972fae60dc
* cleaned up bert iterator tests (#110) Signed-off-by: eraly <susan.eraly@gmail.com> * Various pre-release fixes (#111) * Various fixes Signed-off-by: AlexDBlack <blacka101@gmail.com> * Fix default dtypes for MaxPoolWithArgmax Signed-off-by: AlexDBlack <blacka101@gmail.com> * Small pre-release tweak (#112) * Log UI address on launch as in previous Play-based UI Signed-off-by: AlexDBlack <blacka101@gmail.com> * Logging level tweak for UI Signed-off-by: AlexDBlack <blacka101@gmail.com> * http not https Signed-off-by: AlexDBlack <blacka101@gmail.com> * datavec python ensure host (#113) * ensure host * one more host ensure * info->debug * [WIP] reverse improvements (#115) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * reverse draft Signed-off-by: raver119 <raver119@gmail.com> * reverse kernel Signed-off-by: raver119 <raver119@gmail.com> * reverse kernel Signed-off-by: raver119 <raver119@gmail.com> * 2 micro fixes Signed-off-by: raver119 <raver119@gmail.com> * Shugeo resize fix5 (#102) * Refactored resize images ops to use TF-like bool args as input. * Refactored helpers for cpu implementation of resize_bilinear and resize_nearest_neighbor ops. * Refactored cuda implementation for image.resize_bilinear and image.resize_nearest_neighbor ops helpers. * Refactored nearest_neighbor resize op. * Added a pair of tests for special case of resize_bilinear algorithm. * Fixed issue with resize_bilinear op. * Refactored cpu implementation for helpers with resize_nearest_neighbor op. * Final fixed for resize ops to conform TF v.1.5 * Refactored cuda helpers for resize_neares_neighbor op. * Fixed resize_bilinear to accept proper data. * Fixed issue with non-float input for resize_bilinear op. * Refactored cuda helper for resize_bilinear to proper process non-float inputs. * Added tests for resize_bilinear to int inputs. * Fixed ResizeBilinear wrapper * Tests fixed * Fixed float and bool constant to avoid overflow for some kind of compilers. * Corrected float constants with float data type. * Added f suffix for float constants. * Corrected float constant to avoid overflow with initializing lists. * Corrected float initializing list with float input. * Corrected bool constant with initalizing list. * Corrected float and bool values with initializing lists. * Fixed wrong constant. * Fixed issue with 1x1 input picture for resize. * ResizeBilinear default values on import fix Signed-off-by: raver119 <raver119@gmail.com> |
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| nd4j-backends | ||
| nd4j-buffer | ||
| nd4j-common | ||
| nd4j-context | ||
| nd4j-jdbc | ||
| nd4j-parameter-server-parent | ||
| nd4j-remote | ||
| nd4j-serde | ||
| nd4j-shade | ||
| nd4j-tensorflow | ||
| nd4j-uberjar | ||
| .appveyor.yml | ||
| .codeclimate.yml | ||
| .gitignore | ||
| .travis.yml | ||
| LICENSE | ||
| README.md | ||
| RaspberryPi.md | ||
| VERSION | ||
| buildAllversions.sh | ||
| buildmultiplescalaversions.sh | ||
| pom.xml | ||
README.md
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
-
Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
-
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.