vector以及前一个隐藏层状态hidden state计算出update gate和reset gate。再根据reset gate、当前word vector以及前一个hidden state计算新的记忆单元内容(new memory content)。当reset gate为1的时候，new memory content忽略之前所有memory content，最终的memory是由之前的hidden state与new memory content一起决定。 ![](./img/ch6/figure_6.6.5_1.png) @@ -149,18 +149,29 @@ LSTMs与GRUs的区别如图所示： 从上图可以看出，二者结构十分相似，**不同在于**： -（1）new memory都是根据之前state及input进行计算，但是GRUs中有一个reset gate控制之前state的进入量，而在LSTMs里没有类似gate； +1. new memory都是根据之前state及input进行计算，但是GRUs中有一个reset gate控制之前state的进入量，而在LSTMs里没有类似gate； -（2）产生新的state的方式不同，LSTMs有两个不同的gate，分别是forget gate (f gate)和input gate(i gate)，而GRUs只有一种update gate(z gate)； +2. 产生新的state的方式不同，LSTMs有两个不同的gate，分别是forget gate (f gate)和input gate(i gate)，而GRUs只有一种update gate(z gate)； -（3）LSTMs对新产生的state可以通过output gate(o gate)进行调节，而GRUs对输出无任何调节。 +3. LSTMs对新产生的state可以通过output gate(o gate)进行调节，而GRUs对输出无任何调节。 -### 6.6.7 Clockwork RNNs(CW-RNNs) -CW-RNNs是较新的一种RNNs模型，其论文发表于2014年Beijing ICML。 -CW-RNNs也是一个RNNs的改良版本，是一种使用时钟频率来驱动的RNNs。它将隐藏层分为几个块(组，Group/Module)，每一组按照自己规定的时钟频率对输入进行处理。并且为了降低标准的RNNs的复杂性，CW-RNNs减少了参数的数目，提高了网络性能，加速了网络的训练。CW-RNNs通过不同的隐藏层模块工作在不同的时钟频率下来解决长时间依赖问题。将时钟时间进行离散化，然后在不同的时间点，不同的隐藏层组在工作。因此，所有的隐藏层组在每一步不会都同时工作，这样便会加快网络的训练。并且，时钟周期小的组的神经元的不会连接到时钟周期大的组的神经元，只会周期大的连接到周期小的(认为组与组之间的连接是有向的就好了，代表信息的传递是有向的)，周期大的速度慢，周期小的速度快，那么便是速度慢的连速度快的，反之则不成立。现在还不明白不要紧，下面会进行讲解。 +### 6.6.7 Bidirectional LSTMs +1. 与bidirectional RNNs 类似，bidirectional LSTMs有两层LSTMs。一层处理过去的训练信息，另一层处理将来的训练信息。 +2. 在bidirectional LSTMs中，通过前向LSTMs获得前向隐藏状态，后向LSTMs获得后向隐藏状态，当前隐藏状态是前向隐藏状态与后向隐藏状态的组合。 -CW-RNNs与SRNs网络结构类似，也包括输入层(Input)、隐藏层(Hidden)、输出层(Output)，它们之间也有向前连接，输入层到隐藏层的连接，隐藏层到输出层的连接。但是与SRN不同的是，隐藏层中的神经元会被划分为若干个组，设为$g$，每一组中的神经元个数相同，设为$k$，并为每一个组分配一个时钟周期$T_i\epsilon\{T_1,T_2,...,T_g\}$，每一个组中的所有神经元都是全连接，但是组$j$到组$i$的循环连接则需要满足$T_j$大于$T_i$。如下图所示，将这些组按照时钟周期递增从左到右进行排序，即$T_1 Date: Thu, 8 Nov 2018 12:50:34 +0800 Subject: [PATCH 17/26] Create GPL License --- LICENSE | 674 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 674 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f288702 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. From e73bc854f06bdea137d19a7dd75c87ca784192ed Mon Sep 17 00:00:00 2001 From: scutan90 <31844692+scutan90@users.noreply.github.com> Date: Thu, 8 Nov 2018 17:24:31 +0800 Subject: [PATCH 18/26] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5da3908..b585d4d 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ 1. 