Spark MLlib Deep Learning Deep Belief Network (深度学习-深度信念网络)2.1

http://blog.csdn.net/sunbow0

Spark MLlib Deep Learning工具箱，是依据现有深度学习教程《UFLDL教程》中的算法。在SparkMLlib中的实现。详细Spark MLlib Deep Learning(深度学习)文件夹结构：

第一章Neural Net(NN)

1、源代码

2、源代码解析

3、实例

第二章Deep Belief Nets(DBNs)

1、源代码

2、源代码解析

3、实例

第三章Convolution Neural Network(CNN)

第四章 Stacked Auto-Encoders(SAE)

第五章CAE

第二章Deep Belief Network (深度信念网络)

1源代码

眼下Spark MLlib Deep Learning工具箱源代码的github地址为：

https://github.com/sunbow1/SparkMLlibDeepLearn

1.1 DBN代码

package DBNimport org.apache.spark._
import org.apache.spark.SparkContext._
import org.apache.spark.rdd.RDD
import org.apache.spark.Logging
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg._
import org.apache.spark.mllib.linalg.distributed.RowMatriximport breeze.linalg.{Matrix => BM,CSCMatrix => BSM,DenseMatrix => BDM,Vector => BV,DenseVector => BDV,SparseVector => BSV,axpy => brzAxpy,svd => brzSvd
}
import breeze.numerics.{exp => Bexp,tanh => Btanh
}import scala.collection.mutable.ArrayBuffer
import java.util.Random
import scala.math._/*** W：权重* b：偏置* c：偏置*/
case class DBNweight(W: BDM[Double],vW: BDM[Double],b: BDM[Double],vb: BDM[Double],c: BDM[Double],vc: BDM[Double]) extends Serializable/*** 配置參数*/
case class DBNConfig(size: Array[Int],layer: Int,momentum: Double,alpha: Double) extends Serializable/*** DBN(Deep Belief Network)*/class DBN(private var size: Array[Int],private var layer: Int,private var momentum: Double,private var alpha: Double) extends Serializable with Logging {//                  var size=Array(5, 10, 10)//                  var layer=3//                  var momentum=0.0//                  var alpha=1.0/*** size = architecture;         网络结构* layer = numel(nn.size);      网络层数* momentum = 0.0;              Momentum* alpha = 1.0;                 alpha*/def this() = this(DBN.Architecture, 3, 0.0, 1.0)/** 设置神经网络结构. Default: [10, 5, 1]. */def setSize(size: Array[Int]): this.type = {this.size = sizethis}/** 设置神经网络层数据. Default: 3. */def setLayer(layer: Int): this.type = {this.layer = layerthis}/** 设置Momentum. Default: 0.0. */def setMomentum(momentum: Double): this.type = {this.momentum = momentumthis}/** 设置alpha. Default: 1. */def setAlpha(alpha: Double): this.type = {this.alpha = alphathis}/*** 深度信念网络（Deep Belief Network）* 执行训练DBNtrain*/def DBNtrain(train_d: RDD[(BDM[Double], BDM[Double])], opts: Array[Double]): DBNModel = {// 參数配置 广播配置val sc = train_d.sparkContextval dbnconfig = DBNConfig(size, layer, momentum, alpha)// 初始化权重var dbn_W = DBN.InitialW(size)var dbn_vW = DBN.InitialvW(size)var dbn_b = DBN.Initialb(size)var dbn_vb = DBN.Initialvb(size)var dbn_c = DBN.Initialc(size)var dbn_vc = DBN.Initialvc(size)// 训练第1层printf("Training Level: %d.\n", 1)val weight0 = new DBNweight(dbn_W(0), dbn_vW(0), dbn_b(0), dbn_vb(0), dbn_c(0), dbn_vc(0))val weight1 = RBMtrain(train_d, opts, dbnconfig, weight0)dbn_W(0) = weight1.Wdbn_vW(0) = weight1.vWdbn_b(0) = weight1.bdbn_vb(0) = weight1.vbdbn_c(0) = weight1.cdbn_vc(0) = weight1.vc// 打印权重printf("dbn_W%d.\n", 1)val tmpw0 = dbn_W(0)for (i <- 0 to tmpw0.rows - 1) {for (j <- 0 to tmpw0.cols - 1) {print(tmpw0(i, j) + "\t")}println()}// 训练第2层 至 n层for (i <- 2 to dbnconfig.layer - 1) {// 前向计算x//  x = sigm(repmat(rbm.c', size(x, 1), 1) + x * rbm.W');printf("Training Level: %d.