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...ain/java/streaming/dsl/mmlib/SQLAlg.scala → ...ain/java/streaming/dsl/mmlib/SQLAlg.scala
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12 changes: 12 additions & 0 deletions
12
streamingpro-commons/src/main/java/streaming/common/Md5.scala
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,12 @@ | ||
| package streaming.common | ||
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| /** | ||
| * Created by allwefantasy on 25/4/2018. | ||
| */ | ||
| object Md5 { | ||
| def md5Hash(text: String): String = java.security.MessageDigest.getInstance("MD5").digest(text.getBytes()).map(0xFF & _).map { | ||
| "%02x".format(_) | ||
| }.foldLeft("") { | ||
| _ + _ | ||
| } | ||
| } |
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92 changes: 92 additions & 0 deletions
92
streamingpro-dl4j/src/main/java/streaming/dl4j/Dl4jFunctions.scala
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| @@ -0,0 +1,92 @@ | ||
| package streaming.dl4j | ||
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| import streaming.common.HDFSOperator | ||
| import java.io.File | ||
| import java.util.Collections | ||
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| import net.csdn.common.logging.Loggers | ||
| import streaming.dsl.mmlib.algs.SQLDL4J | ||
| import org.apache.spark.ml.linalg.Vector | ||
| import org.apache.spark.sql._ | ||
| import org.deeplearning4j.eval.Evaluation | ||
| import org.deeplearning4j.nn.conf.MultiLayerConfiguration | ||
| import org.deeplearning4j.optimize.api.IterationListener | ||
| import org.deeplearning4j.optimize.listeners.ScoreIterationListener | ||
| import org.deeplearning4j.spark.impl.multilayer.SparkDl4jMultiLayer | ||
| import org.deeplearning4j.util.ModelSerializer | ||
| import org.nd4j.linalg.factory.Nd4j | ||
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| /** | ||
| * Created by allwefantasy on 25/4/2018. | ||
| */ | ||
| trait Dl4jFunctions { | ||
| val logger = Loggers.getLogger(getClass) | ||
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| def dl4jClassificationTrain(df: DataFrame, path: String, params: Map[String, String], multiLayerConfiguration: () => MultiLayerConfiguration): Unit = { | ||
| require(params.contains("featureSize"), "featureSize is required") | ||
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| val labelSize = params.getOrElse("labelSize", "-1").toInt | ||
| val batchSize = params.getOrElse("batchSize", "32").toInt | ||
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| val epochs = params.getOrElse("epochs", "1").toInt | ||
| val validateTable = params.getOrElse("validateTable", "") | ||
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| val tm = SQLDL4J.init2(df.sparkSession.sparkContext.isLocal, batchSizePerWorker = batchSize) | ||
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| val netConf = multiLayerConfiguration() | ||
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| val sparkNetwork = new SparkDl4jMultiLayer(df.sparkSession.sparkContext, netConf, tm) | ||
| sparkNetwork.setCollectTrainingStats(false) | ||
| sparkNetwork.setListeners(Collections.singletonList[IterationListener](new ScoreIterationListener(1))) | ||
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| val labelFieldName = params.getOrElse("outputCol", "label") | ||
| val newDataSetRDD = if (df.schema.fieldNames.contains(labelFieldName)) { | ||
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| require(params.contains("labelSize"), "labelSize is required") | ||
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| df.select(params.getOrElse("inputCol", "features"), params.getOrElse("outputCol", "label")).rdd.map { row => | ||
| val features = row.getAs[Vector](0) | ||
| val label = row.getAs[Vector](1) | ||
| new org.nd4j.linalg.dataset.DataSet(Nd4j.create(features.toArray), Nd4j.create(label.toArray)) | ||
| }.toJavaRDD() | ||
| } else { | ||
| df.select(params.getOrElse("inputCol", "features")).rdd.map { row => | ||
| val features = row.getAs[Vector](0) | ||
| new org.nd4j.linalg.dataset.DataSet(Nd4j.create(features.toArray), Nd4j.zeros(0)) | ||
| }.toJavaRDD() | ||
| } | ||
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| (0 until epochs).foreach { i => | ||
| sparkNetwork.fit(newDataSetRDD) | ||
| } | ||
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| val tempModelLocalPath = createTempModelLocalPath(path) | ||
| ModelSerializer.writeModel(sparkNetwork.getNetwork, new File(tempModelLocalPath, "dl4j.model"), true) | ||
| copyToHDFS(tempModelLocalPath + "/dl4j.model", path + "/dl4j.model", true) | ||
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| if (!validateTable.isEmpty) { | ||
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| val testDataSetRDD = df.sparkSession.table(validateTable).select(params.getOrElse("inputCol", "features"), params.getOrElse("outputCol", "label")).rdd.map { row => | ||
| val features = row.getAs[Vector](0) | ||
| val label = row.getAs[Vector](1) | ||
| new org.nd4j.linalg.dataset.DataSet(Nd4j.create(features.toArray), Nd4j.create(label.toArray)) | ||
| }.toJavaRDD() | ||
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| val evaluation = sparkNetwork.doEvaluation(testDataSetRDD, batchSize, new Evaluation(labelSize))(0); //Work-around for 0.9.1 bug: see https://deeplearning4j.org/releasenotes | ||
| logger.info("***** Evaluation *****") | ||
| logger.info(evaluation.stats()) | ||
| logger.info("***** Example Complete *****") | ||
| } | ||
| tm.deleteTempFiles(df.sparkSession.sparkContext) | ||
| } | ||
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| def copyToHDFS(tempModelLocalPath: String, path: String, clean: Boolean) = { | ||
| HDFSOperator.copyToHDFS(tempModelLocalPath, path, clean) | ||
| } | ||
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| def createTempModelLocalPath(path: String, autoCreateParentDir: Boolean = true) = { | ||
| HDFSOperator.createTempModelLocalPath(path, autoCreateParentDir) | ||
| } | ||
| } |
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11 changes: 4 additions & 7 deletions
11
...ming/dsl/mmlib/algs/dl4j/FCClassify.scala → ...ming/dsl/mmlib/algs/dl4j/FCClassify.scala
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13 changes: 2 additions & 11 deletions
13
...g/dsl/mmlib/algs/dl4j/SDAutoencoder.scala → ...g/dsl/mmlib/algs/dl4j/SDAutoencoder.scala
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4 changes: 1 addition & 3 deletions
4
streamingpro-spark-2.0/src/main/java/org/apache/spark/ps/cluster/PSExecutorBackend.scala
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