java.lang.IllegalStateException: Network did not have same number of parameters as the broadcasted set parameter

Stack Overflow | graffo | 6 months ago
  1. 0

    Deeplearning4j with spark: SparkDl4jMultiLayer evaluation with JavaRDD<DataSet>

    Stack Overflow | 6 months ago | graffo
    java.lang.IllegalStateException: Network did not have same number of parameters as the broadcasted set parameter
  2. 0

    ScoreFlatMapFunction network parameters number comparision

    GitHub | 3 months ago | psuszyns
    java.lang.IllegalStateException: Network did not have same number of parameters as the broadcasted set parameters
  3. 0
    Execute mvn dependency:tree from your project's root directory.
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  5. 0
    The application is unable to connect to the database. It could be resolved by configuring access privileges on the database side.
  6. 0
    Upgrade sbt-dependency-graph to 0.8.2 or later.

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    Root Cause Analysis

    1. java.lang.IllegalStateException

      Network did not have same number of parameters as the broadcasted set parameter

      at org.deeplearning4j.spark.impl.multilayer.evaluation.EvaluateFlatMapFunction.call()
    2. org.deeplearning4j.spark
      EvaluateFlatMapFunction.call
      1. org.deeplearning4j.spark.impl.multilayer.evaluation.EvaluateFlatMapFunction.call(EvaluateFlatMapFunction.java:75)
      2. org.deeplearning4j.spark.impl.multilayer.evaluation.EvaluateFlatMapFunction.call(EvaluateFlatMapFunction.java:41)
      2 frames
    3. Spark
      Executor$TaskRunner.run
      1. org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$4$1.apply(JavaRDDLike.scala:156)
      2. org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$4$1.apply(JavaRDDLike.scala:156)
      3. org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
      4. org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$17.apply(RDD.scala:706)
      5. org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
      6. org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:297)
      7. org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
      8. org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
      9. org.apache.spark.scheduler.Task.run(Task.scala:88)
      10. org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
      10 frames
    4. Java RT
      Thread.run
      1. java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
      2. java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
      3. java.lang.Thread.run(Thread.java:745)
      3 frames