标签：spark mllib 深度学习 神经网络 deep learning
Spark MLlib Deep Learning Neural Net(深度学习-神经网络)1.1
Spark MLlib Deep Learning工具箱，是根据现有深度学习教程《UFLDL教程》中的算法，在SparkMLlib中的实现。具体Spark MLlib Deep Learning(深度学习)目录结构：
第一章Neural Net(NN)
1、源码
2、源码解析
3、实例
第二章Deep Belief Nets(DBNs)
第三章Convolution Neural Network(CNN)
第四章 Stacked Auto-Encoders(SAE)
第五章CAE
目前Spark MLlib Deep Learning工具箱源码的github地址为：
https://github.com/sunbow1/SparkMLlibDeepLearn
package NN
import 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.RowMatrix
import 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._
/**
* label：目标矩阵
* nna：神经网络每层节点的输出值,a(0),a(1),a(2)
* error：输出层与目标值的误差矩阵
*/
case class NNLabel(label: BDM[Double], nna: ArrayBuffer[BDM[Double]], error: BDM[Double]) extends Serializable
/**
* 配置参数
*/
case class NNConfig(
size: Array[Int],
layer: Int,
activation_function: String,
learningRate: Double,
momentum: Double,
scaling_learningRate: Double,
weightPenaltyL2: Double,
nonSparsityPenalty: Double,
sparsityTarget: Double,
inputZeroMaskedFraction: Double,
dropoutFraction: Double,
testing: Double,
output_function: String) extends Serializable
/**
* NN(neural network)
*/
class NeuralNet(
private var size: Array[Int],
private var layer: Int,
private var activation_function: String,
private var learningRate: Double,
private var momentum: Double,
private var scaling_learningRate: Double,
private var weightPenaltyL2: Double,
private var nonSparsityPenalty: Double,
private var sparsityTarget: Double,
private var inputZeroMaskedFraction: Double,
private var dropoutFraction: Double,
private var testing: Double,
private var output_function: String) extends Serializable with Logging {
// var size=Array(5, 7, 1)
// var layer=3
// var activation_function="tanh_opt"
// var learningRate=2.0
// var momentum=0.5
// var scaling_learningRate=1.0
// var weightPenaltyL2=0.0
// var nonSparsityPenalty=0.0
// var sparsityTarget=0.05
// var inputZeroMaskedFraction=0.0
// var dropoutFraction=0.0
// var testing=0.0
// var output_function="sigm"
/**
* size = architecture;
* n = numel(nn.size);
* activation_function = sigm 隐含层函数Activation functions of hidden layers: 'sigm' (sigmoid) or 'tanh_opt' (optimal tanh).
* learningRate = 2; 学习率learning rate Note: typically needs to be lower when using 'sigm' activation function and non-normalized inputs.
* momentum = 0.5; Momentum
* scaling_learningRate = 1; Scaling factor for the learning rate (each epoch)
* weightPenaltyL2 = 0; 正则化L2 regularization
* nonSparsityPenalty = 0; 权重稀疏度惩罚值on sparsity penalty
* sparsityTarget = 0.05; Sparsity target
* inputZeroMaskedFraction = 0; 加入noise,Used for Denoising AutoEncoders
* dropoutFraction = 0; 每一次mini-batch样本输入训练时，随机扔掉x%的隐含层节点Dropout level (http://www.cs.toronto.edu/~hinton/absps/dropout.pdf)
* testing = 0; Internal variable. nntest sets this to one.
* output = 'sigm'; 输出函数output unit 'sigm' (=logistic), 'softmax' and 'linear' *
*/
def this() = this(NeuralNet.Architecture, 3, NeuralNet.Activation_Function, 2.0, 0.5, 1.0, 0.0, 0.0, 0.05, 0.0, 0.0, 0.0, NeuralNet.Output)
/** 设置神经网络结构. Default: [10, 5, 1]. */
def setSize(size: Array[Int]): this.type = {
this.size = size
this
}
/** 设置神经网络层数据. Default: 3. */
def setLayer(layer: Int): this.type = {
this.layer = layer
this
}
/** 设置隐含层函数. Default: sigm. */
def setActivation_function(activation_function: String): this.type = {
this.activation_function = activation_function
this
}
/** 设置学习率因子. Default: 2. */
def setLearningRate(learningRate: Double): this.type = {
this.learningRate = learningRate
this
}
/** 设置Momentum. Default: 0.5. */
def setMomentum(momentum: Double): this.type = {
this.momentum = momentum
this
}
/** 设置scaling_learningRate. Default: 1. */
def setScaling_learningRate(scaling_learningRate: Double): this.type = {
this.scaling_learningRate = scaling_learningRate
this
}
/** 设置正则化L2因子. Default: 0. */
def setWeightPenaltyL2(weightPenaltyL2: Double): this.type = {
this.weightPenaltyL2 = weightPenaltyL2
this
}
/** 设置权重稀疏度惩罚因子. Default: 0. */
def setNonSparsityPenalty(nonSparsityPenalty: Double): this.type = {
this.nonSparsityPenalty = nonSparsityPenalty
this
}
/** 设置权重稀疏度目标值. Default: 0.05. */
def setSparsityTarget(sparsityTarget: Double): this.type = {
this.sparsityTarget = sparsityTarget
this
}
/** 设置权重加入噪声因子. Default: 0. */
def setInputZeroMaskedFraction(inputZeroMaskedFraction: Double): this.type = {
this.inputZeroMaskedFraction = inputZeroMaskedFraction
this
}
/** 设置权重Dropout因子. Default: 0. */
def setDropoutFraction(dropoutFraction: Double): this.type = {
this.dropoutFraction = dropoutFraction
this
}
/** 设置testing. Default: 0. */
def setTesting(testing: Double): this.type = {
this.testing = testing
this
}
/** 设置输出函数. Default: linear. */
def setOutput_function(output_function: String): this.type = {
this.output_function = output_function
this
}
/**
* 运行神经网络算法.
