Spark MLlib Deep Learning Convolution Neural Network (深度学习-卷积神经网络)3.1

最新推荐文章于 2024-01-21 03:41:21 发布

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最新推荐文章于 2024-01-21 03:41:21 发布 · 6.7k 阅读

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#spark #mllib #深度学习 #卷积神经网络 #Convolution Neural N

3、Spark MLlib Deep Learning Convolution Neural Network (深度学习-卷积神经网络)3.1

http://blog.youkuaiyun.com/sunbow0

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)

1、源码

2、源码解析

3、实例

第三章Convolution Neural Network(CNN)

1、源码

2、源码解析

3、实例

第四章 Stacked Auto-Encoders(SAE)

第五章CAE

第三章Convolution Neural Network (卷积神经网络)

1 源码

目前SparkMLlib Deep Learning工具箱源码的github地址为：

https://github.com/sunbow1/SparkMLlibDeepLearn

1.1 CNN代码

package CNN

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,
  accumulate => Accumulate,
  rot90 => Rot90,
  sum => Bsum
}
import breeze.numerics.{
  exp => Bexp,
  tanh => Btanh
}

import scala.collection.mutable.ArrayBuffer
import java.util.Random
import scala.math._

/**
 * types：网络层类别
 * outputmaps：特征map数量
 * kernelsize：卷积核k大小
 * k: 卷积核
 * b: 偏置
 * dk: 卷积核的偏导
 * db: 偏置的偏导
 * scale: pooling大小
 */
case class CNNLayers(
  types: String,
  outputmaps: Double,
  kernelsize: Double,
  scale: Double,
  k: Array[Array[BDM[Double]]],
  b: Array[Double],
  dk: Array[Array[BDM[Double]]],
  db: Array[Double]) extends Serializable

/**
 * CNN(convolution neural network)卷积神经网络
 */

class CNN(
  private var mapsize: BDM[Double],
  private var types: Array[String],
  private var layer: Int,
  private var onum: Int,
  private var outputmaps: Array[Double],
  private var kernelsize: Array[Double],
  private var scale: Array[Double],
  private var alpha: Double,
  private var batchsize: Double,
  private var numepochs: Double) extends Serializable with Logging {
//        var mapsize = new BDM(1, 2, Array(28.0, 28.0))
//        var types = Array("i", "c", "s", "c", "s")
//        var layer = 5
//        var onum = 10  
//        var outputmaps = Array(0.0, 6.0, 0.0, 12.0, 0.0)
//        var kernelsize = Array(0.0, 5.0, 0.0, 5.0, 0.0)
//        var scale = Array(0.0, 0.0, 2.0, 0.0, 2.0)
//        var alpha = 1.0
//        var batchsize = 50.0
//        var numepochs = 1.0

  def this() = this(new BDM(1, 2, Array(28.0, 28.0)),
    Array("i", "c", "s", "c", "s"), 5, 10,
    Array(0.0, 6.0, 0.0, 12.0, 0.0),
    Array(0.0, 5.0, 0.0, 5.0, 0.0),
    Array(0.0, 0.0, 2.0, 0.0, 2.0),
    1.0, 50.0, 1.0)

  /** 设置输入层大小. Default: [28, 28]. */
  def setMapsize(mapsize: BDM[Double]): this.type = {
    this.mapsize = mapsize
    this
  }

  /** 设置网络层类别. Default: [1"i", "c", "s", "c", "s"]. */
  def setTypes(types: Array[String]): this.type = {
    this.types = types
    this
  }

  /** 设置网络层数. Default: 5. */
  def setLayer(layer: Int): this.type = {
    this.layer = layer
    this
  }

  /** 设置输出维度. Default: 10. */
  def setOnum(onum: Int): this.type = {
    this.onum = onum
    this
  }

  /** 设置特征map数量. Default: [0.0, 6.0, 0.0, 12.0, 0.0]. */
  def setOutputm