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