*/
def NNtrain(train_d: RDD[(BDM[Double], BDM[Double])], opts: Array[Double]): NeuralNetModel = {
val sc = train_d.sparkContext
var initStartTime = System.currentTimeMillis()
var initEndTime = System.currentTimeMillis()
// 参数配置 广播配置
var nnconfig = NNConfig(size, layer, activation_function, learningRate, momentum, scaling_learningRate,
weightPenaltyL2, nonSparsityPenalty, sparsityTarget, inputZeroMaskedFraction, dropoutFraction, testing,
output_function)
// 初始化权重
var nn_W = NeuralNet.InitialWeight(size)
var nn_vW = NeuralNet.InitialWeightV(size)
// val tmpw = nn_W(1)
// for (i <- 0 to tmpw.rows -1) {
// for (j <- 0 to tmpw.cols - 1) {
// print(tmpw(i, j) + "\t")
// }
// println()
// }
// 初始化每层的平均激活度nn.p
// average activations (for use with sparsity)
var nn_p = NeuralNet.InitialActiveP(size)
// 样本数据划分：训练数据、交叉检验数据
val validation = opts(2)
val splitW1 = Array(1.0 - validation, validation)
val train_split1 = train_d.randomSplit(splitW1, System.nanoTime())
val train_t = train_split1(0)
val train_v = train_split1(1)
// m:训练样本的数量
val m = train_t.count
// batchsize是做batch gradient时候的大小
// 计算batch的数量
val batchsize = opts(0).toInt
val numepochs = opts(1).toInt
val numbatches = (m / batchsize).toInt
var L = Array.fill(numepochs * numbatches.toInt)(0.0)
var n = 0
var loss_train_e = Array.fill(numepochs)(0.0)
var loss_val_e = Array.fill(numepochs)(0.0)
// numepochs是循环的次数
for (i <- 1 to numepochs) {
initStartTime = System.currentTimeMillis()
val splitW2 = Array.fill(numbatches)(1.0 / numbatches)
// 根据分组权重，随机划分每组样本数据
val bc_config = sc.broadcast(nnconfig)
for (l <- 1 to numbatches) {
// 权重
val bc_nn_W = sc.broadcast(nn_W)
val bc_nn_vW = sc.broadcast(nn_vW)
// println(i + "\t" + l)
// val tmpw0 = bc_nn_W.value(0)
// for (i <- 0 to tmpw0.rows - 1) {
// for (j <- 0 to tmpw0.cols - 1) {
// print(tmpw0(i, j) + "\t")
// }
// println()
// }
// val tmpw1 = bc_nn_W.value(1)
// for (i <- 0 to tmpw1.rows - 1) {
// for (j <- 0 to tmpw1.cols - 1) {
// print(tmpw1(i, j) + "\t")
// }
// println()
// }
// 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))
// Add noise to input (for use in denoising autoencoder)
// 加入noise，这是denoising autoencoder需要使用到的部分
// 这部分请参见《Extracting and Composing Robust Features with Denoising Autoencoders》这篇论文
// 具体加入的方法就是把训练样例中的一些数据调整变为0，inputZeroMaskedFraction表示了调整的比例
//val randNoise = NeuralNet.RandMatrix(batch_x.numRows.toInt, batch_x.numCols.toInt, inputZeroMaskedFraction)
val batch_xy2 = if (bc_config.value.inputZeroMaskedFraction != 0) {
NeuralNet.AddNoise(batch_xy1, bc_config.value.inputZeroMaskedFraction)
} else batch_xy1
// val tmpxy = batch_xy2.map(f => (f._1.toArray,f._2.toArray)).toArray.map {f => ((new ArrayBuffer() ++ f._1) ++ f._2).toArray}
// for (i <- 0 to tmpxy.length - 1) {
// for (j <- 0 to tmpxy(i).length - 1) {
// print(tmpxy(i)(j) + "\t")
// }
// println()
// }
// NNff是进行前向传播
// nn = nnff(nn, batch_x, batch_y);
val train_nnff = NeuralNet.NNff(batch_xy2, bc_config, bc_nn_W)
// val tmpa0 = train_nnff.map(f => f._1.nna(0)).take(20)
// println("tmpa0")
// for (i <- 0 to 10) {
// for (j <- 0 to tmpa0(i).cols - 1) {
// print(tmpa0(i)(0, j) + "\t")
// }
// println()
// }
// val tmpa1 = train_nnff.map(f => f._1.nna(1)).take(20)
// println("tmpa1")
// for (i <- 0 to 10) {
// for (j <- 0 to tmpa1(i).cols - 1) {
// print(tmpa1(i)(0, j) + "\t")
// }
// println()
// }
// val tmpa2 = train_nnff.map(f => f._1.nna(2)).take(20)
// println("tmpa2")
// for (i <- 0 to 10) {
