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‎build.sbt

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// Eclipse plugin
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EclipseKeys.useProjectId := true
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EclipseKeys.createSrc := EclipseCreateSrc.Default + EclipseCreateSrc.ManagedClasses
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EclipseKeys.executionEnvironment := Some(EclipseExecutionEnvironment.JavaSE18)
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EclipseKeys.withSource := true
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EclipseKeys.withJavadoc := true
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EclipseKeys.withBundledScalaContainers := false
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EclipseKeys.eclipseOutput := Some(".target")
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name := "dl4jtest"
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version := "0.1"
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scalaVersion in ThisBuild := "2.12.0"
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//scalaVersion := "2.12.0"
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//scalaVersion := "2.11.8"
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//resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/"
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//resolvers += "Sonatype OSS Snapshots" at "http://oss.sonatype.org/content/repositories/snapshots/"
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//resolvers += "mvnrepository" at "http://mvnrepository.com/artifact/"
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//resolvers += "Maven Central" at "http://repo1.maven.org/maven2/"
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//classpathTypes += "maven-plugin"
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//libraryDependencies += "org.scala-lang" % "scala-library" % "2.12.0"
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libraryDependencies += "org.slf4j" % "slf4j-simple" % "1.7.21"
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// Apache Common
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libraryDependencies += "org.apache.commons" % "commons-math3" % "3.6.1"
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libraryDependencies += "org.knowm.xchart" % "xchart" % "3.2.1"
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// http://bytedeco.org/
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libraryDependencies += "org.bytedeco" % "javacpp" % "1.2.4"
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libraryDependencies += "org.nd4j" % "canova-api" % "0.0.0.17"
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libraryDependencies += "org.nd4j" % "canova-nd4j-image" % "0.0.0.17"
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// fails libraryDependencies += "org.nd4j" % "nd4j-native-platform" % "0.6.0" classifier "" classifier "linux-x86_64"
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//libraryDependencies += "org.nd4j" % "nd4j-native-platform" % "0.6.0"
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//libraryDependencies += "org.nd4j" % "nd4j-cuda-7.5-platform" % "0.6.0"
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libraryDependencies += "org.nd4j" % "nd4j-cuda-8.0-platform" % "0.6.0"
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//libraryDependencies += "org.nd4j" % "nd4j-jblas" % "0.4-rc3.6" not available
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//libraryDependencies += "org.nd4j" % "nd4j-netlib-blas" % "0.4-rc3.6" not available
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// TODO
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// we want https://github.com/deeplearning4j/nd4s/tree/nd4s-0.6.0
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// https://github.com/deeplearning4j/nd4s/issues/82
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// git clone https://github.com/deeplearning4j/nd4s.git
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// cd nd4s/
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// git tag -l
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// git checkout tags/nd4s-0.6.0
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// or git checkout tags/<tag_name> -b <branch_name>
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// sbt
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// set scalaVersion := "2.12.0"
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// test:console
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// (1 to 9).asNDArray(3,3)
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libraryDependencies += "org.nd4j" % "nd4s_2.12.0-M3" % "0.4-rc3.8"
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//libraryDependencies += "org.nd4j" %% "nd4s" % "0.6.0"
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libraryDependencies += "org.datavec" % "datavec-api" % "0.6.0"
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libraryDependencies += "org.datavec" % "datavec-data-image" % "0.6.0"
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libraryDependencies += "org.datavec" % "datavec-data-codec" % "0.6.0"
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libraryDependencies += "org.datavec" % "datavec-data-audio" % "0.6.0"
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//libraryDependencies += "org.datavec" % "datavec-nd4j-common" % "0.6.0" already loaded
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libraryDependencies += "org.deeplearning4j" % "deeplearning4j-core" % "0.6.0"
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scalacOptions ++= Seq("-unchecked", "-deprecation")
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‎output/bimiodal_1.svg