寻求有愿意继续完善的朋友、编辑、写手; 如有意合作，完善出书（成为共同作者）。 所有提交内容的贡献者，将会在文中体现贡献者个人信息（大佬-西湖大学）。 -2. 联系方式 : 请联系scutjy2015@163.com　(唯一官方邮箱)；　微信Tan： tan_weixin88 +2. 联系方式 : 请联系scutjy2015@163.com　(唯一官方邮箱)；　微信Tan： (进群先在MD版本增加、改善、提交内容后，更易进群，享受分享知识帮助他人。) From 35711e60db5d1f388db3ff0c5c6257f7010f6313 Mon Sep 17 00:00:00 2001 From: Dong Gang Date: Fri, 9 Nov 2018 09:03:21 +0800 Subject: [PATCH 19/26] =?UTF-8?q?Fix=20the=20Typo=20=E2=80=98=E4=BA=8B?= =?UTF-8?q?=E4=BB=B6=E2=80=99?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ch1_数学基础/第一章_数学基础.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ch1_数学基础/第一章_数学基础.md b/ch1_数学基础/第一章_数学基础.md index 3574e6d..1b3708d 100644 --- a/ch1_数学基础/第一章_数学基础.md +++ b/ch1_数学基础/第一章_数学基础.md @@ -159,7 +159,7 @@ $$ 右边的三个矩阵相乘的结果将会是一个接近于$A$的矩阵，在这儿，$r$越接近于$n$，则相乘的结果越接近于$A$。 ## 1.10 机器学习为什么要使用概率？ -事件的概率是衡量该时间发生的可能性的量度。虽然在一次随机试验中某个事件的发生是带有偶然性的，但那些可在相同条件下大量重复的随机试验却往往呈现出明显的数量规律。 +事件的概率是衡量该事件发生的可能性的量度。虽然在一次随机试验中某个事件的发生是带有偶然性的，但那些可在相同条件下大量重复的随机试验却往往呈现出明显的数量规律。 机器学习除了处理不确定量，也需处理随机量。不确定性和随机性可能来自多个方面，使用概率论来量化不确定性。 概率论在机器学习中扮演着一个核心角色，因为机器学习算法的设计通常依赖于对数据的概率假设。 From 5e03948228bfb0418ae39a36665cfff16240f6c9 Mon Sep 17 00:00:00 2001 From: jindongwang Date: Fri, 9 Nov 2018 11:14:13 +0800 Subject: [PATCH 20/26] add: 4 transfer learning questions --- ch11_迁移学习/第十一章_迁移学习.md | 34 ++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/ch11_迁移学习/第十一章_迁移学习.md b/ch11_迁移学习/第十一章_迁移学习.md index 830f922..e41005b 100644 --- a/ch11_迁移学习/第十一章_迁移学习.md +++ b/ch11_迁移学习/第十一章_迁移学习.md @@ -107,3 +107,37 @@ Reference: 1. 所以端到端深度学习的弊端之一是它把可能有用的人工设计的组件排除在外了，精心设计的人工组件可能非常有用，但它们也有可能真的伤害到你的算法表现。 例如，强制你的算法以音位为单位思考，也许让算法自己找到更好的表示方法更好。 所以这是一把双刃剑，可能有坏处，可能有好处，但往往好处更多，手工设计的组件往往在训练集更小的时候帮助更大。 + +## 10.8 什么是负迁移？产生负迁移的原因有哪些？（中科院计算所-王晋东） + +负迁移(Negative Transfer)指的是，在源域上学习到的知识，对于目标域上的学习产生负面作用。 + +产生负迁移的原因主要有： +- 数据问题：源域和目标域压根不相似，谈何迁移？ +- 方法问题：源域和目标域是相似的，但是，迁移学习方法不够好，没找到可迁移的成分。 + +负迁移给迁移学习的研究和应用带来了负面影响。在实际应用中，找到合理的相似性，并且选择或开发合理的迁移学习方法，能够避免负迁移现象。 + +## 10.9 迁移学习的基本问题有哪些？（中科院计算所-王晋东） + +基本问题主要有3个： +- How to transfer: 如何进行迁移学习？（设计迁移方法） +- What to transfer: 给定一个目标领域，如何找到相对应的源领域，然后进行迁移？（源领域选择） +- When to transfer: 什么时候可以进行迁移，什么时候不可以？（避免负迁移） + +## 10.10 迁移学习的基本思路？（中科院计算所-王晋东） + +迁移学习的总体思路可以概括为：开发算法来最大限度地利用有标注的领域的知识，来辅助目标领域的知识获取和学习。 + +迁移学习的核心是，找到源领域和目标领域之间的相似性，并加以合理利用。这种相似性非常普遍。比如，不同人的身体构造是相似的；自行车和摩托车的骑行方式是相似的；国际象棋和中国象棋是相似的；羽毛球和网球的打球方式是相似的。这种相似性也可以理解为不变量。以不变应万变，才能立于不败之地。 + +有了这种相似性后，下一步工作就是， 如何度量和利用这种相似性。度量工作的目标有两点：一是很好地度量两个领域的相似性，不仅定性地告诉我们它们是否相似，更定量地给出相似程度。二是以度量为准则，通过我们所要采用的学习手段，增大两个领域之间的相似性，从而完成迁移学习。 + +**一句话总结： 相似性是核心，度量准则是重要手段。** + +## 10.11 为什么需要迁移学习？（中科院计算所-王晋东） + +1. 大数据与少标注的矛盾：虽然有大量的数据，但往往都是没有标注的，无法训练机器学习模型。人工进行数据标定太耗时。 +2. 大数据与弱计算的矛盾：普通人无法拥有庞大的数据量与计算资源。因此需要借助于模型的迁移。 +3. 普适化模型与个性化需求的矛盾：即使是在同一个任务上，一个模型也往往难以满足每个人的个性化需求，比如特定的隐私设置。这就需要在不同人之间做模型的适配。 +4. 特定应用（如冷启动）的需求。 \ No newline at end of file From 23a8c758f59a609f25ed5de132a9357ad134aceb Mon Sep 17 00:00:00 2001 From: Lixiaodan Date: Thu, 8 Nov 2018 22:23:27 -0800 Subject: [PATCH 21/26] ch5 cnn has been modified --- .../第五章_卷积神经网络（CNN）.md | 72 ++++++++++--------- 1 file changed, 40 insertions(+), 32 deletions(-) diff --git a/ch5_卷积神经网络(CNN)/第五章_卷积神经网络（CNN）.md b/ch5_卷积神经网络(CNN)/第五章_卷积神经网络（CNN）.md index ac922ad..7c8cb8d 100644 --- a/ch5_卷积神经网络(CNN)/第五章_卷积神经网络（CNN）.md +++ b/ch5_卷积神经网络(CNN)/第五章_卷积神经网络（CNN）.md @@ -1,33 +1,40 @@ -# 第五章 卷积神经网络（CNN） +# 第五章 卷积神经网络（CNN） 标签（空格分隔）： 深度学习 - --- + Markdown Revision 1; + Date: 2018/11/08 + Editor: 李骁丹-杜克大学 + Contact: xiaodan.li@duke.edu ## 5.1 卷积神经网络的组成层 -在卷积神经网络中，有3种最主要的层： -> * 卷积运算层 -> * 池化层 -> * 全连接层 +1. 卷积神经网络主要应用于具有网格拓扑结构的数据集。 +2. 卷积神经网络在语音识别，图像处理以及人脸识别方面有了广泛的应用并取得了巨大的成功。 +3. 卷积神经网络主要包含3种层： + +> * 卷积运算层：该层利用过滤器对输入特征进行卷积化以获得本地特征。本地特征构成特征图(feature map)。 +> * 池化层： 池化操作包括取最值，取和以及取平均。池化操作将作用于特征图(feature map)。 +> * 全连接层：基于池化层结果，全连接层将进行将分类预测等操作。 一个完整的神经网络就是由这三种层叠加组成的。 **结构示例** -拿CIFAR-10数据集举例，一个典型的该数据集上的卷积神经网络分类器应该有[INPUT - CONV - RELU - POOL - FC]的结构， +拿CIFAR-10数据集举例，一个典型的卷积神经网络分类器应该有[INPUT - CONV - RELU - POOL - FC]的结构， > * INPUT[32\*32\*3]包含原始图片数据中的全部像素，长宽都是32，有RGB 3个颜色通道。 -> * CONV卷积层中，每个神经元会和上一层的若干小区域连接，计算权重和小区域像素的内积，举个例子可能产出的结果数据是[32\*32\*12]的。 +> * CONV卷积层中，每个神经元会和上一层的若干小区域连接，计算权重和小区域像素的内积，比如结果数据为[32\*32\*12]的。 > * RELU层，就是神经元激励层，主要的计算就是max(0,x)，结果数据依旧是[32\*32\*12]。 -> * POOLing层做的事情，可以理解成一个下采样，可能得到的结果维度就变为[16\*16\*12]了。 -> * 全连接层一般用于最后计算类别得分，得到的结果为[1\*1\*10]的，其中的10对应10个不同的类别。和名字一样，这一层的所有神经元会和上一层的所有神经元有连接。 +> * POOLing层操作可以理解成一个下采样，得到的结果维度就变为[16\*16\*12]。 +> * 全连接层一般用于最后计算类别得分，得到的结果为[1\*1\*10]，其中的10对应10个不同的类别。该层的所有神经元会和上一层的所有神经元连接。 -这样，卷积神经网络作为一个中间的通道，就一步步把原始的图像数据转成最后的类别得分了。有一个点我们要提一下，刚才说到了有几种不同的神经网络层，其中有一些层是有待训练参数的，另外一些没有。详细一点说，卷积层和全连接层包含权重和偏移的；而RELU和POOLing层只是一个固定的函数运算，是不包含权重和偏移参数的。不过POOLing层包含了我们手动指定的超参数，这个我们之后会提到。 +这样，卷积神经网络作为一个中间通道，就一步步把原始图像数据转成最后的类别得分。有一点我们要提一下，刚才提及的几种不同神经网络层，其中有一些层是需要进行参数训练，另外一些不需要。详细一点说，卷积层和全连接层包含权重和偏移；而RELU和POOLing层只是一个固定的函数运算，是不包含权重和偏移参数的。不过POOLing层包含了我们手动指定的超参数。 **总结一下**： -* （1）一个卷积神经网络由多种不同类型的层(卷积层/RELU层/POOLing层/全连接层等)叠加而成。 -* （2）每一层的输入结构是3维的数据，计算完输出依旧是3维的数据。 -* （3）卷积层和全连接层包含训练参数，RELU 和 POOLing 层不包含。 -* （4）卷积层，全连接层和 POOLing 层包含超参数，RELU 层没有。 + +* 一个卷积神经网络由多种不同类型的层(卷积层/RELU层/POOLing层/全连接层等)叠加而成。 +* 每一层的输入结构是3维的数据，计算完输出依旧是3维的数据。 +* 卷积层和全连接层包含训练参数，但是RELU 和 POOLing 层不包含。 +* 卷积层，全连接层和 POOLing 层包含超参数，RELU 层没有。 下图为 CIFAR-10 数据集构建的一个卷积神经网络结构示意图： @@ -35,7 +42,7 @@ ## 5.2 卷积如何检测边缘信息？ -卷积运算是卷积神经网络最基本的组成部分，神经网络的前几层首先检测边缘，然后，后面的层有可能检测到物体的部分区域，更靠后的一些层可能检测到完整的物体。 +卷积运算是卷积神经网络最基本的组成部分。神经网络的前几层首先检测边缘，接下来的层有可能检测到物体的部分区域，更靠后的一些层可能检测到完整的物体。 先介绍一个概念，过滤器： ![image](./img/ch5/img2.png) @@ -50,37 +57,37 @@ ![image](./img/ch5/img4.png) -我们看一下发生了什么事，把过滤器最准图像左上方3*3的范围，逐一相乘并相加，得到-5。 +如图深蓝色区域所示，过滤器在图像左上方3*3的范围内，逐一加权相加，得到-5。 同理，将过滤器右移进行相同操作，再下移，直到过滤器对准图像右下角最后一格。依次运算得到一个4*4的矩阵。 -OK，了解了过滤器以及卷积运算后，让我们看看为何过滤器能检测物体边缘： +在了解了过滤器以及卷积运算后，让我们看看为何过滤器能检测物体边缘： 举一个最简单的例子： ![image](./img/ch5/img5.png) -这张图片如上所示，左半边全是白的，右半边全是灰的，过滤器还是用之前那个，把他们进行卷积： +这张图片如上所示，左半边全是白的，右半边全是灰的，我们仍然使用之前的过滤器，对该图片进行卷积处理： ![image](./img/ch5/img6.png) 可以看到，最终得到的结果中间是一段白色，两边为灰色，于是垂直边缘被找到了。