kogecoo/dl4j-examples-scala

[NO LONGER MAINTAINED] a simple scala porting of https://github.com/deeplearning4j/dl4j-examples

Deeplearning4J Examples for Scala

This project is a simple Scala porting of deeplearning4j-example.

The main purpose of this project is:

• Show example of how to use deeplearning4j Java API from scala

• Provide a sbt project template depends on DL4J

Every examples in this demonstration directly calls Java APIs of DL4J. If you strongly want to write codes in the scala manner, you should consider to use deeplearning4s.

Note that currently dl4j-spark-examples are not merged yet.

Getting Started

This repository has some projects. To choose and run a project, followings commands are useful.

• arbiter examples

  • sbt "; project arbiter-examples; run"

• datavec examples

  • sbt "; project datavec-examples; run"

• dl4j cuda specific examples

  • sbt "; project dl4j-cuda-specific-examples; run"

• dl4j examples

  • sbt "; project dl4j-examples; run"

• dl4j spark examples

  • sbt "; project dl4j-spark; run"

• nd4j examples

  • sbt "; project nd4j-examples; run"

• rl4j examples

  • sbt "; project rl4j-examples; run"

Switch nd4j backend

To switch nd4j backend, set one of following options to nd4jBackend variable in build.sbt.

• Libs.nd4jNativePlatform (default)
• Libs.nd4jCuda75Platform
• Libs.nd4jCuda80Platform

---

Repository of Deeplearning4J neural net examples:

• MLP Neural Nets
• Convolutional Neural Nets
• Recurrent Neural Nets
• TSNE
• Word2Vec & GloVe
• Anomaly Detection

---

Documentation

For more information, check out deeplearning4j.org and its JavaDoc.

If you notice issues, please log them, and if you want to contribute, submit a pull request. Input is welcome here.

dl4j-examples-scala's Issues

Update to 0.4.0 for CUDA

I tried to use the GPU (http://deeplearning4j.org/gpu) with the nd4j-cuda-7.5 package, but it doesn't seem to work with the current scala code (at least on Windows).

The updated https://github.com/deeplearning4j/dl4j-0.4-examples/ works with cuda enabled.

Thanks!

A small error in MultiTimestepRegressionExample.scala

MultiTimestepRegressionExample.scala
line 123 and 124:

      //Run regression evaluation on our single column input
      val evaluation = new RegressionEvaluation(1)

should be:
val evaluation = new RegressionEvaluation(2)

ERROR:
Exception in thread "main" org.nd4j.linalg.exception.ND4JIllegalStateException: op.X length should be equal to op.Y length: [[2, 1, 2, 1, 1, 0, 1, 99]] != [[2, 1, 1, 1, 1, 0, 1, 99]]

`CNNLFWExample.scala` throws `NoClassDefFoundError`

Running CNNLFWExample.scala throws

Exception in thread "main" java.lang.NoClassDefFoundError: com/twelvemonkeys/imageio/spi/ImageWriterSpiBase
    at java.lang.ClassLoader.defineClass1(Native Method)
    at java.lang.ClassLoader.defineClass(ClassLoader.java:760)
    at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
    at java.net.URLClassLoader.defineClass(URLClassLoader.java:467)
    at java.net.URLClassLoader.access$100(URLClassLoader.java:73)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:368)
    at java.net.URLClassLoader$1.run(URLClassLoader.java:362)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:361)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
    at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:348)
    at java.util.ServiceLoader$LazyIterator.nextService(ServiceLoader.java:370)
    at java.util.ServiceLoader$LazyIterator.next(ServiceLoader.java:404)
    at java.util.ServiceLoader$1.next(ServiceLoader.java:480)
    at javax.imageio.spi.IIORegistry.registerApplicationClasspathSpis(IIORegistry.java:210)
    at javax.imageio.spi.IIORegistry.<init>(IIORegistry.java:138)
    at javax.imageio.spi.IIORegistry.getDefaultInstance(IIORegistry.java:159)
    at javax.imageio.ImageIO.<clinit>(ImageIO.java:66)
    at org.canova.image.recordreader.BaseImageRecordReader.<clinit>(BaseImageRecordReader.java:72)
    at org.deeplearning4j.examples.data.ImageDataPipelineExample$.main(ImageDataPipelineExample.scala:22)
    at org.deeplearning4j.examples.data.ImageDataPipelineExample.main(ImageDataPipelineExample.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at com.intellij.rt.execution.application.AppMain.main(AppMain.java:144)
Caused by: java.lang.ClassNotFoundException: com.twelvemonkeys.imageio.spi.ImageWriterSpiBase
    at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
    at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
    ... 29 more

The simple code like

val labeledPath = System.getProperty("user.home")+"/lfw"
val labels = for (f <- new File(labeledPath).listFiles().toList) yield f.getName
val recordReader = new ImageRecordReader(28, 28, true, new java.util.ArrayList(labels))
recordReader.initialize(new FileSplit(new File(labeledPath)))

does the same.

