LSTM + RNN network for time series

RecurrentNets.java

public class RecurrentNets {

    private static final double learningRate = 0.1;
    private static final int seed = 12345;

    private static final int nHidden = 200;
    private static final int truncatedBPTTLength = 22;

    public static MultiLayerNetwork buildLstmNetworks(int nIn, int nOut) {
        //Set up network configuration:
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .l2(0.001)
                .weightInit(WeightInit.XAVIER)
                .updater(new RmsProp(learningRate))
                .list()
                .layer(0, new GravesLSTM.Builder().nIn(nIn).nOut(nHidden)
                        .activation(Activation.SOFTSIGN).build())
                .layer(1, new GravesLSTM.Builder().nIn(nHidden).nOut(nHidden)
                        .activation(Activation.SOFTSIGN).build())
                .layer(2, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
                        .nIn(nHidden).nOut(nOut).build())
                .backpropType(BackpropType.TruncatedBPTT).tBPTTForwardLength(truncatedBPTTLength).tBPTTBackwardLength(truncatedBPTTLength)
                .pretrain(false).backprop(true)
                .build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();

        net.setListeners(new ScoreIterationListener(100));
        return net;
    }
}