\n", i)val tmp_bc_w = sc.broadcast(dbn_W(i - 2))val tmp_bc_c = sc.broadcast(dbn_c(i - 2))val train_d2 = train_d.map { f =>val lable = f._1val x = f._2val x2 = DBN.sigm(x * tmp_bc_w.value.t + tmp_bc_c.value.t)(lable, x2)}// 训练第i层val weighti = new DBNweight(dbn_W(i - 1), dbn_vW(i - 1), dbn_b(i - 1), dbn_vb(i - 1), dbn_c(i - 1), dbn_vc(i - 1))val weight2 = RBMtrain(train_d2, opts, dbnconfig, weighti)dbn_W(i - 1) = weight2.Wdbn_vW(i - 1) = weight2.vWdbn_b(i - 1) = weight2.bdbn_vb(i - 1) = weight2.vbdbn_c(i - 1) = weight2.cdbn_vc(i - 1) = weight2.vc// 打印权重printf("dbn_W%d.\n", i)val tmpw1 = dbn_W(i - 1)for (i <- 0 to tmpw1.rows - 1) {for (j <- 0 to tmpw1.cols - 1) {print(tmpw1(i, j) + "\t")}println()}}new DBNModel(dbnconfig, dbn_W, dbn_b, dbn_c)}/*** 深度信念网络（Deep Belief Network）* 每一层神经网络进行训练rbmtrain*/def RBMtrain(train_t: RDD[(BDM[Double], BDM[Double])],opts: Array[Double],dbnconfig: DBNConfig,weight: DBNweight): DBNweight = {val sc = train_t.sparkContextvar StartTime = System.currentTimeMillis()var EndTime = System.currentTimeMillis()// 权重參数变量var rbm_W = weight.Wvar rbm_vW = weight.vWvar rbm_b = weight.bvar rbm_vb = weight.vbvar rbm_c = weight.cvar rbm_vc = weight.vc// 广播參数val bc_config = sc.broadcast(dbnconfig)// 训练样本数量val m = train_t.count// 计算batch的数量val batchsize = opts(0).toIntval numepochs = opts(1).toIntval numbatches = (m / batchsize).toInt// numepochs是循环的次数 for (i <- 1 to numepochs) {StartTime = System.currentTimeMillis()val splitW2 = Array.fill(numbatches)(1.0 / numbatches)var err = 0.0// 依据分组权重，随机划分每组样本数据  for (l <- 1 to numbatches) {// 1 广播权重參数val bc_rbm_W = sc.broadcast(rbm_W)val bc_rbm_vW = sc.broadcast(rbm_vW)val bc_rbm_b = sc.broadcast(rbm_b)val bc_rbm_vb = sc.broadcast(rbm_vb)val bc_rbm_c = sc.broadcast(rbm_c)val bc_rbm_vc = sc.broadcast(rbm_vc)//        // 打印权重//        println(i + "\t" + l)//        val tmpw0 = bc_rbm_W.value//        for (i <- 0 to tmpw0.rows - 1) {//          for (j <- 0 to tmpw0.cols - 1) {//            print(tmpw0(i, j) + "\t")//          }//          println()//        }// 2 样本划分val train_split2 = train_t.randomSplit(splitW2, System.nanoTime())val batch_xy1 = train_split2(l - 1)//        val train_split3 = train_t.filter { f => (f._1 >= batchsize * (l - 1) + 1) && (f._1 <= batchsize * (l)) }//        val batch_xy1 = train_split3.map(f => (f._2, f._3))// 3 前向计算// v1 = batch;// h1 = sigmrnd(repmat(rbm.c', opts.batchsize, 1) + v1 * rbm.W');// v2 = sigmrnd(repmat(rbm.b', opts.batchsize, 1) + h1 * rbm.W);// h2 = sigm(repmat(rbm.c', opts.batchsize, 1) + v2 * rbm.W');// c1 = h1' * v1;// c2 = h2' * v2;val batch_vh1 = batch_xy1.map { f =>val lable = f._1val v1 = f._2val h1 = DBN.sigmrnd((v1 * bc_rbm_W.value.t + bc_rbm_c.value.t))val v2 = DBN.sigmrnd((h1 * bc_rbm_W.value + bc_rbm_b.value.t))val h2 = DBN.sigm(v2 * bc_rbm_W.value.t + bc_rbm_c.value.t)val c1 = h1.t * v1val c2 = h2.t * v2(lable, v1, h1, v2, h2, c1, c2)}// 4 更新前向计算        // rbm.vW = rbm.momentum * rbm.vW + rbm.alpha * (c1 - c2)     / opts.batchsize;// rbm.vb = rbm.momentum * rbm.vb + rbm.alpha * sum(v1 - v2)' / opts.batchsize;// rbm.vc = rbm.momentum * rbm.vc + rbm.alpha * sum(h1 - h2)' / opts.batchsize;// W 更新方向val vw1 = batch_vh1.map {case (lable, v1, h1, v2, h2, c1, c2) =>c1 - c2}val initw = BDM.zeros[Double](bc_rbm_W.value.rows, bc_rbm_W.value.cols)val (vw2, countw2) = vw1.treeAggregate((initw, 0L))(seqOp = (c, v) => {// c: (m, count), v: (m)val m1 = c._1val m2 = m1 + v(m2, c._2 + 1)},combOp = (c1, c2) => {// c: (m, count)val m1 = c1._1val m2 = c2._1val m3 = m1 + m2(m3, c1._2 + c2._2)})val vw3 = vw2 / countw2.toDoublerbm_vW = bc_config.value.momentum * bc_rbm_vW.value + bc_config.value.alpha * vw3// b 更新方向val vb1 = batch_vh1.map {case (lable, v1, h1, v2, h2, c1, c2) =>(v1 - v2)}val initb = BDM.zeros[Double](bc_rbm_vb.value.cols, bc_rbm_vb.value.rows)val (vb2, countb2) = vb1.treeAggregate((initb, 0L))(seqOp = (c, v) => {// c: (m, count), v: (m)val m1 = c._1val m2 = m1 + v(m2, c._2 + 1)},combOp = (c1, c2) => {// c: (m, count)val m1 = c1._1val m2 = c2._1val m3 = m1 + m2(m3, c1._2 + c2._2)})val vb3 = vb2 / countb2.toDoublerbm_vb = bc_config.value.momentum * bc_rbm_vb.value + bc_config.value.alpha * vb3.t// c 更新方向val vc1 = batch_vh1.map {case (lable, v1, h1, v2, h2, c1, c2) =>(h1 - h2)}val initc = BDM.zeros[Double](bc_rbm_vc.value.cols, bc_rbm_vc.value.rows)val (vc2, countc2) = vc1.treeAggregate((initc, 0L))(seqOp = (c, v) => {// c: (m, count), v: (m)val m1 = c._1val m2 = m1 + v(m2, c._2 + 1)},combOp = (c1, c2) => {// c: (m, count)val m1 = c1._1val m2 = c2._1val m3 = m1 + m2(m3, c1._2 + c2._2)})val vc3 = vc2 / countc2.toDoublerbm_vc = bc_config.value.momentum * bc_rbm_vc.value + bc_config.value.alpha * vc3.t// 5 权重更新// rbm.W = rbm.W + rbm.vW;// rbm.b = rbm.b + rbm.vb;// rbm.c = rbm.c + rbm.vc;rbm_W = bc_rbm_W.value + rbm_vWrbm_b = bc_rbm_b.value + rbm_vbrbm_c = bc_rbm_c.value + rbm_vc// 6 计算误差val dbne1 = batch_vh1.map {case (lable, v1, h1, v2, h2, c1, c2) =>(v1 - v2)}val (dbne2, counte) = dbne1.treeAggregate((0.0, 0L))(seqOp = (c, v) => {// c: (e, count), v: (m)val e1 = c._1val e2 = (v :* v).sumval esum = e1 + e2(esum, c._2 + 1)},combOp = (c1, c2) => {// c: (e, count)val e1 = c1._1val e2 = c2._1val esum = e1 + e2(esum, c1._2 + c2._2)})val dbne = dbne2 / counte.toDoubleerr += dbne}EndTime = System.currentTimeMillis()// 打印误差结果printf("epoch: numepochs = %d , Took = %d seconds; Average reconstruction error is: %f.\n", i, scala.math.ceil((EndTime - StartTime).toDouble / 1000).toLong, err / numbatches.toDouble)}new DBNweight(rbm_W, rbm_vW, rbm_b, rbm_vb, rbm_c, rbm_vc)}}/*** NN(neural network)*/
object DBN extends Serializable {// Initialization mode namesval Activation_Function = "sigm"val Output = "linear"val Architecture = Array(10, 5, 1)/*** 初始化权重* 初始化为0*/def InitialW(size: Array[Int]): Array[BDM[Double]] = {// 初始化权重參数// weights and weight momentum// dbn.rbm{u}.W  = zeros(dbn.sizes(u + 1), dbn.sizes(u));val n = size.lengthval rbm_W = ArrayBuffer[BDM[Double]]()for (i <- 1 to n - 1) {val d1 = BDM.zeros[Double](size(i), size(i - 1))rbm_W += d1}rbm_W.toArray}/*** 初始化权重vW* 初始化为0*/def InitialvW(size: Array[Int]): Array[BDM[Double]] = {// 初始化权重參数// weights and weight momentum// dbn.rbm{u}.vW = zeros(dbn.sizes(u + 1), dbn.sizes(u));val n = size.lengthval rbm_vW = ArrayBuffer[BDM[Double]]()for (i <- 1 to n - 1) {val d1 = BDM.zeros[Double](size(i), size(i - 1))rbm_vW += d1}rbm_vW.toArray}/*** 初始化偏置向量b* 初始化为0*/def Initialb(size: Array[Int]): Array[BDM[Double]] = {// 初始化偏置向量b// weights and weight momentum// dbn.rbm{u}.b  = zeros(dbn.sizes(u), 1);val n = size.lengthval rbm_b = ArrayBuffer[BDM[Double]]()for (i <- 1 to n - 1) {val d1 = BDM.zeros[Double](size(i - 1), 1)rbm_b += d1}rbm_b.toArray}/*** 初始化偏置向量vb* 初始化为0*/def Initialvb(size: Array[Int]): Array[BDM[Double]] = {// 初始化偏置向量b// weights and weight momentum// dbn.rbm{u}.vb = zeros(dbn.sizes(u), 1);val n = size.lengthval rbm_vb = ArrayBuffer[BDM[Double]]()for (i <- 1 to n - 1) {val d1 = BDM.zeros[Double](size(i - 1), 1)rbm_vb += d1}rbm_vb.toArray}/*** 初始化偏置向量c* 初始化为0*/def Initialc(size: Array[Int]): Array[BDM[Double]] = {// 初始化偏置向量c// weights and weight momentum// dbn.rbm{u}.c  = zeros(dbn.sizes(u + 1), 1);val n = size.lengthval rbm_c = ArrayBuffer[BDM[Double]]()for (i <- 1 to n - 1) {val d1 = BDM.zeros[Double](size(i), 1)rbm_c += d1}rbm_c.toArray}/*** 初始化偏置向量vc* 初始化为0*/def Initialvc(size: Array[Int]): Array[BDM[Double]] = {// 初始化偏置向量c// weights and weight momentum// dbn.rbm{u}.vc = zeros(dbn.sizes(u + 1), 1);val n = size.lengthval rbm_vc = ArrayBuffer[BDM[Double]]()for (i <- 1 to n - 1) {val d1 = BDM.zeros[Double](size(i), 1)rbm_vc += d1}rbm_vc.toArray}/*** Gibbs採样* X = double(1./(1+exp(-P)) > rand(size(P)));*/def sigmrnd(P: BDM[Double]): BDM[Double] = {val s1 = 1.0 / (Bexp(P * (-1.0)) + 1.0)val r1 = BDM.rand[Double](s1.rows, s1.cols)val a1 = s1 :> r1val a2 = a1.data.map { f => if (f == true) 1.0 else 0.0 }val a3 = new BDM(s1.rows, s1.cols, a2)a3}/*** Gibbs採样* X = double(1./(1+exp(-P)))+1*randn(size(P));*/def sigmrnd2(P: BDM[Double]): BDM[Double] = {val s1 = 1.0 / (Bexp(P * (-1.0)) + 1.0)val r1 = BDM.rand[Double](s1.rows, s1.cols)val a3 = s1 + (r1 * 1.0)a3}/*** sigm激活函数* X = 1./(1+exp(-P));*/def sigm(matrix: BDM[Double]): BDM[Double] = {val s1 = 1.0 / (Bexp(matrix * (-1.0)) + 1.0)s1}/*** tanh激活函数* f=1.7159*tanh(2/3.*A);*/def tanh_opt(matrix: BDM[Double]): BDM[Double] = {val s1 = Btanh(matrix * (2.0 / 3.0)) * 1.7159s1}}

1.2 DBNModel代码

package DBNimport breeze.linalg.{Matrix => BM,CSCMatrix => BSM,DenseMatrix => BDM,Vector => BV,DenseVector => BDV,SparseVector => BSV
}
import org.apache.spark.rdd.RDD
import scala.collection.mutable.ArrayBufferclass DBNModel(val config: DBNConfig,val dbn_W: Array[BDM[Double]],val dbn_b: Array[BDM[Double]],val dbn_c: Array[BDM[Double]]) extends Serializable {/*** DBN模型转化为NN模型* 权重转换*/def dbnunfoldtonn(outputsize: Int): (Array[Int], Int, Array[BDM[Double]]) = {//1 size layer 參数转换val size = if (outputsize > 0) {val size1 = config.sizeval size2 = ArrayBuffer[Int]()size2 ++= size1size2 += outputsizesize2.toArray} else config.sizeval layer = if (outputsize > 0) config.layer + 1 else config.layer//2 dbn_W 參数转换var initW = ArrayBuffer[BDM[Double]]()for (i <- 0 to dbn_W.length - 1) {initW += BDM.horzcat(dbn_c(i), dbn_W(i))}(size, layer, initW.toArray)}}

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http://blog.csdn.net/sunbow0