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Java example source code file (svmLightWorkerIRUnitTest.properties)

This example Java source code file (svmLightWorkerIRUnitTest.properties) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

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The svmLightWorkerIRUnitTest.properties Java example source code

#
# /*
#  * Copyright 2015 Skymind,Inc.
#  *
#  *    Licensed under the Apache License, Version 2.0 (the "License");
#  *    you may not use this file except in compliance with the License.
#  *    You may obtain a copy of the License at
#  *
#  *        http://www.apache.org/licenses/LICENSE-2.0
#  *
#  *    Unless required by applicable law or agreed to in writing, software
#  *    distributed under the License is distributed on an "AS IS" BASIS,
#  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  *    See the License for the specific language governing permissions and
#  *    limitations under the License.
#  */
#

# This is the path for the KnittingBoar JAR
iterativereduce.jar.path=iterativereduce-0.1-SNAPSHOT.jar

# Path to your application (which was compiled against KB!)
app.jar.path=KnittingBoar-1.0-SNAPSHOT-jar-with-dependencies.jar

# Comma separated list of other JAR's required for depenedencies
app.lib.jar.path=avro-1.7.1.jar,avro-ipc-1.7.1.jar

# Input file(s) to process

app.input.path=src/test/resources/data/svmLight/iris)svmLight_0.txt

# Output results to

app.output.path=file:///tmp/dl4j/dbn_svm_singleWorker.model

# Number of iterations
app.iteration.count=2

app.name=IR_DL4J_WorkerTest

# Requested memory for YARN clients
yarn.memory=512
# The main() class/entry for the AppMaster
yarn.master.main=org.deeplearning4j.iterativereduce.impl.multilayer.Master
# Any extra command-line args
yarn.master.args=

# The main() class/entry for the AppWorker
yarn.worker.main=org.deeplearning4j.iterativereduce.impl.multilayer.WorkerNode

# Any extra command-line args
yarn.worker.args=

org.deeplearning4j.scaleout.multilayerconf={"hiddenLayerSizes":[2,2],"confs":[{"sparsity":0.0,"useAdaGrad":true,"lr":0.10000000149011612,"corruptionLevel":0.30000001192092896,"numIterations":1000,"momentum":0.5,"l2":0.0,"useRegularization":false,"momentumAfter":null,"resetAdaGradIterations":-1,"dropOut":0.0,"applySparsity":false,"weightInit":"VI","optimizationAlgo":"CONJUGATE_GRADIENT","lossFunction":"RECONSTRUCTION_CROSSENTROPY","concatBiases":false,"constrainGradientToUnitNorm":false,"seed":123,"variables":[],"nIn":4,"nOut":3,"activationFunction":"org.nd4j.linalg.api.activation.Sigmoid","visibleUnit":"BINARY","hiddenUnit":"BINARY","k":1,"weightShape":[4,3],"filterSize":[2,2,2,2],"numFeatureMaps":2,"featureMapSize":[2,2],"stride":[2,2],"kernel":5,"batchSize":10,"rng":"org.apache.commons.math3.random.MersenneTwister","layerFactory":"org.deeplearning4j.nn.layers.factory.PretrainLayerFactory,org.deeplearning4j.hadoop.yarn.models.featuredetectors.rbm.RBM","stepFunction":"org.deeplearning4j.optimize.stepfunctions.GradientStepFunction","listeners":[],"renderWeightIterations":-1,"distclass":"org.apache.commons.math3.distribution.NormalDistribution"},{"sparsity":0.0,"useAdaGrad":true,"lr":0.10000000149011612,"corruptionLevel":0.30000001192092896,"numIterations":1000,"momentum":0.5,"l2":0.0,"useRegularization":false,"momentumAfter":null,"resetAdaGradIterations":-1,"dropOut":0.0,"applySparsity":false,"weightInit":"VI","optimizationAlgo":"CONJUGATE_GRADIENT","lossFunction":"RECONSTRUCTION_CROSSENTROPY","concatBiases":false,"constrainGradientToUnitNorm":false,"seed":123,"variables":[],"nIn":4,"nOut":3,"activationFunction":"org.nd4j.linalg.api.activation.Sigmoid","visibleUnit":"BINARY","hiddenUnit":"BINARY","k":1,"weightShape":[4,3],"filterSize":[2,2,2,2],"numFeatureMaps":2,"featureMapSize":[2,2],"stride":[2,2],"kernel":5,"batchSize":0,"rng":"org.apache.commons.math3.random.MersenneTwister","layerFactory":"org.deeplearning4j.nn.layers.factory.PretrainLayerFactory,org.deeplearning4j.hadoop.yarn.models.featuredetectors.rbm.RBM","stepFunction":"org.deeplearning4j.optimize.stepfunctions.GradientStepFunction","listeners":[],"renderWeightIterations":-1,"distclass":"org.apache.commons.math3.distribution.NormalDistribution"},{"sparsity":0.0,"useAdaGrad":true,"lr":0.10000000149011612,"corruptionLevel":0.30000001192092896,"numIterations":1000,"momentum":0.5,"l2":0.0,"useRegularization":false,"momentumAfter":null,"resetAdaGradIterations":-1,"dropOut":0.0,"applySparsity":false,"weightInit":"VI","optimizationAlgo":"CONJUGATE_GRADIENT","lossFunction":"RECONSTRUCTION_CROSSENTROPY","concatBiases":false,"constrainGradientToUnitNorm":false,"seed":123,"variables":[],"nIn":4,"nOut":3,"activationFunction":"org.nd4j.linalg.api.activation.Sigmoid","visibleUnit":"BINARY","hiddenUnit":"BINARY","k":1,"weightShape":[4,3],"filterSize":[2,2,2,2],"numFeatureMaps":2,"featureMapSize":[2,2],"stride":[2,2],"kernel":5,"batchSize":0,"rng":"org.apache.commons.math3.random.MersenneTwister","layerFactory":"org.deeplearning4j.nn.layers.factory.PretrainLayerFactory,org.deeplearning4j.hadoop.yarn.models.featuredetectors.rbm.RBM","stepFunction":"org.deeplearning4j.optimize.stepfunctions.GradientStepFunction","listeners":[],"renderWeightIterations":-1,"distclass":"org.apache.commons.math3.distribution.NormalDistribution"}],"useDropConnect":false,"useGaussNewtonVectorProductBackProp":false,"pretrain":true,"useRBMPropUpAsActivations":false,"dampingFactor":100.0}
org.deeplearning4j.features=4
org.deeplearning4j.batchSize=10
org.deeplearning4j.numberClasses=3

# Any other configuration params, will be pushed down to clients
#org.deeplearning4j.conf.RecordFactoryClassname=org.deeplearning4j.MetronomeRecordFactory
app.recordreader.class=org.canova.api.records.reader.impl.SVMLightRecordReader
org.deeplearning4j.labelindex=4

#org.deeplearning4j.conf.evaluate.dataset.path=src/test/resources/data/uci/iris/oneworker/iris_data_normalised.mne

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