// for (j <- 0 to tmpa2(i).cols - 1) {
// print(tmpa2(i)(0, j) + "\t")
// }
// println()
// }
// sparsity计算，计算每层节点的平均稀疏度
nn_p = NeuralNet.ActiveP(train_nnff, bc_config, nn_p)
val bc_nn_p = sc.broadcast(nn_p)
// NNbp是后向传播
// nn = nnbp(nn);
val train_nnbp = NeuralNet.NNbp(train_nnff, bc_config, bc_nn_W, bc_nn_p)
// val tmpd0 = rdd5.map(f => f._2(2)).take(20)
// println("tmpd0")
// for (i <- 0 to 10) {
// for (j <- 0 to tmpd0(i).cols - 1) {
// print(tmpd0(i)(0, j) + "\t")
// }
// println()
// }
// val tmpd1 = rdd5.map(f => f._2(1)).take(20)
// println("tmpd1")
// for (i <- 0 to 10) {
// for (j <- 0 to tmpd1(i).cols - 1) {
// print(tmpd1(i)(0, j) + "\t")
// }
// println()
// }
// val tmpdw0 = rdd5.map(f => f._3(0)).take(20)
// println("tmpdw0")
// for (i <- 0 to 10) {
// for (j <- 0 to tmpdw0(i).cols - 1) {
// print(tmpdw0(i)(0, j) + "\t")
// }
// println()
// }
// val tmpdw1 = rdd5.map(f => f._3(1)).take(20)
// println("tmpdw1")
// for (i <- 0 to 10) {
// for (j <- 0 to tmpdw1(i).cols - 1) {
// print(tmpdw1(i)(0, j) + "\t")
// }
// println()
// }
// nn = NNapplygrads(nn) returns an neural network structure with updated
// weights and biases
// 更新权重参数：w=w-α*[dw + λw]
val train_nnapplygrads = NeuralNet.NNapplygrads(train_nnbp, bc_config, bc_nn_W, bc_nn_vW)
nn_W = train_nnapplygrads(0)
nn_vW = train_nnapplygrads(1)
// val tmpw2 = train_nnapplygrads(0)(0)
// for (i <- 0 to tmpw2.rows - 1) {
// for (j <- 0 to tmpw2.cols - 1) {
// print(tmpw2(i, j) + "\t")
// }
// println()
// }
// val tmpw3 = train_nnapplygrads(0)(1)
// for (i <- 0 to tmpw3.rows - 1) {
// for (j <- 0 to tmpw3.cols - 1) {
// print(tmpw3(i, j) + "\t")
// }
// println()
// }
// error and loss
// 输出误差计算
val loss1 = train_nnff.map(f => f._1.error)
val (loss2, counte) = loss1.treeAggregate((0.0, 0L))(
seqOp = (c, v) => {
// c: (e, count), v: (m)
val e1 = c._1
val e2 = (v :* v).sum
val esum = e1 + e2
(esum, c._2 + 1)
},
combOp = (c1, c2) => {
// c: (e, count)
val e1 = c1._1
val e2 = c2._1
val esum = e1 + e2
(esum, c1._2 + c2._2)
})
val Loss = loss2 / counte.toDouble
L(n) = Loss * 0.5
n = n + 1
}
// 计算本次迭代的训练误差及交叉检验误差
// Full-batch train mse
val evalconfig = NNConfig(size, layer, activation_function, learningRate, momentum, scaling_learningRate,
weightPenaltyL2, nonSparsityPenalty, sparsityTarget, inputZeroMaskedFraction, dropoutFraction, 1.0,
output_function)
loss_train_e(i - 1) = NeuralNet.NNeval(train_t, sc.broadcast(evalconfig), sc.broadcast(nn_W))
if (validation > 0) loss_val_e(i - 1) = NeuralNet.NNeval(train_v, sc.broadcast(evalconfig), sc.broadcast(nn_W))
// 更新学习因子
// nn.learningRate = nn.learningRate * nn.scaling_learningRate;
nnconfig = NNConfig(size, layer, activation_function, nnconfig.learningRate * nnconfig.scaling_learningRate, momentum, scaling_learningRate,
weightPenaltyL2, nonSparsityPenalty, sparsityTarget, inputZeroMaskedFraction, dropoutFraction, testing,
output_function)
initEndTime = System.currentTimeMillis()
// 打印输出结果
printf("epoch: numepochs = %d , Took = %d seconds; Full-batch train mse = %f, val mse = %f.\n", i, scala.math.ceil((initEndTime - initStartTime).toDouble / 1000).toLong, loss_train_e(i - 1), loss_val_e(i - 1))
}
val configok = NNConfig(size, layer, activation_function, learningRate, momentum, scaling_learningRate,
weightPenaltyL2, nonSparsityPenalty, sparsityTarget, inputZeroMaskedFraction, dropoutFraction, 1.0,
output_function)
new NeuralNetModel(configok, nn_W)
}
}
/**
