wekaDeeplearning4j

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.

Installation with Pre-Built Zips

The latest release provide pre-built zip files of the packages 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.

Usage

An example script is provided that can be run on the Iris dataset in the scripts directory.

$ java -cp $WEKA_HOME/weka.jar weka.Run \
		.Dl4jMlpClassifier \
		-S 1 \
		-layer "weka.dl4j.layers.OutputLayer \
		        -activation weka.dl4j.activations.ActivationSoftmax \
		        -lossFn weka.dl4j.lossfunctions.LossMCXENT" \
		-config "weka.dl4j.NeuralNetConfiguration \
		        -updater weka.dl4j.updater.Adam" \
		-numEpochs 10 \
		-t datasets/nominal/iris.arff \
		-split-percentage 66

Documentation

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