用CNTK搞深度学习 （二） 训练基于RNN的自然语言模型 ( language model )

前一篇文章  用 CNTK 搞深度学习 （一） 入门    介绍了用CNTK构建简单前向神经网络的例子。现在假设读者已经懂得了使用CNTK的基本方法。现在我们做一个稍微复杂一点，也是自然语言挖掘中很火的一个模型： 用递归神经网络构建一个语言模型。

递归神经网络 （RNN），用图形化的表示则是隐层连接到自己的神经网络（当然只是RNN中的一种）：

不同于普通的神经网络，RNN假设样例之间并不是独立的。例如要预测“上”这个字的下一个字是什么，那么在“上”之前出现过的字就很重要，如果之前出现过“工作”，那么很可能是在说“上班”; 如果之前出前过“家乡”，那么很可能就是“上海”。 RNN就可以很好的学习出时序的特征。简单的说，RNN把前一时刻的隐层的值也作为一类feature，作为下一时刻输入的一部分。

我们这里构建这样一种language model：给定一个单词，预测下一个可能出现的单词。

这个RNN的输入是dim维的，dim等于词汇量的大小。输入向量只有在代表这个单词的分量上是1，其余为0，即[0,0,0,...0,1,0,...0]。 输出也是dim维的向量，表示每个单词出现的概率。

CNTK上构建RNN模型，主要有两点与普通的神经网络很不一样：

（1）输入格式。 此时输入的是按句子分开的文本，同一个句子内部的单词是有顺序的。所以输入要指定成 LMSequenceReader 的格式。 这个格式很麻烦（再吐槽一下，我也不是很懂，就不详细解释了，大家可以按照格式自行领悟）

（2） 模型：要使用递归模型。 主要是Delay() 函数的使用

一个可用的代码如下（再次被官方教程坑了好久，现代码改编自 CNTK-2016-02-08-Windows-64bit-CPU-Only\cntk\Examples\Text\PennTreebank\Config ）：

# Parameters can be overwritten on the command line
# for example: cntk configFile=myConfigFile RootDir=../..
# For running from Visual Studio add
# currentDirectory=$(SolutionDir)/<path to corresponding data folder>
RootDir = ".."

ConfigDir = "$RootDir$/Config"
DataDir = "$RootDir$/Data"
OutputDir = "$RootDir$/Output"
ModelDir = "$OutputDir$/Models"

# deviceId=- for CPU, >= for GPU devices, "auto" chooses the best GPU, or CPU if no usable GPU is available
deviceId = "-1"

command = writeWordAndClassInfo:train
#command = write

precision = "float"
traceLevel =
modelPath = "$ModelDir$/rnn.dnn"

# uncomment the following line to write logs to a file
stderr=$OutputDir$/rnnOutput

type = double
numCPUThreads = 

confVocabSize =
confClassSize = 

#trainFile = "ptb.train.txt"
trainFile = "review_tokens_split_first5w_lines.txt"
#validFile = "ptb.valid.txt"
testFile = "review_tokens_split_first10_lines.txt"

writeWordAndClassInfo = [
    action = "writeWordAndClass"
    inputFile = "$DataDir$/$trainFile$"
    outputVocabFile = "$ModelDir$/vocab.txt"
    outputWord2Cls = "$ModelDir$/word2cls.txt"
    outputCls2Index = "$ModelDir$/cls2idx.txt"
    vocabSize = "$confVocabSize$"
    nbrClass = "$confClassSize$"
    cutoff =
    printValues = true
]

#######################################
#  TRAINING CONFIG                    #
#######################################

train = [
    action = "train"
    minibatchSize =
    traceLevel =
    epochSize =
    recurrentLayer =
    defaultHiddenActivity = 0.1
    useValidation = true
    rnnType = "CLASSLM"

     # uncomment below and comment SimpleNetworkBuilder section to use NDL to train RNN LM
     NDLNetworkBuilder=[
        networkDescription="D:\tools\Deep Learning\CNTK-2016-02-08-Windows-64bit-CPU-Only\cntk\Examples\Text\PennTreebank\AdditionalFiles\RNNLM\rnnlm.ndl"
     ]

    SGD = [
        learningRatesPerSample = 0.1
        momentumPerMB =
        gradientClippingWithTruncation = true
        clippingThresholdPerSample = 15.0
        maxEpochs =
        unroll = false
        numMBsToShowResult =
        gradUpdateType = "none"
        loadBestModel = true

