nn

    val iterations = 500
    val seed = 456
    val listenerFreq = 1
    val batchSize = 100
    val numEx = 5000

    val server = UiServer.getInstance();
    log.info("Started on port " + server.getPort());

    log.info("Load data....")
    val iter = new GamesIterator(batchSize, numEx)
    
    val h1 = 100
    val h2 = 40
//    val h3 = 40
    val h4 = 40
    log.info("Build model....")
    val conf: MultiLayerConfiguration = new NeuralNetConfiguration.Builder()
      .seed(seed)
      .iterations(iterations)
//      .miniBatch(false)
      .learningRate(1e-3f)
//      .momentum(0.6f)
      .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
      .l1(1e-1).l2(2e-4)
      .list(3)
      .layer(0, new DenseLayer.Builder()
        .nIn(50)
        .nOut(h1)
        .weightInit(WeightInit.RELU)
        .activation("relu")
        .updater(Updater.NESTEROVS)
        .build()
    )
      .layer(1, new DenseLayer.Builder()
        .nIn(h1)
        .nOut(h2)
        .weightInit(WeightInit.RELU)
        .activation("relu")
        .updater(Updater.NESTEROVS)
        .build()
    )
    .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.RMSE_XENT)
        .nIn(h2)
        .nOut(1)
        .activation("sigmoid")
        .build()
    )
      .build()
    val model = new MultiLayerNetwork(conf)
    model.init()

    model.setListeners(new HistogramIterationListener(1), new ScoreIterationListener(listenerFreq))
    log.info("Train model....")
    model.fit(iter)