为什么呢？因为在6*6图像中红框标出来的部分，也就是图像中的分界线所在部分，与过滤器进行卷积，结果是30。而在不是分界线的所有部分进行卷积，结果都为0. -在这个图中，白色的分界线很粗，那是因为6*6的图像实在太小了，若是换成1000*1000的图像，我们会发现在最终结果中，分界线不粗且很明显。 +在这个图中，白色的分界线很粗，那是因为6\*6的图像尺寸过小，对于1000\*1000的图像，我们会发现在最终结果中，分界线较细但很明显。 这就是检测物体垂直边缘的例子，水平边缘的话只需将过滤器旋转90度。 ## 5.3 卷积的几个基本定义？ -首先，我们需要就定义一个卷积层的几个参数达成一致。 +首先，我们需要定义卷积层参数。 ### 5.3.1 卷积核大小 -（Kernel Size）: 卷积核的大小定义了卷积的视图范围。二维的常见选择大小是3，即3×3像素。 +（Kernel Size）: 卷积核大小定义了卷积的视图范围。针对二维，通常选择大小是3，即3×3像素。 ### 5.3.2 卷积核的步长 -（Stride）: Stride定义了内核的步长。虽然它的默认值通常为1，但我们可以将步长设置为2，然后对类似于MaxPooling的图像进行向下采样。 +（Stride）: Stride定义了卷积核步长。虽然它的默认值通常为1，但我们可以将步长设置为2，然后对类似于MaxPooling的图像进行向下采样。 ### 5.3.3 边缘填充 @@ -88,40 +95,41 @@ OK，了解了过滤器以及卷积运算后，让我们看看为何过滤器能 ### 5.3.4 输入和输出通道 - 一个卷积层接受一定数量的输入通道(I)，并计算一个特定数量的输出通道(O)，这一层所需的参数可以由I*O*K计算，K等于卷积核中值的数量。 + 一个卷积层接受一定数量的输入通道(I)，并计算一个特定数量的输出通道(O)，这一层所需的参数可以由I\*O\*K计算，K等于卷积核中值的数量。 ## 5.4 卷积的网络类型分类？ ### 5.4.1 普通卷积 +普通卷积如下图所示。 ![image](./img/ch5/img7.png) ### 5.4.2 扩张卷积 -又名空洞（atrous）卷积，扩张的卷积引入了另一个被称为扩张率（dilation rate）的卷积层。这定义了卷积核中值之间的间隔。一个3×3卷积核的扩张率为2，它的视图与5×5卷积核相同，而只使用9个参数。想象一下，取一个5×5卷积核，每两行或两列删除一行或一列。 +又名空洞（atrous）卷积，扩张的卷积引入了另一个被称为扩张率（dilation rate）的卷积层。扩张率定义了卷积核中值之间的间隔。一个3×3卷积核的扩张率为2，它的视图与5×5卷积核相同，而只使用9个参数。一个5×5卷积核，每两行或两列删除一行或一列。 -这将以同样的计算代价提供更广阔的视角。扩张的卷积在实时分割领域特别受欢迎。如果需要广泛的视图，并且不能负担多个卷积或更大的卷积核，那么就使用它们。 +这将以同样的计算代价提供更广阔的视角。扩张的卷积在实时分割领域特别受欢迎。如果需要广泛视图，但不能负担较多个卷积或更大的卷积核，往往会使用扩张卷积。 举例： ![image](./img/ch5/img8.png) ### 5.4.3 转置卷积 -转置卷积也就是反卷积（deconvolution）。虽然有些人经常直接叫它反卷积，但严格意义上讲是不合适的，因为它不符合一个反卷积的概念。反卷积确实存在，但它们在深度学习领域并不常见。一个实际的反卷积会恢复卷积的过程。想象一下，将一个图像放入一个卷积层中。现在把输出传递到一个黑盒子里，然后你的原始图像会再次出来。这个黑盒子就完成了一个反卷积。这是一个卷积层的数学逆过程。 +转置卷积也称反卷积（deconvolution）。虽然有些人经常直接叫其反卷积，但严格意义上讲是不合适的，因为它不符合反卷积的基本概念。反卷积确实存在，但它们在深度学习领域并不常见。一个实际的反卷积会恢复卷积的过程。将一个图像放入一个卷积层中。接着把输出传输到一个黑盒子里，最后该黑盒将输出原始图像。这个黑盒子就完成了一个反卷积。其是卷积层的数学逆过程。 -一个转置的卷积在某种程度上是相似的，因为它产生的相同的空间分辨率是一个假设的反卷积层。然而，在值上执行的实际数学操作是不同的。一个转置的卷积层执行一个常规的卷积，但是它会恢复它的空间变换（spatial transformation）。 +转置卷积在某种程度上与反卷积类似，因为它在相同的空间分辨率下，是一个假设的反卷积层。然而，在数值上执行的实际数学操作是不同的。一个转置卷积层执行一个常规的卷积，但是它会恢复它的空间变换（spatial transformation）。 在这一点上，你应该非常困惑，让我们来看一个具体的例子： 5×5的图像被馈送到一个卷积层。步长设置为2，无边界填充，而卷积核是3×3。