This reproduces older issue with Java examples.

The java version of the code

String labeledPath = System.getProperty("user.home")+"/lfw";
List<String> labels = new ArrayList<>();
for(File f : new File(labeledPath).listFiles()) {
    labels.add(f.getName());
}
RecordReader recordReader = new ImageRecordReader(28, 28, true, labels);
recordReader.initialize(new FileSplit(new File(labeledPath)));

in Java examples works fine but fails at Scala project although they have the same dependencies.

Strange behavior of ParagraphVectors methods

Hello,

I picked up one of your examples to train a doc2vec model. After running the piece of code below, I do not understand the logs. Whatever the document or the raw string passed in the methods nearestLabels or inferVector, it always returns me the same results which are not what I expected.

Below is the piece of code concerned:

  def makeParagraphVectors(): (ParagraphVectors, LabelAwareIterator, TokenizerFactory) = {
    val resource = new ClassPathResource("paravec/labeled")

    // build a iterator for our dataset
    val iterator = new FileLabelAwareIterator.Builder()
        .addSourceFolder(resource.getFile)
        .build()

    val it2 = new FileLabelAwareIterator.Builder()
        .addSourceFolder(resource.getFile)
        .build().asScala.toList

    val tokenizerFactory = new DefaultTokenizerFactory()
    tokenizerFactory.setTokenPreProcessor(new CommonPreprocessor())

    // ParagraphVectors training configuration
    val paragraphVectors = new ParagraphVectors.Builder()
        .learningRate(0.025)
        .minLearningRate(0.001)
        .batchSize(1000)
        .epochs(20)
        .iterate(iterator)
        .trainWordVectors(true)
        .workers(4)
        .tokenizerFactory(tokenizerFactory)
        .build()

    // Start model training
    paragraphVectors.fit()

    println(paragraphVectors.nearestLabels(it2.head, 10))
    println(paragraphVectors.nearestLabels(it2(15), 10))
    println(paragraphVectors.nearestLabels(it2(25), 10))
    println(paragraphVectors.nearestLabels("This is a test", 10))

    println(paragraphVectors.inferVector(it2.head).data())
    println(paragraphVectors.inferVector(it2(15)).data())
    println(paragraphVectors.inferVector(it2(25)).data())
    println(paragraphVectors.inferVector("This is a test").data())

    (paragraphVectors, iterator, tokenizerFactory)
  }

Here is the logs from SBT :

