gist for https://github.com/deeplearning4j/deeplearning4j/issues/6326

gistfile1.txt

import java.util.ArrayList;
import java.util.List;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.dropout.GaussianNoise;
import org.deeplearning4j.nn.conf.graph.MergeVertex;
import org.deeplearning4j.nn.conf.layers.LSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.MultiDataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.lossfunctions.LossFunctions;

public class Test {
  public static void main(String[] args) throws Exception {

    ComputationGraph net = null;

    int hiddenaLayerSize = 5;
    String LSTM_LAYER = "lstm";
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
        .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
        .seed(1000)
        .gradientNormalization(GradientNormalization.RenormalizeL2PerParamType)
        .l2(1e-5)
        .dropOut(0.8)
        .updater(new Adam(3))
        .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("vectors", "ontology")
        .addVertex("merge", new MergeVertex(), "vectors", "ontology")
        // .addLayer("test", new DropoutLayer.Builder(1).build(), "merge")
        .addLayer(LSTM_LAYER,
            new Bidirectional(Bidirectional.Mode.CONCAT, new LSTM.Builder()
                .nIn(10).nOut(hiddenaLayerSize)
                .activation(Activation.TANH)
                .dropOut(new GaussianNoise(0.05))
                .build())
            ,"merge")
        .addLayer("intentOut",
            new RnnOutputLayer.Builder().activation(Activation.SOFTMAX)
            .lossFunction(LossFunctions.LossFunction.MCXENT).nIn(hiddenaLayerSize*2)
            .nOut(6).build(),
            LSTM_LAYER)
        .addLayer("neOut",
            new RnnOutputLayer.Builder().activation(Activation.SOFTMAX)
            .lossFunction(LossFunctions.LossFunction.MCXENT).nIn(hiddenaLayerSize*2)
            .nOut(4).build(),
            LSTM_LAYER)
        .setOutputs("intentOut", "neOut").build();

    net = new ComputationGraph(conf);
    net.init();
    
    List<TrainingListener> listeners = new ArrayList<>();
    listeners.add(new ScoreIterationListener(1));
    net.setListeners(
        (TrainingListener[]) listeners.toArray(new TrainingListener[listeners.size()]));

    INDArray[] features = new INDArray[2];
    features[0] = Nd4j.create(1, 5, 5);
    features[1] = Nd4j.create(1, 5, 5);
    INDArray[] labels = new INDArray[2];
    labels[0] = Nd4j.create(1, 6, 5);
    labels[1] = Nd4j.create(1, 4, 5);
    MultiDataSet mds = new org.nd4j.linalg.dataset.MultiDataSet(features, labels);

    net.fit(mds);
  }
}