        # settings for Auto Adjust Learning Rate
        AutoAdjust = [
            autoAdjustLR = "adjustAfterEpoch"
            reduceLearnRateIfImproveLessThan = 0.001
            continueReduce = false
            increaseLearnRateIfImproveMoreThan =
            learnRateDecreaseFactor = 0.5
            learnRateIncreaseFactor = 1.382
            numMiniBatch4LRSearch =
            numPrevLearnRates =
            numBestSearchEpoch =
        ]

        dropoutRate = 0.0
    ]

    reader = [
        readerType = "LMSequenceReader"
        randomize = "none"
        nbruttsineachrecurrentiter = 

        # word class info
        wordclass = "$ModelDir$/vocab.txt"

        # if writerType is set, we will cache to a binary file
        # if the binary file exists, we will use it instead of parsing this file
        # writerType=BinaryReader

        # write definition
        wfile = "$OutputDir$/sequenceSentence.bin"

        # wsize - inital size of the file in MB
        # if calculated size would be bigger, that is used instead
        wsize = 

        # wrecords - number of records we should allocate space for in the file
        # files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
        wrecords = 

        # windowSize - number of records we should include in BinaryWriter window
        windowSize = "$confVocabSize$"

        file = "$DataDir$/$trainFile$"

        # additional features sections
        # for now store as expanded category data (including label in)
        features = [
            # sentence has no features, so need to set dimension to zero
            dim =
            # write definition
            sectionType = "data"
        ]

        # sequence break table, list indexes into sequence records, so we know when a sequence starts/stops
        sequence = [
            dim =
            wrecords =
            # write definition
            sectionType = "data"
        ]

        #labels sections
        labelIn = [
            dim =
            labelType = "Category"
            beginSequence = "</s>"
            endSequence = "</s>"

            # vocabulary size
            labelDim = "$confVocabSize$"
            labelMappingFile = "$OutputDir$/sentenceLabels.txt"

            # Write definition
            # sizeof(unsigned) which is the label index type
            elementSize =
            sectionType = "labels"
            mapping = [
                # redefine number of records for this section, since we don't need to save it for each data record
                wrecords =
                # variable size so use an average string size
                elementSize =
                sectionType = "labelMapping"
            ]

            category = [
                dim =
                # elementSize = sizeof(ElemType) is default
                sectionType = "categoryLabels"
            ]
        ]

        # labels sections
        labels = [
            dim =
            labelType = "NextWord"
            beginSequence = "O"
            endSequence = "O"

            # vocabulary size
            labelDim = "$confVocabSize$"
            labelMappingFile = "$OutputDir$/sentenceLabels.out.txt"

            # Write definition
            # sizeof(unsigned) which is the label index type
            elementSize =
            sectionType = "labels"
            mapping = [
                # redefine number of records for this section, since we don't need to save it for each data record
                wrecords =
                # variable size so use an average string size
                elementSize =
                sectionType = "labelMapping"
            ]

            category = [
                dim =
                # elementSize = sizeof(ElemType) is default
                sectionType = categoryLabels
            ]
        ]
    ]
]

write = [
    action = "write"

    outputPath = "$OutputDir$/Write"
    #outputPath = "-"                    # "-" will write to stdout; useful for debugging
    outputNodeNames = "Out,WFeat2Hid,WHid2Hid,WHid2Word" # when processing one sentence per minibatch, this is the sentence posterior
    #format = [
        #sequencePrologue = "log P(W)="    # (using this to demonstrate some formatting strings)
        #type = "real"
    #]

    minibatchSize =               # choose this to be big enough for the longest sentence
    # need to be small since models are updated for each minibatch
    traceLevel =
    epochSize = 

    reader = [
        # reader to use
        readerType = "LMSequenceReader"
        randomize = "none"              # BUGBUG: This is ignored.
        nbruttsineachrecurrentiter =   # one sentence per minibatch
        cacheBlockSize =               # workaround to disable randomization

        # word class info
        wordclass = "$ModelDir$/vocab.txt"

        # if writerType is set, we will cache to a binary file
        # if the binary file exists, we will use it instead of parsing this file
        # writerType = "BinaryReader"

        # write definition
        wfile = "$OutputDir$/sequenceSentence.bin"
        # wsize - inital size of the file in MB
        # if calculated size would be bigger, that is used instead
        wsize = 

        # wrecords - number of records we should allocate space for in the file
        # files cannot be expanded, so this should be large enough. If known modify this element in config before creating file
        wrecords = 

        # windowSize - number of records we should include in BinaryWriter window
        windowSize = "$confVocabSize$"

        file = "$DataDir$/$testFile$"

        # additional features sections
        # for now store as expanded category data (including label in)
        features = [
            # sentence has no features, so need to set dimension to zero
            dim =
            # write definition
            sectionType = "data"
        ]