* NN(neural network)
*/
object NeuralNet extends Serializable {
// Initialization mode names
val Activation_Function = "sigm"
val Output = "linear"
val Architecture = Array(10, 5, 1)
/**
* 增加随机噪声
* 若随机值>=Fraction，值不变，否则改为0
*/
def AddNoise(rdd: RDD[(BDM[Double], BDM[Double])], Fraction: Double): RDD[(BDM[Double], BDM[Double])] = {
val addNoise = rdd.map { f =>
val features = f._2
val a = BDM.rand[Double](features.rows, features.cols)
val a1 = a :>= Fraction
val d1 = a1.data.map { f => if (f == true) 1.0 else 0.0 }
val a2 = new BDM(features.rows, features.cols, d1)
val features2 = features :* a2
(f._1, features2)
}
addNoise
}
/**
* 初始化权重
* 初始化为一个很小的、接近零的随机值
*/
def InitialWeight2(size: Array[Int]): Array[BDM[Double]] = {
// 初始化权重参数
// weights and weight momentum
// nn.W{i - 1} = (rand(nn.size(i), nn.size(i - 1)+1) - 0.5) * 2 * 4 * sqrt(6 / (nn.size(i) + nn.size(i - 1)));
// nn.vW{i - 1} = zeros(size(nn.W{i - 1}));
val n = size.length
val nn_W = ArrayBuffer[BDM[Double]]()
val d1 = BDM((2.54631575950577, -2.72375471180638, -1.83131523622017, -0.832303531504013, -1.28869970471936, -0.460188104184124), (-1.52091024201213, 1.81815348316090, -0.533406209340414, 1.77153723107141, -1.70376378930231, 1.95852409868481), (0.604392922735100, -0.312805008341265, 2.46338861792203, -2.77264318419692, -2.74202474572555, 0.142284005609256), (-0.0792951314491902, 0.652983968878905, 2.35836765255640, -2.04274164893227, 1.39603060318734, -1.68208055847319), (2.21352121948139, 1.65144527075334, -0.507588360889342, -1.68141383648426, -0.310581480324221, 0.973756570035639), (1.48264358368951, 2.38613449604874, 2.22681802175890, -1.70428719030501, 2.44271213316363, 1.91268676272635), (-0.246256073282793, 1.34750367072394, -2.50094445126864, 0.587138926992906, -0.192365052800164, -2.71732925728203))
nn_W += d1
val d2 = BDM((1.25592501437006, -0.834980000207940, 2.29875024099543, 0.0194882319892158, 1.45126037957791, -0.492648144141757, -1.35365058999520, -2.15014190874756))
nn_W += d2
nn_W.toArray
}
def InitialWeight(size: Array[Int]): Array[BDM[Double]] = {
// 初始化权重参数
// weights and weight momentum
// nn.W{i - 1} = (rand(nn.size(i), nn.size(i - 1)+1) - 0.5) * 2 * 4 * sqrt(6 / (nn.size(i) + nn.size(i - 1)));
// nn.vW{i - 1} = zeros(size(nn.W{i - 1}));
val n = size.length
val nn_W = ArrayBuffer[BDM[Double]]()
for (i <- 1 to n - 1) {
val d1 = BDM.rand(size(i), size(i - 1) + 1)
d1 :-= 0.5
val f1 = 2 * 4 * sqrt(6.0 / (size(i) + size(i - 1)))
val d2 = d1 :* f1
//val d3 = new DenseMatrix(d2.rows, d2.cols, d2.data, d2.isTranspose)
//val d4 = Matrices.dense(d2.rows, d2.cols, d2.data)
nn_W += d2
}
nn_W.toArray
}
/**
* 初始化权重vW
* 初始化为0
*/
def InitialWeightV(size: Array[Int]): Array[BDM[Double]] = {
// 初始化权重参数
// weights and weight momentum
// nn.vW{i - 1} = zeros(size(nn.W{i - 1}));
val n = size.length
val nn_vW = ArrayBuffer[BDM[Double]]()
for (i <- 1 to n - 1) {
val d1 = BDM.zeros[Double](size(i), size(i - 1) + 1)
nn_vW += d1
}
nn_vW.toArray
}
/**
* 初始每一层的平均激活度
* 初始化为0
*/
def InitialActiveP(size: Array[Int]): Array[BDM[Double]] = {
// 初始每一层的平均激活度
// average activations (for use with sparsity)
// nn.p{i} = zeros(1, nn.size(i));
val n = size.length
val nn_p = ArrayBuffer[BDM[Double]]()
nn_p += BDM.zeros[Double](1, 1)
for (i <- 1 to n - 1) {
val d1 = BDM.zeros[Double](1, size(i))
nn_p += d1
}
nn_p.toArray
}
/**
* 随机让网络某些隐含层节点的权重不工作
* 若随机值>=Fraction，矩阵值不变，否则改为0
*/
def DropoutWeight(matrix: BDM[Double], Fraction: Double): Array[BDM[Double]] = {
