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深度学习批归一化及其相关算法研究进展

刘建伟 赵会丹 罗雄麟 许鋆

刘建伟, 赵会丹, 罗雄麟, 许鋆. 深度学习批归一化及其相关算法研究进展. 自动化学报, 2020, 46(6): 1090−1120 doi: 10.16383/j.aas.c180564
引用本文: 刘建伟, 赵会丹, 罗雄麟, 许鋆. 深度学习批归一化及其相关算法研究进展. 自动化学报, 2020, 46(6): 1090−1120 doi: 10.16383/j.aas.c180564
Liu Jian-Wei, Zhao Hui-Dan, Luo Xiong-Lin, Xu Jun. Research progress on batch normalization of deep learning and its related algorithms. Acta Automatica Sinica, 2020, 46(6): 1090−1120 doi: 10.16383/j.aas.c180564
Citation: Liu Jian-Wei, Zhao Hui-Dan, Luo Xiong-Lin, Xu Jun. Research progress on batch normalization of deep learning and its related algorithms. Acta Automatica Sinica, 2020, 46(6): 1090−1120 doi: 10.16383/j.aas.c180564

深度学习批归一化及其相关算法研究进展

doi: 10.16383/j.aas.c180564
基金项目: 国家重点研究发展计划项目基金(2016YFC0303703),中国石油大学(北京)年度前瞻导向及培育项目基金(2462018QZDX02)资助
详细信息
    作者简介:

    刘建伟:中国石油大学(北京)自动化系副研究员. 主要研究方向为模式识别与智能系统, 先进控制. 本文通信作者. E-mail: liujw@cup.edu.cn

    赵会丹:中国石油大学(北京)自动化系硕士研究生. 2016年获得中国石油大学(北京)自动化系学士学位. 主要研究方向为模式识别与智能系统. E-mail: zhaohuidan93@126.com

    罗雄麟:中国石油大学(北京)自动化系教授. 主要研究方向为智能控制, 复杂系统分析, 预测与控制. E-mail: luoxl@cup.edu.cn

    许鋆:哈尔滨工业大学(深圳)机电工程与自动化学院副教授. 主要研究方向为复杂非线性系统分析, 预测与控制. E-mail: xujunqgy@hit.edu.cn

Research Progress on Batch Normalization of Deep Learning and Its Related Algorithms

Funds: Supported by 2016 National Key Research and Development Program, 2016YFC0303703-03 and 2018 China University of Petroleum (Beijing) Prospective Orientation and Cultivation Project, 2462018QZDX02
  • 摘要: 深度学习已经广泛应用到各个领域, 如计算机视觉和自然语言处理等, 并都取得了明显优于早期机器学习算法的效果. 在信息技术飞速发展的今天, 训练数据逐渐趋于大数据集, 深度神经网络不断趋于大型化, 导致训练越来越困难, 速度和精度都有待提升. 2013年, Ioffe等指出训练深度神经网络过程中存在一个严重问题: 中间协变量迁移(Internal covariate shift), 使网络训练过程对参数初值敏感、收敛速度变慢, 并提出了批归一化(Batch normalization, BN)方法, 以减少中间协变量迁移问题, 加快神经网络训练过程收敛速度. 目前很多网络都将BN作为一种加速网络训练的重要手段, 鉴于BN的应用价值, 本文系统综述了BN及其相关算法的研究进展. 首先对BN的原理进行了详细分析. BN虽然简单实用, 但也存在一些问题, 如依赖于小批量数据集的大小、训练和推理过程对数据处理方式不同等, 于是很多学者相继提出了BN的各种相关结构与算法, 本文对这些结构和算法的原理、优势和可以解决的主要问题进行了分析与归纳. 然后对BN在各个神经网络领域的应用方法进行了概括总结, 并且对其他常用于提升神经网络训练性能的手段进行了归纳. 最后进行了总结, 并对BN的未来研究方向进行了展望.
  • 图  1  批归一化算法结构图

