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* #8395 Keras import - support scaled identity weight init Signed-off-by: AlexDBlack <blacka101@gmail.com> * More Keras scaled weight init fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * #8352 Deprecate duplicate SamplingDataSetIterator class Signed-off-by: AlexDBlack <blacka101@gmail.com> * Remove /O2 optimization for faster CUDA build Signed-off-by: AlexDBlack <blacka101@gmail.com> * Tweak regression test precision for CUDA Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix edge cases for buffer creation Signed-off-by: AlexDBlack <blacka101@gmail.com> * Update MKLDNN validation tests to new helper enable/disable settings Signed-off-by: AlexDBlack <blacka101@gmail.com> * Delete debugging class Signed-off-by: AlexDBlack <blacka101@gmail.com> * MKLDNN test - add proper skip for CUDA backend Signed-off-by: AlexDBlack <blacka101@gmail.com> * Align WeightInitUtil with weight init classes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix for SameDiff test layers weight init when using IWeightInit classes Signed-off-by: AlexDBlack <blacka101@gmail.com>
ND4J: Scientific Computing on the JVM
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
- Search for nd4j in the Maven Central Repository to find the available nd4j jars.
- Include the appropriate dependency in your pom.xml.
Clone from the GitHub Repo
https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source
Contribute
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Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
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If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
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Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
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Write a test, which shows that the bug was fixed or that the feature works as expected.
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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:formatin the subproject you work on, by using thecontrib/formatter.xmlat the root of the repository to configure the Eclipse formatter, or by using the INtellij plugin. -
Send a pull request, and bug us on Gitter until it gets merged and published.