[info] [finance, health]
[info] [finance, health]
[info] [finance, health]
[info] [finance, health]
[info] [0.0049676113,0.0028787095,0.003321892,-0.003161574,0.0012740159,-0.0025561636,-0.0037729545,0.0031211937,6.4683676E-4,-0.0044721086,0.0032461053,0.0018189406,8.640766E-4,7.406825E-4,-0.0021893135,-0.0035865551,-0.0029473228,0.0035764086,-0.0022779887,-4.3441445E-4,0.0036415625,-2.249512E-4,-0.0020271144,-1.2530028E-4,-0.003965818,0.0035014106,0.004738228,-0.0043535195,0.001056447,0.0034712702,-0.0013758489,1.2745916E-4,0.004932679,-0.0031809532,-0.0049393554,0.0041481643,0.0030583949,1.4191866E-5,-0.004493359,-0.0041063563,-5.278683E-4,-4.500538E-4,-0.0041667484,0.0019469637,1.8355846E-5,-6.400758E-4,0.00272915,0.0014724642,-0.0038122342,7.4458716E-5,-0.0039577573,0.0025236274,0.0023528296,0.0020324527,-9.352094E-4,0.00464999,-0.0044168485,-0.0013031652,-1.313886E-4,-0.0037407083,-0.0015897963,0.004480338,-0.004225244,-0.0024998602,0.0010590708,0.0030442316,0.0030825895,-0.0020763727,-0.0041732173,-8.023769E-4,3.5224258E-4,-0.0047283247,0.0024792869,-0.0032535812,-8.4940257E-4,-0.0034813022,-0.0023037186,-0.0011500418,2.1316529E-4,-0.003058113,-0.0044661416,-1.5185654E-4,-2.1609008E-4,-3.8820563E-4,2.1732807E-4,0.0032119108,2.6806473E-4,-3.7400395E-4,-0.002717987,-0.0010265463,0.0012069005,3.8004696E-4,-0.0035895149,0.002335109,0.0034582221,0.0030708283,-0.004603518,0.0027415962,-0.0045232372,0.0036679548]
[info] [0.0049676113,0.0028787095,0.003321892,-0.003161574,0.0012740159,-0.0025561636,-0.0037729545,0.0031211937,6.4683676E-4,-0.0044721086,0.0032461053,0.0018189406,8.640766E-4,7.406825E-4,-0.0021893135,-0.0035865551,-0.0029473228,0.0035764086,-0.0022779887,-4.3441445E-4,0.0036415625,-2.249512E-4,-0.0020271144,-1.2530028E-4,-0.003965818,0.0035014106,0.004738228,-0.0043535195,0.001056447,0.0034712702,-0.0013758489,1.2745916E-4,0.004932679,-0.0031809532,-0.0049393554,0.0041481643,0.0030583949,1.4191866E-5,-0.004493359,-0.0041063563,-5.278683E-4,-4.500538E-4,-0.0041667484,0.0019469637,1.8355846E-5,-6.400758E-4,0.00272915,0.0014724642,-0.0038122342,7.4458716E-5,-0.0039577573,0.0025236274,0.0023528296,0.0020324527,-9.352094E-4,0.00464999,-0.0044168485,-0.0013031652,-1.313886E-4,-0.0037407083,-0.0015897963,0.004480338,-0.004225244,-0.0024998602,0.0010590708,0.0030442316,0.0030825895,-0.0020763727,-0.0041732173,-8.023769E-4,3.5224258E-4,-0.0047283247,0.0024792869,-0.0032535812,-8.4940257E-4,-0.0034813022,-0.0023037186,-0.0011500418,2.1316529E-4,-0.003058113,-0.0044661416,-1.5185654E-4,-2.1609008E-4,-3.8820563E-4,2.1732807E-4,0.0032119108,2.6806473E-4,-3.7400395E-4,-0.002717987,-0.0010265463,0.0012069005,3.8004696E-4,-0.0035895149,0.002335109,0.0034582221,0.0030708283,-0.004603518,0.0027415962,-0.0045232372,0.0036679548]
[info] [0.0049676113,0.0028787095,0.003321892,-0.003161574,0.0012740159,-0.0025561636,-0.0037729545,0.0031211937,6.4683676E-4,-0.0044721086,0.0032461053,0.0018189406,8.640766E-4,7.406825E-4,-0.0021893135,-0.0035865551,-0.0029473228,0.0035764086,-0.0022779887,-4.3441445E-4,0.0036415625,-2.249512E-4,-0.0020271144,-1.2530028E-4,-0.003965818,0.0035014106,0.004738228,-0.0043535195,0.001056447,0.0034712702,-0.0013758489,1.2745916E-4,0.004932679,-0.0031809532,-0.0049393554,0.0041481643,0.0030583949,1.4191866E-5,-0.004493359,-0.0041063563,-5.278683E-4,-4.500538E-4,-0.0041667484,0.0019469637,1.8355846E-5,-6.400758E-4,0.00272915,0.0014724642,-0.0038122342,7.4458716E-5,-0.0039577573,0.0025236274,0.0023528296,0.0020324527,-9.352094E-4,0.00464999,-0.0044168485,-0.0013031652,-1.313886E-4,-0.0037407083,-0.0015897963,0.004480338,-0.004225244,-0.0024998602,0.0010590708,0.0030442316,0.0030825895,-0.0020763727,-0.0041732173,-8.023769E-4,3.5224258E-4,-0.0047283247,0.0024792869,-0.0032535812,-8.4940257E-4,-0.0034813022,-0.0023037186,-0.0011500418,2.1316529E-4,-0.003058113,-0.0044661416,-1.5185654E-4,-2.1609008E-4,-3.8820563E-4,2.1732807E-4,0.0032119108,2.6806473E-4,-3.7400395E-4,-0.002717987,-0.0010265463,0.0012069005,3.8004696E-4,-0.0035895149,0.002335109,0.0034582221,0.0030708283,-0.004603518,0.0027415962,-0.0045232372,0.0036679548]
[info] [0.0049676113,0.0028787095,0.003321892,-0.003161574,0.0012740159,-0.0025561636,-0.0037729545,0.0031211937,6.4683676E-4,-0.0044721086,0.0032461053,0.0018189406,8.640766E-4,7.406825E-4,-0.0021893135,-0.0035865551,-0.0029473228,0.0035764086,-0.0022779887,-4.3441445E-4,0.0036415625,-2.249512E-4,-0.0020271144,-1.2530028E-4,-0.003965818,0.0035014106,0.004738228,-0.0043535195,0.001056447,0.0034712702,-0.0013758489,1.2745916E-4,0.004932679,-0.0031809532,-0.0049393554,0.0041481643,0.0030583949,1.4191866E-5,-0.004493359,-0.0041063563,-5.278683E-4,-4.500538E-4,-0.0041667484,0.0019469637,1.8355846E-5,-6.400758E-4,0.00272915,0.0014724642,-0.0038122342,7.4458716E-5,-0.0039577573,0.0025236274,0.0023528296,0.0020324527,-9.352094E-4,0.00464999,-0.0044168485,-0.0013031652,-1.313886E-4,-0.0037407083,-0.0015897963,0.004480338,-0.004225244,-0.0024998602,0.0010590708,0.0030442316,0.0030825895,-0.0020763727,-0.0041732173,-8.023769E-4,3.5224258E-4,-0.0047283247,0.0024792869,-0.0032535812,-8.4940257E-4,-0.0034813022,-0.0023037186,-0.0011500418,2.1316529E-4,-0.003058113,-0.0044661416,-1.5185654E-4,-2.1609008E-4,-3.8820563E-4,2.1732807E-4,0.0032119108,2.6806473E-4,-3.7400395E-4,-0.002717987,-0.0010265463,0.0012069005,3.8004696E-4,-0.0035895149,0.002335109,0.0034582221,0.0030708283,-0.004603518,0.0027415962,-0.0045232372,0.0036679548]

