apache-spark - スレッド「メイン」の例外 java.lang.NoClassDefFoundError: org/deeplearning4j/nn/conf/layers/Layer

Question

Deeplearning4j ライブラリを使用して、spark でアプリケーションを構築しようとしています。spark-submit コマンドを使用して jar (intelliJ を使用して構築) を実行するクラスターがあります。これが私のコードです

package Com.Spark.Examples

import scala.collection.mutable.ListBuffer
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.canova.api.records.reader.RecordReader
import org.canova.api.records.reader.impl.CSVRecordReader
import org.deeplearning4j.nn.api.OptimizationAlgorithm
import org.deeplearning4j.nn.conf.MultiLayerConfiguration
import org.deeplearning4j.nn.conf.NeuralNetConfiguration
import org.deeplearning4j.nn.conf.layers.DenseLayer
import org.deeplearning4j.nn.conf.layers.OutputLayer
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork
import org.deeplearning4j.nn.weights.WeightInit
import org.deeplearning4j.spark.impl.multilayer.SparkDl4jMultiLayer
import org.nd4j.linalg.lossfunctions.LossFunctions

object FeedForwardNetworkWithSpark {
  def main(args:Array[String]): Unit ={
    val recordReader:RecordReader = new CSVRecordReader(0,",")
    val conf = new SparkConf()
      .setAppName("FeedForwardNetwork-Iris")
    val sc = new SparkContext(conf)
    val numInputs:Int = 4
    val outputNum = 3
    val iterations =1
    val multiLayerConfig:MultiLayerConfiguration = new NeuralNetConfiguration.Builder()
      .seed(12345)
      .iterations(iterations)
      .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
      .learningRate(1e-1)
      .l1(0.01).regularization(true).l2(1e-3)
      .list(3)
      .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(3).activation("tanh").weightInit(WeightInit.XAVIER).build())
      .layer(1, new DenseLayer.Builder().nIn(3).nOut(2).activation("tanh").weightInit(WeightInit.XAVIER).build())
      .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER)
        .activation("softmax")
        .nIn(2).nOut(outputNum).build())
      .backprop(true).pretrain(false)
      .build
    val network:MultiLayerNetwork = new MultiLayerNetwork(multiLayerConfig)
    network.init
    network.setUpdater(null)
    val sparkNetwork:SparkDl4jMultiLayer = new
        SparkDl4jMultiLayer(sc,network)
    val nEpochs:Int = 6
    val listBuffer = new ListBuffer[Array[Float]]()
    (0 until nEpochs).foreach{i => val net:MultiLayerNetwork = sparkNetwork.fit("/user/iris.txt",4,recordReader)
      listBuffer +=(net.params.data.asFloat().clone())
      }
    println("Parameters vs. iteration Output: ")
    (0 until listBuffer.size).foreach{i =>
      println(i+"\t"+listBuffer(i).mkString)}
  }
}

これが私のbuild.sbtファイルです

name := "HWApp"

version := "0.1"

scalaVersion := "2.12.3"

libraryDependencies += "org.apache.spark" % "spark-core_2.10" % "1.6.0" % "provided"
libraryDependencies += "org.apache.spark" % "spark-mllib_2.10" % "1.6.0" % "provided"
libraryDependencies += "org.deeplearning4j" % "deeplearning4j-nlp" % "0.4-rc3.8"
libraryDependencies += "org.deeplearning4j" % "dl4j-spark" % "0.4-rc3.8"
libraryDependencies += "org.deeplearning4j" % "deeplearning4j-core" % "0.4-rc3.8"
libraryDependencies += "org.nd4j" % "nd4j-x86" % "0.4-rc3.8" % "test"
libraryDependencies += "org.nd4j" % "nd4j-api" % "0.4-rc3.8"
libraryDependencies += "org.nd4j" % "nd4j-jcublas-7.0" % "0.4-rc3.8"
libraryDependencies += "org.nd4j" % "canova-api" % "0.0.0.14"

intelliJ で自分のコードを見ると、エラーは表示されませんが、クラスターでアプリケーションを実行すると、次のような結果が得られました。

それが私に何を望んでいるのかわかりません。少しでも助けていただければ幸いです。ありがとう。