结果得到了2×2的图像。 -如果我们想要逆转这个过程，我们需要反向的数学运算，以便从我们输入的每个像素中生成9个值。然后，我们将步长设置为2来遍历输出图像。这就是一个反卷积过程。 +如果想要逆转这个过程，我们需要反向的数学运算，以便从我们输入的每个像素中生成9个值。我们将步长设置为2来遍历输出图像。这就是一个反卷积过程。 ![image](./img/ch5/img9.png) -一个转置的卷积并不会这样做。唯一的共同点是，它保证输出将是一个5×5的图像，同时仍然执行正常的卷积运算。为了实现这一点，我们需要在输入上执行一些奇特的填充。 +一个转置的卷积做法与此不同。唯一的共同点是，它的输出仍然是一个5×5的图像，但其实际上执行的是正常卷积运算。为了实现这一点，我们需要在输入上执行一些奇特的填充。 -正如你现在所能想象的，这一步不会逆转上面的过程。至少不考虑数值。 +因此这一步不会逆转上面的过程。至少不考虑数值。 -它仅仅是重新构造了之前的空间分辨率并进行了卷积运算。这可能不是数学上的逆过程，但是对于编码-解码器（Encoder-Decoder）架构来说，这仍然是非常有用的。这样我们就可以把图像的尺度上推（upscaling）和卷积结合起来，而不是做两个分离的过程。 +它仅仅是重新构造了之前的空间分辨率并进行了卷积运算。这不是数学上的逆过程，但是对于编码-解码器（Encoder-Decoder）架构来说，这仍然是非常有用的。这样我们就可以把图像的尺度上推（upscaling）和卷积结合起来，而不是做两个分离的过程。 如果我们想反转这个过程，我们需要反数学运算，以便从我们输入的每个像素中生成9个值。之后，我们以2步幅的设置来遍历输出图像。这将是一个反卷积。 From 1f7018cc98b43b09d6dd57af0c1bd8959bac3e0c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=8D=E8=BF=B7=E8=8C=AB=EF=BC=8C=E4=B8=8D=E7=AD=89?= =?UTF-8?q?=E5=BE=85?= Date: Fri, 9 Nov 2018 18:51:48 +0800 Subject: [PATCH 22/26] little mmodify --- ch4_经典网络/modify_log.txt | 3 +++ ch4_经典网络/第四章_经典网络.md | 4 +--- ch9_图像分割/第九章_图像分割.md | 12 +++++------- 3 files changed, 9 insertions(+), 10 deletions(-) diff --git a/ch4_经典网络/modify_log.txt b/ch4_经典网络/modify_log.txt index 330454f..1388880 100644 --- a/ch4_经典网络/modify_log.txt +++ b/ch4_经典网络/modify_log.txt @@ -24,6 +24,9 @@ modify_log---->用来记录修改日志 1. 删除4.6.3，4.8.4 2. 修改4.3问题答案，添加部分论文链接 +<----qjhuang-2018-11-9----> +1. 修改部分书写错误 + 其他---->待增加 2. 修改readme内容 3. 修改modify内容 diff --git a/ch4_经典网络/第四章_经典网络.md b/ch4_经典网络/第四章_经典网络.md index 7e78952..480508e 100644 --- a/ch4_经典网络/第四章_经典网络.md +++ b/ch4_经典网络/第四章_经典网络.md @@ -421,7 +421,7 @@ figure 6' 17/8之间的特征图尺寸缩小 ![](./img/ch4/image47.png)      -Inception v4 中的Inception模块（分别为Inception A Inception B Inception C +Inception v4 中的Inception模块（分别为Inception A Inception B Inception C） ![](./img/ch4/image48.png) @@ -466,8 +466,6 @@ Inception-ResNet-v2中的reduction模块（分别为reduction A reduction B） ![](./img/ch4/image63.png) # 4.8 ResNet及其变体 -     -http://www.sohu.com/a/157818653_390227      自从AlexNet在LSVRC2012分类比赛中取得胜利之后，深度残差网络（Deep Residual Network）可以说成为过去几年中，在计算机视觉、深度学习社区领域中最具突破性的成果了。