• Deep And Wide
    • 1. 算法介绍
      • 1.1 Deep and Wide中的层
      • 1.3 网络构建
    • 2. 运行与性能
      • 2.1 Json配置文件说明
      • 2.2 提交脚本说明

    Deep And Wide

    1. 算法介绍

    Deep and Wide算法是将Embedding的结果直接输入DNN进一步提取高阶特特交叉, 最后将一阶特征与高阶特征组合起来进行预测, 其构架如下:DNN

    1.1 Deep and Wide中的层

    • SimpleInputLayer: 稀疏数据输入层, 对稀疏高维数据做了特别优化, 本质上是一个FCLayer
    • Embedding: 隐式嵌入层, 如果特征非one-hot, 则乘以特征值
    • FCLayer: DNN中最常见的层, 线性变换后接传递函数
    • SumPooling: 将多个输入的数据做element-wise的加和, 要求输入具本相同的shape
    • SimpleLossLayer: 损失层, 可以指定不同的损失函数

    1.3 网络构建

    1.   override def buildNetwork(): Unit = {
    2.     val wide = new SimpleInputLayer("input", 1, new Identity(),
    3.       JsonUtils.getOptimizerByLayerType(jsonAst, "SparseInputLayer"))
    5.     val embeddingParams = JsonUtils.getLayerParamsByLayerType(jsonAst, "Embedding")
    6.       .asInstanceOf[EmbeddingParams]
    7.     val embedding = new Embedding("embedding", embeddingParams.outputDim, embeddingParams.numFactors,
    8.       embeddingParams.optimizer.build()
    9.     )
    11.     val hiddenLayer = JsonUtils.getFCLayer(jsonAst, embedding)
    13.     val join = new SumPooling("sumPooling", 1, Array[Layer](wide, hiddenLayer))
    15.     new SimpleLossLayer("simpleLossLayer", join, lossFunc)
    16.   }

    2. 运行与性能

    2.1 Json配置文件说明

    Deep and wide的参数较多, 需要用Json配置文件的方式指定(关于Json配置文件的完整说明请参考Json说明), 一个典型的例子如下:

    1. {
    2.   "data": {
    3.     "format": "dummy",
    4.     "indexrange": 148,
    5.     "numfield": 13,
    6.     "validateratio": 0.1
    7.   },
    8.   "model": {
    9.     "modeltype": "T_DOUBLE_SPARSE_LONGKEY",
    10.     "modelsize": 148
    11.   },
    12.   "train": {
    13.     "epoch": 10,
    14.     "numupdateperepoch": 10,
    15.     "lr": 0.1,
    16.     "decay": 0.8
    17.   },
    18.   "default_optimizer": {
    19.     "type": "momentum",
    20.     "momentum": 0.9,
    21.     "reg2": 0.01
    22.   },
    23.   "layers": [
    24.     {
    25.       "name": "wide",
    26.       "type": "simpleinputlayer",
    27.       "outputdim": 1,
    28.       "transfunc": "identity"
    29.     },
    30.     {
    31.       "name": "embedding",
    32.       "type": "embedding",
    33.       "numfactors": 8,
    34.       "outputdim": 104
    35.     },
    36.     {
    37.       "name": "fclayer",
    38.       "type": "FCLayer",
    39.       "inputlayer": "embedding",
    40.       "outputdims": [
    41.         100,
    42.         100,
    43.         1
    44.       ],
    45.       "transfuncs": [
    46.         "relu",
    47.         "relu",
    48.         "identity"
    49.       ]
    50.     },
    51.     {
    52.       "name": "sumPooling",
    53.       "type": "SumPooling",
    54.       "outputdim": 1,
    55.       "inputlayers": [
    56.         "wide",
    57.         "fclayer"
    58.       ]
    59.     },
    60.     {
    61.       "name": "simplelosslayer",
    62.       "type": "simplelosslayer",
    63.       "lossfunc": "logloss",
    64.       "inputlayer": "sumPooling"
    65.     }
    66.   ]
    67. }

    2.2 提交脚本说明

    1. runner="com.tencent.angel.ml.core.graphsubmit.GraphRunner"
    2. modelClass="com.tencent.angel.ml.classification.WideAndDeep"
    4. $ANGEL_HOME/bin/angel-submit \
    5.     --angel.job.name DeepFM \
    6.     --action.type train \
    7.     --angel.app.submit.class $runner \
    8.     --ml.model.class.name $modelClass \
    9.     --angel.train.data.path $input_path \
    10.     --angel.workergroup.number $workerNumber \
    11.     --angel.worker.memory.gb $workerMemory  \
    12.     --angel.ps.number $PSNumber \
    13.     --angel.ps.memory.gb $PSMemory \  
    14.     --angel.task.data.storage.level $storageLevel \
    15.     --angel.task.memorystorage.max.gb $taskMemory

    对深度学习模型, 其数据, 训练和网络的配置请优先使用Json文件指定.