Weka package for the Deeplearning4j java library
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README.md

wekaDeeplearning4j

Logo

DL4J wrapper for WEKA. Original code written by Mark Hall. This package currently introduces a new classifier, Dl4jMlpClassifier, which allows arbitrary-depth MLPs to be built with a degree of flexibility (e.g. type of weight initialisation, loss function, gradient descent algorithm, etc.).

The full documentation, giving installation instructions and getting started guides, is available here.

Weka Workbench GUI

Installation with Pre-Built Zip

The latest release provides a pre-built zip file of the package that allow easy installation via commandline

java -cp weka.jar weka.core.WekaPackageManager \
     -install-package package.zip

or via the GUI package manager as described here.

GPU Support

To add GPU support, download and run the latest install-cuda-libs.sh for Linux/Macosx or install-cuda-libs.ps1 for Windows. Make sure CUDA is installed on your system as explained here.

The install script automatically downloads the libraries and copies them into your wekaDeeplearning4j package installation. If you want to download the library zip yourself, choose the appropriate combination of your platform and CUDA version from the latest release and point the installation script to the file, e.g.:

./install-cuda.sh ~/Downloads/wekaDeeplearning4j-cuda-9.1-1.5.0-linux-x86_64.zip

Usage

Example scipts are provided in the weka-run-test-scripts directory, e.g.:

$ java -cp ${WEKA_HOME}/weka.jar weka.Run \
       .Dl4jMlpClassifier \
       -S 1 \
       -layer "weka.dl4j.layers.DenseLayer -nOut 32 -activation \"weka.dl4j.activations.ActivationReLU \" " \
       -layer "weka.dl4j.layers.OutputLayer -activation \"weka.dl4j.activations.ActivationSoftmax \" " \
       -numEpochs 10 \
       -t ../datasets/nominal/iris.arff

Documentation

The full documentation, giving installation instructions and getting started guides, is available at https://deeplearning.cms.waikato.ac.nz/.

The java documentation can be found here.

Contributing

If you want to contribute to the project, check out the contributing guide.

Misc.

Original code by Mark Hall