ResNet可以实现高达数百，甚至数千个层的训练，且仍能获得超赞的性能。 diff --git a/ch9_图像分割/第九章_图像分割.md b/ch9_图像分割/第九章_图像分割.md index 4da48eb..d98254b 100644 --- a/ch9_图像分割/第九章_图像分割.md +++ b/ch9_图像分割/第九章_图像分割.md @@ -219,9 +219,7 @@ learning rate：0.001。    (2) 左边的网络是收缩路径：使用卷积和maxpooling。    -(3) 右边的网络是扩张路径:使用上采样产生的特征图与左侧收缩路径对应层产生的特征图进行concatenate操作。（pooling层会丢失图像信息和降低图像分辨率且是不可逆的操作，对图像分割任务有一些影响，对图像分类任务的影响不大，为什么要做上采样？ -   -因为上采样可以补足一些图片的信息，但是信息补充的肯定不完全，所以还需要与左边的分辨率比较高的图片相连接起来（直接复制过来再裁剪到与上采样图片一样大小），这就相当于在高分辨率和更抽象特征当中做一个折衷，因为随着卷积次数增多，提取的特征也更加有效，更加抽象，上采样的图片是经历多次卷积后的图片，肯定是比较高效和抽象的图片，然后把它与左边不怎么抽象但更高分辨率的特征图片进行连接）。 +(3) 右边的网络是扩张路径:使用上采样产生的特征图与左侧收缩路径对应层产生的特征图进行concatenate操作。（pooling层会丢失图像信息和降低图像分辨率且是不可逆的操作，对图像分割任务有一些影响，对图像分类任务的影响不大，为什么要做上采样？因为上采样可以补足一些图片的信息，但是信息补充的肯定不完全，所以还需要与左边的分辨率比较高的图片相连接起来（直接复制过来再裁剪到与上采样图片一样大小），这就相当于在高分辨率和更抽象特征当中做一个折衷，因为随着卷积次数增多，提取的特征也更加有效，更加抽象，上采样的图片是经历多次卷积后的图片，肯定是比较高效和抽象的图片，然后把它与左边不怎么抽象但更高分辨率的特征图片进行连接）。    (4) 最后再经过两次反卷积操作，生成特征图，再用两个1X1的卷积做分类得到最后的两张heatmap,例如第一张表示的是第一类的得分，第二张表示第二类的得分heatmap,然后作为softmax函数的输入，算出概率比较大的softmax类，选择它作为输入给交叉熵进行反向传播训练。 @@ -608,14 +606,14 @@ ScribbleSup分为两步，第一步将像素的类别信息从scribbles传播到 Graph Cut的能量函数为： $$ -\sum_{i}\psi _i\left ( y_i | X,S \right ) +\sum_{i,j}\psi _{ij}\left ( y_i,y_j,X \right ) +\sum_{i}\psi _i\left(y_i|X,S\right)+\sum_{i,j}\psi_{ij}\left(y_i,y_j,X\right) $$    在这个graph中，每个super-pixel是graph中的一个节点，相接壤的super-pixel之间有一条连接的边。这个能量函数中的一元项包括两种情况，一个是来自于scribble的，一个是来自CNN对该super-pixel预测的概率。整个最优化过程实际上是求graph cut能量函数和CNN参数联合最优值的过程： $$ -\sum_{i}\psi _i^{scr}\left ( y_i | X,S \right ) +\sum _i-logP\left(y_i | X,\theta \right)+\sum_{i,j}\psi _{ij}\left ( y_i,y_j| X \right ) +\sum_{i}\psi _i^{scr}\left(y_i|X,S\right)+\sum _i-logP\left(y_i| X,\theta\right)+\sum_{i,j}\psi _{ij}\left(y_i,y_j|X\right) $$    @@ -633,9 +631,9 @@ UC Berkeley的Deepak Pathak使用了一个具有图像级别标记的训练数 该方法把训练过程看作是有线性限制条件的最优化过程： $$ -\underset{\theta ,P}{minimize} \qquad D(P(X)||Q(X|\theta ))\\ +\underset{\theta ,P}{minimize}\qquad D(P(X)||Q(X|\theta ))\\ -subject\ to \qquad A\overrightarrow{P}\geqslant \overrightarrow{b},\sum_{X}^{ }P(X)=1 +subject\to\qquad A\overrightarrow{P}\geqslant\overrightarrow{b},\sum_{X}^{ }P(X)=1 $$    From aafb0ed0682dcb8e4be45188539937a11fbb8dae Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=B8=8D=E8=BF=B7=E8=8C=AB=EF=BC=8C=E4=B8=8D=E7=AD=89?