    Fig.  1  Structural diagram of batch normalization

    图  2  隐层中的批归一化算法结构图

    Fig.  2  Structural diagram of batch normalization in hidden layers

    图  3  批归一化相关结构与算法

    Fig.  3  Correlation structure and algorithms of batch normalization

    图  4  归一化传播算法结构图

    Fig.  4  Structure diagram of normalization propagation

    图  5  批量重归一化算法结构图

    Fig.  5  Structure diagram of batch renormalization

    图  6  逐步归纳批量归一化算法结构图

    Fig.  6  Structure diagram of diminishing batch normalization

    图  7  批归一化和权重归一化对比图

    Fig.  7  Comparison graph of batch normalization and layer normalization

    图  8  层归一化算法结构图

    Fig.  8  Structure diagram of layer normalization

    图  9  权重归一化算法结构图

    Fig.  9  Structure diagram of weight normalization

    图  10  自归一化神经网络结构图

    Fig.  10  Structure diagram of self-normalizing neural networks

    图  11  批归一化应用领域

    Fig.  11  Applications of batch normalization

    图  12  在CNN中应用BN

    Fig.  12  Applications of BN in CNN

    图  13  RNN结构图

    Fig.  13  Structure diagram of RNN

    图  14  LSTM结构图

    Fig.  14  Structure diagram of LSTM

    图  15  AdaBN域自适应过程

    Fig.  15  Domain adaptive process of AdaBN

    表  1  各种BN-Inception模型分类效果对比

    Table  1  Comparison of classification effects of various BN-Inception models

    模型正确率达到 72.2 % 所需迭代次数最高正确率 (%)
    Inception$ 31.0 \times 10^6 $72.2
    BN-Inception$ 13.3 \times 10^6$72.7
    BN-x5$2.1 \times 10^6$73.0
    BN-x30$2.7 \times 10^6$74.8
    BN-x5-sigmoid69.8
    下载: 导出CSV

    表  2  NIN + NP与相关模型分类效果对比 (%)

    Table  2  Comparison classification effects of NIN + NP and related models (%)

    模型CIFAR-10CIFAR-100SVHN
    NIN10.4735.682.35
    NIN + NP9.1132.191.88
    NIN + BN9.4135.322.25
    Maxout11.6838.572.47
    下载: 导出CSV

    表  3  使用不同$ \alpha ^{(j)} $值的模型分类效果对比

    Table  3  Comparison of classification effects using different $ \alpha ^{(j)}$ in model

    $ \alpha ^{(j)} $MNISTNICIFAR-10
    训练周期误差 (%)训练周期误差 (%)训练周期误差 (%)
    1522.70587.694517.31
    0.75691.91677.374917.03
    0.5691.84807.464417.11
    0.25461.91387.324317.00
    0.1481.90667.364817.10
    0.01511.94747.474316.82
    0.001481.95987.434616.28
    $ 1/j $592.10787.453717.26
    $ 1/j^2 $532.00747.594417.23
    019924.275326.09279.34
    下载: 导出CSV

    表  4  DQN + WN与DQN模型实验效果对比

    Table  4  Comparison of experimental results of DQN + WN and DQN

    游戏DQNDQN + WN
    Breakout410403
    Enduro1 2501448
    Seaquest7 1887 357
    Space invaders1 7792 179
    下载: 导出CSV

    表  5  FNN + SNN与相关模型实验效果对比(1)

    Table  5  Comparing experimental results of FNN + SNN and related models (1)

    模型平均秩差
    FNN + SNN−6.7
    SVM−6.4
    Random forest−5.9
    FNN + LN−5.3
    下载: 导出CSV

    表  6  FNN + SNN与相关模型实验效果对比(2) (%)

    Table  6  Comparing experimental results of FNN + SNN and related models (2) (%)

    方法网络层数
    24681632
    FNN + SNN83.784.283.984.583.582.5
    FNN + BN80.077.277.075.073.776.0
    FNN + WN83.782.282.581.978.156.6
    FNN + LN84.384.082.580.978.778.8
    FNN + ResNet82.280.581.281.881.280.4
    下载: 导出CSV

    表  7  CNN + BN与CNN模型分类效果对比

    Table  7  Comparing experimental results of CNN + BN and CNN

    数据集激活函数模型学习率错误率 (%)
    wm50ReLUCNN + BN0.0833.4
    wm50ReLUCNN0.00835.32
    wm50SigmoidCNN + BN0.0835.52
    wm50SigmoidCNN0.00842.80
    wm100ReLUCNN + BN0.0832.90
    wm100ReLUCNN0.00833.10
    wm100SigmoidCNN + BN0.0833.77
    wm100SigmoidCNN0.00838.50
    下载: 导出CSV

    表  8  LSRM + BN模型与相关模型实验效果对比

    Table  8  Comparing experimental results of LSRM + BN and related models

    模型PPL
    小型LSTM78.5
    小型LSTM + BN62.5
    中型LSTM49.1
    中型LSTM + BN41.0
    大型LSTM49.3
    大型LSTM + BN35.0
    下载: 导出CSV

    表  9  MIM模型与相关模型实验效果对比(%)

    Table  9  Comparing experimental results of MIM and related models (%)

    模型CIFAR-10MNIST
    maxout11.680.47
    NIN10.410.45
    RCNN-160[67]8.690.35
    MIM8.520.31
    下载: 导出CSV

    表  10  AdaBN与相关模型实验效果对比(%)

    Table  10  Comparing experimental results of AdaBN and related models (%)

    模型A$ \to $ WD$ \to $ WW $ \to $DA$ \to $ D
    AlexNet[73]61.695.499.063.8
    DDC[74]61.895.098.564.4
    DAN[75]68.568.599.067.0
    Inception BN70.394.310070.5
    GFK[76]66.797.099.470.1
    AdaBN74.295.799.873.1
    下载: 导出CSV