As you can see, the nearest labels are the same whatever the document passed to the method. I think this is due to the vectors which are exactly identical.

Could you bring me an explanation, please?

NoClassDefFoundError: org/bytedeco/javacpp/Pointer

Hi!

This is more of a usage question than a bug report:

When I try to run the examples I get this error (after it compiles successfully):
[error] (run-main-0) java.lang.NoClassDefFoundError: org/bytedeco/javacpp/Pointer java.lang.NoClassDefFoundError: org/bytedeco/javacpp/Pointer at java.lang.ClassLoader.defineClass1(Native Method) at java.lang.ClassLoader.defineClass(Unknown Source) at java.security.SecureClassLoader.defineClass(Unknown Source) at java.net.URLClassLoader.defineClass(Unknown Source)

Did I miss something in the setup? I just compiled everything and then ran it (on Windows 10 / 64bit).

Thanks!

Coding mistake in SparkLSTMCharacterExample.scala

I think this code.

SparkLSTMCharacterExample.scala
at line 319

mistake:
sb(i) = new StringBuilder(initialization)

right:
sb(i) = new StringBuilder(_initialization)

scala/collection/GenTraversableOnce$class error on run

I am getting an error when running based on your build.sbt

if i run change scala version to 2.10.4 it works

Large Variation in score

Hi, I am trying to tweak this MNIST classification example, but the score is very fluctuating in the original example. Is it a matter of concern, I believe that it must be reducing with iterations. It is like
[run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 1 is 2.3248168004290615 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 2 is 2.310877844708895 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 3 is 2.281165104066008 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 4 is 2.2738542019248174 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 5 is 2.248261843198336 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 6 is 2.24091211018936 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 7 is 2.1950365952758877 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 8 is 2.172930997642235 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 9 is 2.1635689016920234 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 10 is 2.1427176755381434 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 11 is 2.1004157344611043 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 12 is 2.084322279860774 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 13 is 2.034252318455981 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 14 is 2.0617900390407238 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 15 is 1.9633047426579575 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 16 is 1.9383670327182587 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 17 is 1.8498002376724805 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 18 is 1.8793566017154713 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 19 is 1.7586419520775582 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 20 is 1.7129728844703802 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 21 is 1.6220095317638712 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 22 is 1.6140536221970845 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 23 is 1.3905487585135181 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 24 is 1.3246678166401142 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 25 is 1.2621293949330583 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 26 is 0.9941675965591589 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 27 is 1.049686059677813 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 28 is 0.9104684911659423 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 29 is 1.1313450173581712 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 30 is 0.89999398093824 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 31 is 0.7661770332824115 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 32 is 0.49285846191402505 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 33 is 0.6619068926111996 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 34 is 0.4986666933965083 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 35 is 0.6958231542593786 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 36 is 0.5914391843687415 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 37 is 0.5103911283628144 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 38 is 0.49481729546310393 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 39 is 0.395870566086333 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 40 is 0.7600454410890629 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 41 is 0.4750480917091142 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 42 is 0.44892801291561807 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 43 is 0.5201641861817826 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 44 is 0.433935157112388 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 45 is 0.4985093856425681 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 46 is 0.9183344682526384 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 47 is 0.9333935616154833 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 48 is 0.3956761393842334 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 49 is 0.6081738161386094 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 50 is 0.6231280119496833 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 51 is 0.4086774093711775 [run-main-0] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - 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