DeepFM

1. 算法介绍

DeepFM算法是在FM(Factorization machine)的基础上加入深度层构成. 与PNN, NFM算法相比, 它保留了FM的二阶隐式特征交叉的同时又用深度网络来获取高阶特征交叉. 其构架如下:

DeepFM

1.1 Embedding与BiInnerSumCross层的说明

与传统的FM实现不同, 这里采用Embedding与BiInnerSumCross结合的方式实现二阶隐式交叉, 传统的FM二次交叉项的表达式如下:

model^T(x_j\bold{v}_j)-\sum_i(x_i\bold{v}_i)^T(x_i\bold{v}_i)))

在实现中, 用Embedding的方式存储Deep Factorization Machine(DeepFM) - 图3, 调用Embedding的calOutput后, 将Deep Factorization Machine(DeepFM) - 图4计算后一起输出, 所以一个样本的Embedding output结果为:

model=(\bold{u}_1,\bold{u}_2,\bold{u}_3,\cdots,\bold{u}_k))

原始的二次交叉项的结为可重新表达为:

model^T(\sum_j\bold{u}_j)-\sum_i\bold{u}_i^T\bold{u}_i))

以上即是BiInnerSumCross的前向计算公式, 用Scala代码实现为:

  1. val sumVector = VFactory.denseDoubleVector(mat.getSubDim)
  3. (0 until batchSize).foreach { row =>
  4.     val partitions = mat.getRow(row).getPartitions
  5.     partitions.foreach { vectorOuter =>
  6.     data(row) -= vectorOuter.dot(vectorOuter)
  7.     sumVector.iadd(vectorOuter)
  8.     }
  9.     data(row) += sumVector.dot(sumVector)
  10.     data(row) /= 2
  11.     sumVector.clear()
  12. }

1.2 其它层说明

  • SparseInputLayer: 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
  • 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.     ensureJsonAst()
  4.     val wide = new SparseInputLayer("input", 1, new Identity(),
  5.       JsonUtils.getOptimizerByLayerType(jsonAst, "SparseInputLayer")
  6.     )
  8.     val embeddingParams = JsonUtils.getLayerParamsByLayerType(jsonAst, "Embedding")
  9.       .asInstanceOf[EmbeddingParams]
  10.     val embedding = new Embedding("embedding", embeddingParams.outputDim,
  11.       embeddingParams.numFactors, embeddingParams.optimizer.build()
  12.     )
  14.     val innerSumCross = new BiInnerSumCross("innerSumPooling", embedding)
  16.     val mlpLayer = JsonUtils.getFCLayer(jsonAst, embedding)
  18.     val join = new SumPooling("sumPooling", 1, Array[Layer](wide, innerSumCross, mlpLayer))
  20.     new SimpleLossLayer("simpleLossLayer", join, lossFunc)
  21.   }

2. 运行与性能

2.1 Json配置文件说明

DeepFM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.     "sampleratio": 0.2
  8.   },
  9.   "model": {
  10.     "modeltype": "T_DOUBLE_SPARSE_LONGKEY",
  11.     "modelsize": 148
  12.   },
  13.   "train": {
  14.     "epoch": 10,
  15.     "numupdateperepoch": 10,
  16.     "lr": 0.5,
  17.     "decay": 0.01
  18.   },
  19.   "default_optimizer": "Momentum",
  20.   "layers": [
  21.     {
  22.       "name": "wide",
  23.       "type": "sparseinputlayer",
  24.       "outputdim": 1,
  25.       "transfunc": "identity"
  26.     },
  27.     {
  28.       "name": "embedding",
  29.       "type": "embedding",
  30.       "numfactors": 8,
  31.       "outputdim": 104,
  32.       "optimizer": {
  33.         "type": "momentum",
  34.         "momentum": 0.9,
  35.         "reg2": 0.01
  36.       }
  37.     },
  38.     {
  39.       "name": "fclayer",
  40.       "type": "FCLayer",
  41.       "outputdims": [
  42.         100,
  43.         100,
  44.         1
  45.       ],
  46.       "transfuncs": [
  47.         "relu",
  48.         "relu",
  49.         "identity"
  50.       ],
  51.       "inputlayer": "embedding"
  52.     },
  53.     {
  54.       "name": "biinnersumcross",
  55.       "type": "BiInnerSumCross",
  56.       "inputlayer": "embedding",
  57.       "outputdim": 1
  58.     },
  59.     {
  60.       "name": "sumPooling",
  61.       "type": "SumPooling",
  62.       "outputdim": 1,
  63.       "inputlayers": [
  64.         "wide",
  65.         "biinnersumcross",
  66.         "fclayer"
  67.       ]
  68.     },
  69.     {
  70.       "name": "simplelosslayer",
  71.       "type": "simplelosslayer",
  72.       "lossfunc": "logloss",
  73.       "inputlayer": "sumPooling"
  74.     }
  75.   ]
  76. }

2.2 提交脚本说明

  1. runner="com.tencent.angel.ml.core.graphsubmit.GraphRunner"
  2. modelClass="com.tencent.angel.ml.classification.DeepFM"
  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.