8.1.3 Building a Customized DeepWalk Model

You can build a DeepWalk model using customized hyper-parameters as described in the following code:

• JShell
• Java
• Python

opg4j> var model = analyst.deepWalkModelBuilder().
                setMinWordFrequency(1).
                setBatchSize(512).
                setNumEpochs(1).
                setLayerSize(100).
                setLearningRate(0.05).
                setMinLearningRate(0.0001).
                setWindowSize(3).
                setWalksPerVertex(6).
                setWalkLength(4).
                setSampleRate(0.00001).
                setNegativeSample(2).
                setValidationFraction(0.01).
                build()

DeepWalkModel model= analyst.deepWalkModelBuilder()
    .setMinWordFrequency(1)
    .setBatchSize(512)
    .setNumEpochs(1)
    .setLayerSize(100)
    .setLearningRate(0.05)
    .setMinLearningRate(0.0001)
    .setWindowSize(3)
    .setWalksPerVertex(6)
    .setWalkLength(4)
    .setSampleRate(0.00001)
    .setNegativeSample(2)
    .setValidationFraction(0.01)
    .build();

model = analyst.deepwalk_builder(min_word_frequency=1,
                                batch_size=512,num_epochs=1,
                                layer_size=100,
                                learning_rate=0.05,
                                min_learning_rate=0.0001,
                                window_size=3,
                                walks_per_vertex=6,
                                walk_length=4,
                                sample_rate=0.00001,
                                negative_sample=2,
                                validation_fraction=0.01)

See DeepWalkModelBuilder in Javadoc for more explanation for each builder operation along with the default values.