= =?UTF-8?q?=E5=BE=85?= Date: Fri, 9 Nov 2018 18:57:43 +0800 Subject: [PATCH 23/26] little mmodify --- ch9_图像分割/modify_log.txt | 3 +++ ch9_图像分割/第九章_图像分割.md | 12 ++++++------ 2 files changed, 9 insertions(+), 6 deletions(-) diff --git a/ch9_图像分割/modify_log.txt b/ch9_图像分割/modify_log.txt index f18ca4b..2a39323 100644 --- a/ch9_图像分割/modify_log.txt +++ b/ch9_图像分割/modify_log.txt @@ -31,6 +31,9 @@ modify_log---->用来记录修改日志 <----qjhuang-2018-11-7----> 1. 修改9.5答案部分说法（可讨论） +<----qjhuang-2018-11-9----> +1. 修改部分答案公式，链接 + 其他---->待增加 2. 修改readme内容 3. 修改modify内容 diff --git a/ch9_图像分割/第九章_图像分割.md b/ch9_图像分割/第九章_图像分割.md index d98254b..9636d82 100644 --- a/ch9_图像分割/第九章_图像分割.md +++ b/ch9_图像分割/第九章_图像分割.md @@ -578,7 +578,8 @@ Why K个mask？通过对每个 Class 对应一个Mask可以有效避免类间竞 ## **9.9 CNN在基于弱监督学习的图像分割中的应用**    -https://zhuanlan.zhihu.com/p/23811946 +答案来源：[CNN在基于弱监督学习的图像分割中的应用](https://zhuanlan.zhihu.com/p/23811946) +    最近基于深度学习的图像分割技术一般依赖于卷积神经网络CNN的训练，训练过程中需要非常大量的标记图像，即一般要求训练图像中都要有精确的分割结果。    @@ -586,11 +587,11 @@ https://zhuanlan.zhihu.com/p/23811946    如果学习算法能通过对一些初略标记过的数据集的学习就能完成好的分割结果，那么对训练数据的标记过程就很简单，这可以大大降低花在训练数据标记上的时间。这些初略标记可以是：    -1， 只给出一张图像里面包含哪些物体， +1、只给出一张图像里面包含哪些物体，    -2， 给出某个物体的边界框， +2、给出某个物体的边界框，    -3， 对图像中的物体区域做部分像素的标记，例如画一些线条、涂鸦等（scribbles)。 +3、对图像中的物体区域做部分像素的标记，例如画一些线条、涂鸦等（scribbles)。 **9.9.1 Scribble标记** @@ -608,14 +609,12 @@ Graph Cut的能量函数为： $$ \sum_{i}\psi _i\left(y_i|X,S\right)+\sum_{i,j}\psi_{ij}\left(y_i,y_j,X\right) $$ -    在这个graph中，每个super-pixel是graph中的一个节点，相接壤的super-pixel之间有一条连接的边。这个能量函数中的一元项包括两种情况，一个是来自于scribble的，一个是来自CNN对该super-pixel预测的概率。整个最优化过程实际上是求graph cut能量函数和CNN参数联合最优值的过程： $$ \sum_{i}\psi _i^{scr}\left(y_i|X,S\right)+\sum _i-logP\left(y_i| X,\theta\right)+\sum_{i,j}\psi _{ij}\left(y_i,y_j|X\right) $$ -    上式的最优化是通过交替求$Y$和$\theta$的最优值来实现的。文章中发现通过三次迭代就能得到比较好的结果。