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‎readme.txt

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sbt eclipse
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reload
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eclipse with-source=true
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ND4J Examples
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..............
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sbt "run-main org.nd4j.examples.MatrixOperationExample"
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sbt "run-main org.nd4j.examples.Nd4jEx1_INDArrayBasics"
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sbt "run-main org.nd4j.examples.Nd4jEx2_CreatingINDArrays"
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sbt "run-main org.nd4j.examples.Nd4jEx3_GettingAndSettingSubsets"
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sbt "run-main org.nd4j.examples.Nd4jEx4_Ops"
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sbt "run-main org.nd4j.examples.Nd4jEx5_Accumulations"
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sbt "run-main org.nd4j.examples.Nd4jEx6_BooleanIndexing"
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Checking if OpenBLAS is working
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...................................
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Instalation
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............
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sudo update-alternatives --list libblas.so
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/usr/lib/atlas-base/atlas/libblas.so
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/usr/lib/libblas/libblas.so
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/usr/lib/openblas-base/libblas.so
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root@gandalfix:/home/hmf# sudo update-alternatives --config libblas.so
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There are 3 choices for the alternative libblas.so (providing /usr/lib/libblas.so).
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Selection Path Priority Status
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------------------------------------------------------------
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* 0 /usr/lib/openblas-base/libblas.so 40 auto mode
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1 /usr/lib/atlas-base/atlas/libblas.so 35 manual mode
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2 /usr/lib/libblas/libblas.so 10 manual mode
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3 /usr/lib/openblas-base/libblas.so 40 manual mode
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Press <enter> to keep the current choice[*], or type selection number: 0
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sudo update-alternatives --config libblas.so.3
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There are 3 choices for the alternative libblas.so.3 (providing /usr/lib/libblas.so.3).
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Selection Path Priority Status
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------------------------------------------------------------
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* 0 /usr/lib/openblas-base/libblas.so.3 40 auto mode
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1 /usr/lib/atlas-base/atlas/libblas.so.3 35 manual mode
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2 /usr/lib/libblas/libblas.so.3 10 manual mode
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3 /usr/lib/openblas-base/libblas.so.3 40 manual mode
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Press <enter> to keep the current choice[*], or type selection number: 0
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sudo update-alternatives --config liblapack.so
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sudo update-alternatives --config liblapack.so.3
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Checking with R
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...............
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Start R
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root@gandalfix:/home/hmf# ps aux | grep exec/R
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Press <enter> to keep the current choice[*], or type selection number: 0
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root@gandalfix:/home/hmf# ps aux | grep exec/R
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hmf 19961 9.2 0.2 418792 36924 pts/18 Sl+ 17:55 0:00 /usr/lib/R/bin/exec/R
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root 19973 0.0 0.0 14232 1016 pts/19 S+ 17:55 0:00 grep --color=auto exec/R
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root@gandalfix:/home/hmf# lsof -p 19961 | grep 'blas\|lapack'
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lsof: WARNING: can't stat() fuse.gvfsd-fuse file system /run/user/1000/gvfs
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Output information may be incomplete.
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R 19961 hmf mem REG 8,2 6172176 18612834 /usr/lib/openblas-base/liblapack.so.3
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R 19961 hmf mem REG 8,2 31962416 18488605 /usr/lib/libopenblasp-r0.2.18.so
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R 19961 hmf mem REG 8,2 395528 18612831 /usr/lib/openblas-base/libblas.so.3
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Checking with ND4J Examples
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.............................
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sbt "run-main org.nd4j.examples.MatrixOperationExample"
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(See the line referring to BLAS)
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[info] Loading global plugins from /home/hmf/.sbt/0.13/plugins
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[info] Loading project definition from /home/hmf/tmp/dl4jtest/project
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[info] Set current project to dl4jtest (in build file:/home/hmf/tmp/dl4jtest/)
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[info] Running org.nd4j.examples.MatrixOperationExample
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[run-main-0] INFO org.nd4j.nativeblas.NativeOps - Number of threads used for NativeOps: 4
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[run-main-0] INFO org.nd4j.nativeblas.Nd4jBlas - Number of threads used for BLAS: 4
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[run-main-0] INFO org.reflections.Reflections - Reflections took 90 ms to scan 11 urls, producing 121 keys and 415 values
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[run-main-0] INFO org.reflections.Reflections - Reflections took 35 ms to scan 11 urls, producing 121 keys and 415 values
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[1.00, 2.00]
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[3.00, 4.00]
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[[1.00, 3.00],
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[2.00, 4.00]]
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[[3.00, 4.00],
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[5.00, 6.00]]
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Creating nd array with data type FLOAT
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11.00
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[13.00, 16.00]
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[[18.00, 22.00],
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[26.00, 32.00]]
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[[11.00, 25.00],
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[17.00, 39.00]]
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[[3.00, 6.00],
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[4.00, 8.00]]
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[5.00, 5.00]
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[success] Total time: 1 s, completed 15/nov/2016 10:43:35
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DL4J
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-----
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Anomaly Detection
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.................
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1. Autoencoder
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. https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/anomalydetection/MNISTAnomalyExample.java
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. sbt "run-main org.dl4j.examples.MNISTAnomalyExample"
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2. https://github.com/deeplearning4j/dl4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/unsupervised/deepbelief/DeepAutoEncoderExample.java
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. sbt "run-main org.dl4j.examples.DeepAutoEncoderExample"
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‎sbtmkdirs.sh