        #labels sections
        labelIn = [
            dim = 

            # vocabulary size
            labelDim = "$confVocabSize$"
            labelMappingFile = "$OutputDir$/sentenceLabels.txt"

            labelType = "Category"
            beginSequence = "</s>"
            endSequence = "</s>"

            # Write definition
            # sizeof(unsigned) which is the label index type
            elementSize =
            sectionType = "labels"

            mapping = [
                # redefine number of records for this section, since we don't need to save it for each data record
                wrecords =
                # variable size so use an average string size
                elementSize =
                sectionType = "labelMapping"
            ]

            category = [
                dim =
                # elementSize = sizeof(ElemType) is default
                sectionType = "categoryLabels"
            ]
        ]

        #labels sections
        labels = [
            dim =
            labelType = "NextWord"
            beginSequence = "O"
            endSequence = "O"

            # vocabulary size
            labelDim = "$confVocabSize$"

            labelMappingFile = "$OutputDir$/sentenceLabels.out.txt"
            # Write definition
            # sizeof(unsigned) which is the label index type
            elementSize =
            sectionType = "labels"

            mapping = [
                # redefine number of records for this section, since we don't need to save it for each data record
                wrecords =
                # variable size so use an average string size
                elementSize =
                sectionType = "labelMapping"
            ]

            category = [
                dim =
                # elementSize = sizeof(ElemType) is default
                sectionType = "categoryLabels"
            ]
        ]
    ]
]    

rnnlm.ndl:

run=ndlCreateNetwork

ndlCreateNetwork=[
    # vocabulary size
    featDim=
    # vocabulary size
    labelDim=
    # hidden layer size
    hiddenDim=
    # number of classes
    nbrClass=

    initScale=

    features=SparseInput(featDim, tag="feature")

    # labels in classbasedCrossEntropy is dense and contain  values for each sample
    labels=Input(, tag="label")

    # define network
    WFeat2Hid=Parameter(hiddenDim, featDim, init="uniform", initValueScale=initScale)
    WHid2Hid=Parameter(hiddenDim, hiddenDim, init="uniform", initValueScale=initScale)

    # WHid2Word is special that it is hiddenSize X labelSize
    WHid2Word=Parameter( hiddenDim,labelDim,  init="uniform", initValueScale=initScale)
     WHid2Class=Parameter(nbrClass, hiddenDim, init="uniform", initValueScale=initScale)

    PastHid = Delay(hiddenDim, HidAfterSig, delayTime=, needGradient=true)
    HidFromHeat = Times(WFeat2Hid, features)
    HidFromRecur = Times(WHid2Hid, PastHid)
    HidBeforeSig = Plus(HidFromHeat, HidFromRecur)
    HidAfterSig = Sigmoid(HidBeforeSig)

    Out = TransposeTimes(WHid2Word, HidAfterSig)  #word part

    ClassProbBeforeSoftmax=Times(WHid2Class, HidAfterSig)

    cr = ClassBasedCrossEntropyWithSoftmax(labels, HidAfterSig, WHid2Word, ClassProbBeforeSoftmax, tag="criterion")
    EvalNodes=(Cr)
    OutputNodes=(Cr)
]

从代码上看，CNTK会让人花很大一部分精力在Data Reader上。

writeWordAndClassInfo 是简单的对所有词汇做个统计，并对单词聚类。 这里用的class based RNN，主要是为了加速计算，先把单词分成不相交的几类。 这个模块输出的文件有4列，分别是单词索引，出现频率，单词，类别。
Train 当然就是训练模型了，文本量大的话，训练还是很慢的。
Write 是输出模块，注意看这一行：  outputNodeNames = "Out,WFeat2Hid,WHid2Hid,WHid2Word" 

我想最多人关心的应该是对于一个句子，运行这个训练好的RNN之后，如何得到隐层的值吧？ 我的做法是把训练好的RNN的参数给保存下来，然后...然后无论是用java还是用python的人，都能根据这个参数还原一个RNN网络，然后我们想干嘛就能干嘛了。

Train中我是用了自己定义的模型：NDLNetworkBuilder 。 也可以用通用的递归模型，此时只要简单地规定一个参数就行了，例如

SimpleNetworkBuilder=[
        trainingCriterion=classcrossentropywithsoftmax
        evalCriterion=classcrossentropywithsoftmax
        nodeType=Sigmoid
        initValueScale=6.0
        layerSizes=::
        addPrior=false
        addDropoutNodes=false
        applyMeanVarNorm=false
        uniformInit=true;

        # these are for the class information for class-based language modeling
        vocabSize=
        nbrClass=
    ]

我这里使用自己定义的网络，主要是为了日后想改成LSTM结构。

原创博客，未经允许，请勿转载。