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Oleh 8fc0e63ce7 Oleh powderev (#171) 
* Libnd4j: Add broadcastable elementwise power derivative #7461 first step of Pow_bp operation implementation

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 some corrections of calculation steps

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 some bug fixes, the PowDerevative op made broadcastable, add the raw tests for op, need refactoring to use broadcast ops

* Libnd4j: Add broadcastable elementwise power derivative #7461 fixed several bugs add broadcast support and tests, need to fix scalar+array and array+scalar

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 fixed bugs for scalar inputs, fixed multinomial tests, added tests

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 fised bugs for different shapes support, tests updated

* Libnd4j: Add broadcastable elementwise power derivative #7461 applied all possible variants via tiled arrays, add support of broadcast for Pow and PowDerivative ops, covered by tests, before review have to be replaced tiled implementation by applyTrueBroadcast

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 replaced tile by broadcast implementation, fixed issue with negative x input, corrected tests, need additional testing

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 added and corrected test cases, corrected implementation need review

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 code clean up

* Libnd4j: Add broadcastable elementwise power derivative #7461 code clean up, removed some tests, add tests with scalar

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 code improvement and clean up, split tests

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative #7461 some code clean up

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* Libnd4j: Add broadcastable elementwise power derivative replace __isnanf by internal realization

Signed-off-by: Oleg <oleg.semeniv@gmail.com>

* pow_bp wrapper

* Fixed PowBp wrapper

* Tests added

* Test fixed

* Fix return type

* Disable powBp usage

* Pow backprop changed

Co-authored-by: Alexander Stoyakin <alexander.stoyakin@gmail.com>
 		2020-01-20 12:59:12 +03:00
..
ci Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
contrib Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
nd4j-backends Oleh powderev (#171) 2020-01-20 12:59:12 +03:00
nd4j-buffer String changes (#3) 2020-01-04 13:27:50 +03:00
nd4j-common Various fixes (#143) 2020-01-04 13:45:07 +11:00
nd4j-context Various fixes (#143) 2020-01-04 13:45:07 +11:00
nd4j-jdbc String changes (#3) 2020-01-04 13:27:50 +03:00
nd4j-parameter-server-parent Various fixes (#43) 2019-11-14 19:38:20 +11:00
nd4j-remote Add support for CUDA 10.2 (#89) 2019-11-29 16:31:03 +11:00
nd4j-serde J9+ -> J8 ByteBuffer fix (#59) 2019-11-20 07:43:17 +03:00
nd4j-shade nd4j-jackson: exclude java.xml.stream.XML*Factory from service loader to avoid clashes with other non-shaded jackson etc on classpath 2019-12-13 21:41:28 +11:00
nd4j-tensorflow TF Updates (#87) 2019-12-04 17:11:03 +11:00
nd4j-uberjar Add support for CUDA 10.2 (#89) 2019-11-29 16:31:03 +11:00
.appveyor.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.codeclimate.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.gitignore Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
.travis.yml Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
LICENSE Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
README.md Various fixes (#43) 2019-11-14 19:38:20 +11:00
RaspberryPi.md Update links to eclipse repos (#252) 2019-09-10 19:09:46 +10:00
VERSION Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
buildAllversions.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
buildmultiplescalaversions.sh Eclipse Migration Initial Commit 2019-06-06 15:21:15 +03:00
pom.xml Version upgrades (#199) 2019-08-30 14:35:27 +10:00

README.md

ND4J: Scientific Computing on the JVM

Join the chat at https://gitter.im/deeplearning4j/deeplearning4j Maven Central Javadoc

ND4J is an Apache 2.0-licensed scientific computing library for the JVM. By contributing code to this repository, you agree to make your contribution available under an Apache 2.0 license.

It is meant to be used in production environments rather than as a research tool, which means routines are designed to run fast with minimum RAM requirements.

Please search for the latest version on search.maven.org.

Or use the versions displayed in: https://github.com/eclipse/deeplearning4j-examples/blob/master/pom.xml

Main Features

  • Versatile n-dimensional array object
  • Multiplatform functionality including GPUs
  • Linear algebra and signal processing functions

Specifics

  • Supports GPUs via with the CUDA backend nd4j-cuda-7.5 and Native via nd4j-native.
  • All of this is wrapped in a unifying interface.
  • The API mimics the semantics of Numpy, Matlab and scikit-learn.

Documentation

Documentation is available at deeplearning4j.org. Access the JavaDocs for more detail.

Installation

To install ND4J, there are a couple of approaches, and more information can be found on the DL4J website.

Install from Maven Central

  1. Search for nd4j in the Maven Central Repository to find the available nd4j jars.
  2. Include the appropriate dependency in your pom.xml.

Clone from the GitHub Repo

https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source

Contribute

  1. Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.

  2. If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.

  3. Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).

  4. Write a test, which shows that the bug was fixed or that the feature works as expected.

  5. Note the repository follows the Google Java style with two modifications: 120-char column wrap and 4-spaces indentation. You can format your code to this format by typing mvn formatter:format in the subproject you work on, by using the contrib/formatter.xml at the root of the repository to configure the Eclipse formatter, or by using the INtellij plugin.

  6. Send a pull request, and bug us on Gitter until it gets merged and published.