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#!/bin/bash
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#------------------------------------------------------------------------------
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# Name: sbtmkdirs
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# Purpose: Create an SBT project directory structure with a few simple options.
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# Author: Alvin Alexander, http://alvinalexander.com
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# Info: http://alvinalexander.com/sbtmkdirs
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# License: Creative Commons Attribution-ShareAlike 2.5 Generic
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# http://creativecommons.org/licenses/by-sa/2.5/
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#------------------------------------------------------------------------------
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declare -r TRUE=0
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declare -r FALSE=1
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# takes a string and returns true if it seems to represent "yes"
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function isYes() {
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local x=$1
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[ $x = "y" ] && echo $TRUE; return
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[ $x = "Y" ] && echo $TRUE; return
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[ $x = "yes" ] && echo $TRUE; return
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echo $FALSE
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}
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echo "This script creates an SBT project directory beneath the current directory."
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while [ $TRUE ]; do
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echo ""
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read -p "Directory/Project Name (MyFirstProject): " directoryName
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directoryName=${directoryName:-MyFirstProject}
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read -p "Create .gitignore File? (Y/n): " createGitignore
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createGitignore=${createGitignore:-y}
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read -p "Create README.md File? (Y/n): " createReadme
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createReadme=${createReadme:-y}
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echo ""
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echo "-----------------------------------------------"
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echo "Directory/Project Name: $directoryName"
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echo "Create .gitignore File?: $createGitignore"
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echo "Create README.md File?: $createReadme"
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echo "-----------------------------------------------"
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read -p "Create Project? (Y/n): " createProject
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createProject=${createProject:-y}
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[ "$(isYes $createProject)" = "$TRUE" ] && break
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done
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mkdir -p ${directoryName}/src/{main,test}/{java,resources,scala}
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mkdir ${directoryName}/lib ${directoryName}/project ${directoryName}/target
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# optional
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#mkdir -p ${directoryName}/src/main/config
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#mkdir -p ${directoryName}/src/{main,test}/{filters,assembly}
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#mkdir -p ${directoryName}/src/site
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#---------------------------------
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# create an initial build.sbt file
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#---------------------------------
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echo "name := \"$directoryName\"
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version := \"1.0\"
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scalaVersion := \"2.11.7\"" > ${directoryName}/build.sbt
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#------------------------------
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# create .gitignore, if desired
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#------------------------------
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if [ "$(isYes $createGitignore)" = "$TRUE" ]; then
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echo "bin/
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project/
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target/
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.cache
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.classpath
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.project
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.settings" > ${directoryName}/.gitignore
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fi
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#-----------------------------
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# create README.me, if desired
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#-----------------------------
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if [ "$(isYes $createReadme)" = "$TRUE" ]; then
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touch ${directoryName}/README.md
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fi
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echo ""
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echo "Project created. See the following URL for build.sbt examples:"
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echo "http://alvinalexander.com/scala/sbt-syntax-examples"

‎src/main/java/org/dl4j/examples/DeepAutoEncoderExample.java

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package org.dl4j.examples;
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import org.deeplearning4j.datasets.fetchers.MnistDataFetcher;
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import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
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import org.deeplearning4j.nn.api.OptimizationAlgorithm;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.layers.OutputLayer;
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import org.deeplearning4j.nn.conf.layers.RBM;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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import org.nd4j.linalg.dataset.DataSet;
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import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
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import org.nd4j.linalg.lossfunctions.LossFunctions;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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/**
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* ***** NOTE: This example has not been tuned. It requires additional work to produce sensible results *****
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*
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* @author Adam Gibson
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*/
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public class DeepAutoEncoderExample {
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private static Logger log = LoggerFactory.getLogger(DeepAutoEncoderExample.class);
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public static void main(String[] args) throws Exception {
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final int numRows = 28;
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final int numColumns = 28;
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int seed = 123;
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int numSamples = MnistDataFetcher.NUM_EXAMPLES;
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int batchSize = 1000;
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int iterations = 1;
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int listenerFreq = iterations/5;
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log.info("Load data....");
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DataSetIterator iter = new MnistDataSetIterator(batchSize,numSamples,true);
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log.info("Build model....");
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
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.seed(seed)
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.iterations(iterations)
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.optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
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.list()
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.layer(0, new RBM.Builder().nIn(numRows * numColumns).nOut(1000).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
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.layer(1, new RBM.Builder().nIn(1000).nOut(500).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
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.layer(2, new RBM.Builder().nIn(500).nOut(250).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
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.layer(3, new RBM.Builder().nIn(250).nOut(100).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
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.layer(4, new RBM.Builder().nIn(100).nOut(30).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build()) //encoding stops
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.layer(5, new RBM.Builder().nIn(30).nOut(100).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build()) //decoding starts
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.layer(6, new RBM.Builder().nIn(100).nOut(250).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
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.layer(7, new RBM.Builder().nIn(250).nOut(500).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
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.layer(8, new RBM.Builder().nIn(500).nOut(1000).lossFunction(LossFunctions.LossFunction.RMSE_XENT).build())
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.layer(9, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation("sigmoid").nIn(1000).nOut(numRows*numColumns).build())
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.pretrain(true).backprop(true)
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.build();
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MultiLayerNetwork model = new MultiLayerNetwork(conf);
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model.init();
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model.setListeners(new ScoreIterationListener(listenerFreq));
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log.info("Train model....");
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while(iter.hasNext()) {
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DataSet next = iter.next();
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model.fit(new DataSet(next.getFeatureMatrix(),next.getFeatureMatrix()));
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}
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}
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}

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