val aa = BDM.rand[Double](matrix.rows, matrix.cols)
val aa1 = aa :> Fraction
val d1 = aa1.data.map { f => if (f == true) 1.0 else 0.0 }
val aa2 = new BDM(matrix.rows: Int, matrix.cols: Int, d1: Array[Double])
val matrix2 = matrix :* aa2
Array(aa2, matrix2)
}
/**
* 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)) * 1.7159
s1
}
/**
* nnff是进行前向传播
* 计算神经网络中的每个节点的输出值;
*/
def NNff(
batch_xy2: RDD[(BDM[Double], BDM[Double])],
bc_config: org.apache.spark.broadcast.Broadcast[NNConfig],
bc_nn_W: org.apache.spark.broadcast.Broadcast[Array[BDM[Double]]]): RDD[(NNLabel, Array[BDM[Double]])] = {
// 第1层:a(1)=[1 x]
// 增加偏置项b
val train_data1 = batch_xy2.map { f =>
val lable = f._1
val features = f._2
val nna = ArrayBuffer[BDM[Double]]()
val Bm1 = new BDM(features.rows, 1, Array.fill(features.rows * 1)(1.0))
val features2 = BDM.horzcat(Bm1, features)
val error = BDM.zeros[Double](lable.rows, lable.cols)
nna += features2
NNLabel(lable, nna, error)
}
// println("bc_size " + bc_config.value.size(0) + bc_config.value.size(1) + bc_config.value.size(2))
// println("bc_layer " + bc_config.value.layer)
// println("bc_activation_function " + bc_config.value.activation_function)
// println("bc_output_function " + bc_config.value.output_function)
//
// println("tmpw0 ")
// val tmpw0 = bc_nn_W.value(0)
// for (i <- 0 to tmpw0.rows - 1) {
// for (j <- 0 to tmpw0.cols - 1) {
// print(tmpw0(i, j) + "\t")
// }
// println()
// }
// feedforward pass
// 第2至n-1层计算，a(i)=f(a(i-1)*w(i-1)')
//val tmp1 = train_data1.map(f => f.nna(0).data).take(1)(0)
//val tmp2 = new BDM(1, tmp1.length, tmp1)
//val nn_a = ArrayBuffer[BDM[Double]]()
//nn_a += tmp2
val train_data2 = train_data1.map { f =>
val nn_a = f.nna
val dropOutMask = ArrayBuffer[BDM[Double]]()
dropOutMask += new BDM[Double](1, 1, Array(0.0))
for (j <- 1 to bc_config.value.layer - 2) {
// 计算每层输出
// Calculate the unit's outputs (including the bias term)
// nn.a{i} = sigm(nn.a{i - 1} * nn.W{i - 1}')
// nn.a{i} = tanh_opt(nn.a{i - 1} * nn.W{i - 1}');
val A1 = nn_a(j - 1)
val W1 = bc_nn_W.value(j - 1)
val aw1 = A1 * W1.t
val nnai1 = bc_config.value.activation_function match {
case "sigm" =>
val aw2 = NeuralNet.sigm(aw1)
aw2
case "tanh_opt" =>
val aw2 = NeuralNet.tanh_opt(aw1)
//val aw2 = Btanh(aw1 * (2.0 / 3.0)) * 1.7159
aw2
}
// dropout计算
// Dropout是指在模型训练时随机让网络某些隐含层节点的权重不工作，不工作的那些节点可以暂时认为不是网络结构的一部分
// 但是它的权重得保留下来（只是暂时不更新而已），因为下次样本输入时它可能又得工作了
// 参照 http://www.cnblogs.com/tornadomeet/p/3258122.html
val dropoutai = if (bc_config.value.dropoutFraction > 0) {
if (bc_config.value.testing == 1) {
val nnai2 = nnai1 * (1.0 - bc_config.value.dropoutFraction)
Array(new BDM[Double](1, 1, Array(0.0)), nnai2)
} else {
NeuralNet.DropoutWeight(nnai1, bc_config.value.dropoutFraction)
}
} else {
val nnai2 = nnai1
Array(new BDM[Double](1, 1, Array(0.0)), nnai2)
}
val nnai2 = dropoutai(1)
dropOutMask += dropoutai(0)
// Add the bias term
// 增加偏置项b
// nn.a{i} = [ones(m,1) nn.a{i}];
val Bm1 = BDM.ones[Double](nnai2.rows, 1)
val nnai3 = BDM.horzcat(Bm1, nnai2)
nn_a += nnai3
}
(NNLabel(f.label, nn_a, f.error), dropOutMask.toArray)
}
// 输出层计算
val train_data3 = train_data2.map { f =>
val nn_a = f._1.nna
// nn.a{n} = sigm(nn.a{n - 1} * nn.W{n - 1}');
// nn.a{n} = nn.a{n - 1} * nn.W{n - 1}';
val An1 = nn_a(bc_config.value.layer - 2)
val Wn1 = bc_nn_W.value(bc_config.value.layer - 2)
val awn1 = An1 * Wn1.t
val nnan1 = bc_config.value.output_function match {