    表  11  批归一化及其相关算法功能对比

    Table  11  An exampletable in one column

    归一化方法收敛速度
    (训练周期)
    计算量优势缺点应用领域
    未加归一化的
    网络
    批归一化 (BN)相比于未加批归一化的网络, 收敛速度加快10倍以上适中减少网络训练过程中的中间协变量迁移问题, 使网络训练过程对参数初始值不再敏感, 可以使用更高的学习率进行训练, 加快网络训练过程收敛速度依赖 mini-batch 数据集的大小, 训练和推理时计算过程不同在CNN、分片线性神经网络等FNN中效果较好, 对RNN促进效果相对较差
    归一化传播 (NormProp)比BN更稳定、收敛速度明显更快少于BN减少中间协变量迁移现象, 不依赖于mini-batch数据集大小, 网络中每一层的输出都服从正态分布, 训练和推理阶段计算过程相同没有正则化效果, 也不能和其他正则化手段如Dropout
    共用
    理论上可以应用到使用任何激活函数、目标函数的网络, 网络可以使用任何梯度传播算法进行训练, 但具体效果还需要进一步
    证实
    批量重归一化 (BR)mini-batch数据集中含有的数据量很少或包含服从非独立同分布的样本时, 比BN更稳定, 收敛更快计算量稍多于BN减少中间协变量迁移现象, 使网络训练对参数初值不再敏感, 可以使用更高的学习率进行训练, mini-batch中数据量很少或服从非独立同分布时, 使用BR的网络性能明显优于使用BN的网络,收敛速度更快, 训练精度更高计算量稍多于BN在mini-batch数据量很少或包含服从非独立同分布的样例时, 应用效果优于BN
    逐步归纳批量归一化 (DBN)比BN更稳定, 收敛速度类似BN计算量多于BN减少中间协变量迁移, 将神经网络的训练和推理过程关联起来, 使得网络在训练时不仅考虑当前使用的mini-batch数据集, 会同时考虑过去网络训练使用过的mini-batch数据集对mini-batch数据集仍有一定的依赖性理论上可以应用BN的网络, 都可以应用DBN, 但是因为没有从根本上克服BN的问题, 在应用上同样会受到一定的限制
    层归一化 (LN)比BN鲁棒性强, 收敛速度更快计算量少于BNLN对每一层内的神经元使用单一样例进行归一化, 在训练和推理阶段计算过程相同, 应用到在线学习任务和RNN中的效果明显优于其他归一化方法, 可减少训练时间, 提升网络性能在CNN等神经网络中的效果不如BN层归一化对于稳定RNN中的隐层状态很有效, 可进一步推广, 但在CNN等前馈神经网络中的效果不如BN
    连接边权值行向量归一化 (WN)比BN收敛速度更快计算量少于BN对mini-batch数据集没有依赖性, 不需要对过去处理过的情况进行记忆, 计算复杂度低. 网络训练和推理时计算过程相同, 不会像BN一样引入过多噪声对网络没有正则化效果可以更好地应用到RNN和一些对噪声敏感的网络中, 如深度强化学习和深度生成式模型, 这些模型中使用BN的效果都不够好
    自归一化神经网络 (SNN)适中使用SeLU构造网络, 输入数据经过SNN的多层映射后, 网络中每一层输出的均值和方差可以收敛到固定点, 具有归一化特性, 网络鲁棒性强需要使用特定的激活函数SeLU才能构成网络, 在网络中使用dropout等手段会破坏网络结构, 使网络失去自归一化特性理论上可以构建任何前馈神经网络和递归神经网络, 但网络需要使用SeLU激活函数, 且不能破坏对数据均值和方差的逐层特征映射
    下载: 导出CSV

    表  12  深度神经网络加速训练方法

    Table  12  Accelerated training method of deep neural network

    名称    作用     代表文献
       Dropout防止网络过拟合, 是最常用的正则化方法   文献 [8191]
       正则化防止网络过拟合   文献 [92106]
       数据增广 (Data augmentation)通过数据变换增加训练样本数量   文献 [107118]
       改进梯度下降算法选用合适的梯度下降算法, 更有利于神经网络训练   文献 [119133]
       激活函数选择选择适当的激活函数, 更有利于网络训练   文献 [134145]
       学习率选择选择适当的学习率可以加速神经网络训练   文献 [146154]
       参数初始化好的参数初始化更易于神经网络训练   文献 [155158]
       预训练对网络进行预训练, 适当加入先验信息, 更易于网络训练   文献 [159162]
       二值化网络 (Binarized neural networks)节省神经网络训练过程所需存储空间和训练时间   文献 [163167]
       随机深度神经网络缓解深度过深的神经网络训练困难的问题   文献 [168171]
       深度神经网络压缩在不影响网络精度的情况下减少神经网络训练所需存储要求   文献 [172178]
    下载: 导出CSV
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  • 网络出版日期:  2020-07-10
  • 刊出日期:  2020-07-10

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