case "sigm" =>
val awn2 = NeuralNet.sigm(awn1)
//val awn2 = 1.0 / (Bexp(awn1 * (-1.0)) + 1.0)
awn2
case "linear" =>
val awn2 = awn1
awn2
}
nn_a += nnan1
(NNLabel(f._1.label, nn_a, f._1.error), f._2)
}
// error and loss
// 输出误差计算
// nn.e = y - nn.a{n};
// val nn_e = batch_y - nnan
val train_data4 = train_data3.map { f =>
val batch_y = f._1.label
val nnan = f._1.nna(bc_config.value.layer - 1)
val error = (batch_y - nnan)
(NNLabel(f._1.label, f._1.nna, error), f._2)
}
train_data4
}
/**
* sparsity计算，网络稀疏度
* 计算每个节点的平均值
*/
def ActiveP(
train_nnff: RDD[(NNLabel, Array[BDM[Double]])],
bc_config: org.apache.spark.broadcast.Broadcast[NNConfig],
nn_p_old: Array[BDM[Double]]): Array[BDM[Double]] = {
val nn_p = ArrayBuffer[BDM[Double]]()
nn_p += BDM.zeros[Double](1, 1)
// calculate running exponential activations for use with sparsity
// sparsity计算，计算sparsity，nonSparsityPenalty 是对没达到sparsitytarget的参数的惩罚系数
for (i <- 1 to bc_config.value.layer - 1) {
val pi1 = train_nnff.map(f => f._1.nna(i))
val initpi = BDM.zeros[Double](1, bc_config.value.size(i))
val (piSum, miniBatchSize) = pi1.treeAggregate((initpi, 0L))(
seqOp = (c, v) => {
// c: (nnasum, count), v: (nna)
val nna1 = c._1
val nna2 = v
val nnasum = nna1 + nna2
(nnasum, c._2 + 1)
},
combOp = (c1, c2) => {
// c: (nnasum, count)
val nna1 = c1._1
val nna2 = c2._1
val nnasum = nna1 + nna2
(nnasum, c1._2 + c2._2)
})
val piAvg = piSum / miniBatchSize.toDouble
val oldpi = nn_p_old(i)
val newpi = (piAvg * 0.01) + (oldpi * 0.09)
nn_p += newpi
}
nn_p.toArray
}
/**
* NNbp是后向传播
* 计算权重的平均偏导数
*/
def NNbp(
train_nnff: RDD[(NNLabel, Array[BDM[Double]])],
bc_config: org.apache.spark.broadcast.Broadcast[NNConfig],
bc_nn_W: org.apache.spark.broadcast.Broadcast[Array[BDM[Double]]],
bc_nn_p: org.apache.spark.broadcast.Broadcast[Array[BDM[Double]]]): Array[BDM[Double]] = {
// 第n层偏导数：d(n)=-(y-a(n))*f'(z)，sigmoid函数f'(z)表达式:f'(z)=f(z)*[1-f(z)]
// sigm: d{n} = - nn.e .* (nn.a{n} .* (1 - nn.a{n}));
// {'softmax','linear'}: d{n} = - nn.e;
val train_data5 = train_nnff.map { f =>
val nn_a = f._1.nna
val error = f._1.error
val dn = ArrayBuffer[BDM[Double]]()
val nndn = bc_config.value.output_function match {
case "sigm" =>
val fz = nn_a(bc_config.value.layer - 1)
(error * (-1.0)) :* (fz :* (1.0 - fz))
case "linear" =>
error * (-1.0)
}
dn += nndn
(f._1, f._2, dn)
}
// 第n-1至第2层导数：d(n)=-(w(n)*d(n+1))*f'(z)
val train_data6 = train_data5.map { f =>
// 假设 f(z) 是sigmoid函数 f(z)=1/[1+e^(-z)]，f'(z)表达式，f'(z)=f(z)*[1-f(z)]
// 假设 f(z) tanh f(z)=1.7159*tanh(2/3.*A) ，f'(z)表达式，f'(z)=1.7159 * 2/3 * (1 - 1/(1.7159)^2 * f(z).^2)
//val di = ArrayBuffer( BDM((1.765226346140333)))
// val nn_a = ArrayBuffer[BDM[Double]]()
// val a1=BDM((1.0,0.312605257000000,0.848582961000000,0.999014768000000,0.278330771000000,0.462701179000000))
// val a2= BDM((1.0,0.838091550300577,0.996782915917104,0.118033012437165))
// val a3= BDM((2.18788852054974))
// nn_a += a1
// nn_a += a2
// nn_a += a3
val nn_a = f._1.nna
val di = f._3
val dropout = f._2
for (i <- bc_config.value.layer - 2 to 1) {
// f'(z)表达式
val nnd_act = bc_config.value.activation_function match {
case "sigm" =>
val d_act = nn_a(i) :* (1.0 - nn_a(i))
d_act
case "tanh_opt" =>
val fz2 = (1.0 - ((nn_a(i) :* nn_a(i)) * (1.0 / (1.7159 * 1.7159))))
val d_act = fz2 * (1.7159 * (2.0 / 3.0))
d_act
}
// 稀疏度惩罚误差计算:-(t/p)+(1-t)/(1-p)
// sparsityError = [zeros(size(nn.a{i},1),1) nn.nonSparsityPenalty * (-nn.sparsityTarget ./ pi + (1 - nn.sparsityTarget) ./ (1 - pi))];
val sparsityError = if (bc_config.value.nonSparsityPenalty > 0) {
val nn_pi1 = bc_nn_p.value(i)
val nn_pi2 = (bc_config.value.sparsityTarget / nn_pi1) * (-1.0) + (1.0 - bc_config.value.sparsityTarget) / (1.0 - nn_pi1)
val Bm1 = new BDM(nn_pi2.rows, 1, Array.fill(nn_pi2.rows * 1)(1.0))
val sparsity = BDM.horzcat(Bm1, nn_pi2 * bc_config.value.nonSparsityPenalty)
sparsity
} else {
val nn_pi1 = bc_nn_p.value(i)
val sparsity = BDM.zeros[Double](nn_pi1.rows, nn_pi1.cols + 1)
sparsity
}
// 导数：d(n)=-( w(n)*d(n+1)+ sparsityError )*f'(z)
// d{i} = (d{i + 1} * nn.W{i} + sparsityError) .* d_act;
val W1 = bc_nn_W.value(i)
val nndi1 = if (i + 1 == bc_config.value.layer - 1) {
//in this case in d{n} there is not the bias term to be removed
val di1 = di(i - 1)
val di2 = (di1 * W1 + sparsityError) :* nnd_act
di2
} else {
// in this case in d{i} the bias term has to be removed
val di1 = di(i - 1)(::, 1 to -1)
val di2 = (di1 * W1 + sparsityError) :* nnd_act
di2
}
// dropoutFraction
val nndi2 = if (bc_config.value.dropoutFraction > 0) {
val dropouti1 = dropout(i)
val Bm1 = new BDM(nndi1.rows: Int, 1: Int, Array.fill(nndi1.rows * 1)(1.0))
val dropouti2 = BDM.horzcat(Bm1, dropouti1)
nndi1 :* dropouti2
} else nndi1
di += nndi2
}
di += BDM.zeros(1, 1)
// 计算最终需要的偏导数值：dw(n)=(1/m)∑d(n+1)*a(n)
// nn.dW{i} = (d{i + 1}' * nn.a{i}) / size(d{i + 1}, 1);
val dw = ArrayBuffer[BDM[Double]]()
for (i <- 0 to bc_config.value.layer - 2) {
val nndW = if (i + 1 == bc_config.value.layer - 1) {
(di(bc_config.value.layer - 2 - i).t) * nn_a(i)
} else {
(di(bc_config.value.layer - 2 - i)(::, 1 to -1)).t * nn_a(i)
}
dw += nndW
}
(f._1, di, dw)
}
val train_data7 = train_data6.map(f => f._3)
// Sample a subset (fraction miniBatchFraction) of the total data
// compute and sum up the subgradients on this subset (this is one map-reduce)
val initgrad = ArrayBuffer[BDM[Double]]()
for (i <- 0 to bc_config.value.layer - 2) {
val init1 = if (i + 1 == bc_config.value.layer - 1) {
BDM.zeros[Double](bc_config.value.size(i + 1), bc_config.value.size(i) + 1)
} else {
BDM.zeros[Double](bc_config.value.size(i + 1), bc_config.value.size(i) + 1)
}
initgrad += init1
}
val (gradientSum, miniBatchSize) = train_data7.treeAggregate((initgrad, 0L))(
seqOp = (c, v) => {
// c: (grad, count), v: (grad)
val grad1 = c._1
val grad2 = v
val sumgrad = ArrayBuffer[BDM[Double]]()
for (i <- 0 to bc_config.value.layer - 2) {
val Bm1 = grad1(i)
val Bm2 = grad2(i)
val Bmsum = Bm1 + Bm2
sumgrad += Bmsum
}
(sumgrad, c._2 + 1)
},
combOp = (c1, c2) => {
// c: (grad, count)
val grad1 = c1._1
val grad2 = c2._1
val sumgrad = ArrayBuffer[BDM[Double]]()
for (i <- 0 to bc_config.value.layer - 2) {
val Bm1 = grad1(i)
val Bm2 = grad2(i)
val Bmsum = Bm1 + Bm2
sumgrad += Bmsum
}
(sumgrad, c1._2 + c2._2)
})
// 求平均值
val gradientAvg = ArrayBuffer[BDM[Double]]()
for (i <- 0 to bc_config.value.layer - 2) {
val Bm1 = gradientSum(i)
val Bmavg = Bm1 :/ miniBatchSize.toDouble
gradientAvg += Bmavg
}
gradientAvg.toArray
}
/**
* NNapplygrads是权重更新
* 权重更新
*/
def NNapplygrads(
train_nnbp: Array[BDM[Double]],
bc_config: org.apache.spark.broadcast.Broadcast[NNConfig],
bc_nn_W: org.apache.spark.broadcast.Broadcast[Array[BDM[Double]]],
bc_nn_vW: org.apache.spark.broadcast.Broadcast[Array[BDM[Double]]]): Array[Array[BDM[Double]]] = {
// nn = nnapplygrads(nn) returns an neural network structure with updated
// weights and biases
// 更新权重参数：w=w-α*[dw + λw]
val W_a = ArrayBuffer[BDM[Double]]()
val vW_a = ArrayBuffer[BDM[Double]]()
for (i <- 0 to bc_config.value.layer - 2) {
val nndwi = if (bc_config.value.weightPenaltyL2 > 0) {
val dwi = train_nnbp(i)
val zeros = BDM.zeros[Double](dwi.rows, 1)
val l2 = BDM.horzcat(zeros, dwi(::, 1 to -1))
val dwi2 = dwi + (l2 * bc_config.value.weightPenaltyL2)
dwi2
} else {
val dwi = train_nnbp(i)
dwi
}
val nndwi2 = nndwi :* bc_config.value.learningRate
val nndwi3 = if (bc_config.value.momentum > 0) {
val vwi = bc_nn_vW.value(i)
val dw3 = nndwi2 + (vwi * bc_config.value.momentum)
dw3
} else {
nndwi2
}
// nn.W{i} = nn.W{i} - dW;
W_a += (bc_nn_W.value(i) - nndwi3)
// nn.vW{i} = nn.momentum*nn.vW{i} + dW;
val nnvwi1 = if (bc_config.value.momentum > 0) {
val vwi = bc_nn_vW.value(i)
val vw3 = nndwi2 + (vwi * bc_config.value.momentum)
vw3
} else {
bc_nn_vW.value(i)
}
vW_a += nnvwi1
}
Array(W_a.toArray, vW_a.toArray)
}
/**
* nneval是进行前向传播并计算输出误差
* 计算神经网络中的每个节点的输出值，并计算平均误差;
*/
def NNeval(
batch_xy: RDD[(BDM[Double], BDM[Double])],
bc_config: org.apache.spark.broadcast.Broadcast[NNConfig],
bc_nn_W: org.apache.spark.broadcast.Broadcast[Array[BDM[Double]]]): Double = {
// NNff是进行前向传播
// nn = nnff(nn, batch_x, batch_y);
val train_nnff = NeuralNet.NNff(batch_xy, bc_config, bc_nn_W)
// error and loss
// 输出误差计算
val loss1 = train_nnff.map(f => f._1.error)
val (loss2, counte) = loss1.treeAggregate((0.0, 0L))(
seqOp = (c, v) => {
// c: (e, count), v: (m)
val e1 = c._1
val e2 = (v :* v).sum
val esum = e1 + e2
(esum, c._2 + 1)
},
combOp = (c1, c2) => {
// c: (e, count)
val e1 = c1._1
val e2 = c2._1
val esum = e1 + e2
(esum, c1._2 + c2._2)
})
val Loss = loss2 / counte.toDouble
Loss * 0.5
}
}
package NN
import breeze.linalg.{
Matrix => BM,
CSCMatrix => BSM,
DenseMatrix => BDM,
Vector => BV,
DenseVector => BDV,
SparseVector => BSV
}
import org.apache.spark.rdd.RDD
/**
* label：目标矩阵
* features：特征矩阵
* predict_label：预测矩阵
* error：误差
*/
case class PredictNNLabel(label: BDM[Double], features: BDM[Double], predict_label: BDM[Double], error: BDM[Double]) extends Serializable
/**
* NN(neural network)
*/
class NeuralNetModel(
val config: NNConfig,
val weights: Array[BDM[Double]]) extends Serializable {
/**
* 返回预测结果
* 返回格式：(label, feature, predict_label, error)
*/
def predict(dataMatrix: RDD[(BDM[Double], BDM[Double])]): RDD[PredictNNLabel] = {
val sc = dataMatrix.sparkContext
val bc_nn_W = sc.broadcast(weights)
val bc_config = sc.broadcast(config)
// NNff是进行前向传播
// nn = nnff(nn, batch_x, batch_y);
val train_nnff = NeuralNet.NNff(dataMatrix, bc_config, bc_nn_W)
val predict = train_nnff.map { f =>
val label = f._1.label
val error = f._1.error
val nnan = f._1.nna(bc_config.value.layer - 1)
val nna1 = f._1.nna(0)(::, 1 to -1)
PredictNNLabel(label, nna1, nnan, error)
}
predict
}
/**
* 计算输出误差
* 平均误差;
*/
def Loss(predict: RDD[PredictNNLabel]): Double = {
val predict1 = predict.map(f => f.error)
// error and loss
// 输出误差计算
val loss1 = predict1
val (loss2, counte) = loss1.treeAggregate((0.0, 0L))(
seqOp = (c, v) => {
// c: (e, count), v: (m)
val e1 = c._1
val e2 = (v :* v).sum
val esum = e1 + e2
(esum, c._2 + 1)
},
combOp = (c1, c2) => {
// c: (e, count)
val e1 = c1._1
val e2 = c2._1
val esum = e1 + e2
(esum, c1._2 + c2._2)
})
val Loss = loss2 / counte.toDouble
Loss * 0.5
}
}
转载请注明出处：
Spark MLlib Deep Learning Neural Net(深度学习-神经网络)1.1
标签：spark mllib 深度学习 神经网络 deep learning
原文地址：http://blog.csdn.net/sunbow0/article/details/46126479
