Example usage for org.deeplearning4j.ui.stats StatsListener StatsListener

Introduction

In this page you can find the example usage for org.deeplearning4j.ui.stats StatsListener StatsListener.

Prototype

public StatsListener(StatsStorageRouter router, int listenerFrequency) 

Source Link

Document

Create a StatsListener with network information collected every n >= 1 time steps

Usage

From source file:aiLogicImplementation.RNNBasic.java

License:Apache License

public void create(StatsStorage statsStorage) {
    NeuralNetConfiguration.Builder builder = new NeuralNetConfiguration.Builder();
    builder.iterations(1000);/*from ww w .jav  a2 s. c o  m*/
    builder.learningRate(0.01);
    builder.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT);
    builder.seed(123);
    builder.biasInit(0.2);
    builder.miniBatch(true);
    //        builder.updater(Updater.NESTEROVS);
    builder.updater(new Nesterovs(0.01));
    builder.weightInit(WeightInit.XAVIER);
    builder.regularization(true);
    builder.l2(0.001);
    //        builder.gradientNormalization(GradientNormalization.ClipL2PerParamType);
    //        builder.gradientNormalizationThreshold(0.5);

    ListBuilder listBuilder = builder.list();

    for (int i = 0; i < HIDDEN_LAYER_CONT; i++) {
        GravesLSTM.Builder hiddenLayerBuilder = new GravesLSTM.Builder();
        hiddenLayerBuilder.nIn(i == 0 ? NUMBER_OF_FEATURE_INPUT : HIDDEN_LAYER_WIDTH);
        hiddenLayerBuilder.nOut(HIDDEN_LAYER_WIDTH);
        if (i == 0) {
            hiddenLayerBuilder.activation(Activation.TANH);
        } else {
            hiddenLayerBuilder.activation(Activation.TANH);
        }
        listBuilder.layer(i, hiddenLayerBuilder.build());
    }

    //        RnnOutputLayer.Builder outputLayerBuilder = new RnnOutputLayer.Builder(LossFunction.XENT);
    RnnOutputLayer.Builder outputLayerBuilder = new RnnOutputLayer.Builder(LossFunction.L2);
    //        RnnOutputLayer.Builder outputLayerBuilder = new RnnOutputLayer.Builder(LossFunction.MCXENT);
    //        outputLayerBuilder.activation(Activation.SIGMOID);
    outputLayerBuilder.activation(Activation.RELU);
    //        outputLayerBuilder.activation(Activation.SOFTMAX);
    outputLayerBuilder.nIn(HIDDEN_LAYER_WIDTH);
    outputLayerBuilder.nOut(NUMBER_OF_FEATURE_OUTPUT);
    outputLayerBuilder.weightInit(WeightInit.XAVIER);
    //        outputLayerBuilder.dist(new UniformDistribution(0, 1));
    listBuilder.layer(HIDDEN_LAYER_CONT, outputLayerBuilder.build());
    //        listBuilder.backpropType(BackpropType.TruncatedBPTT);
    //        listBuilder.tBPTTForwardLength(tbpttLength);
    //        listBuilder.tBPTTBackwardLength(tbpttLength);

    listBuilder.pretrain(false);
    listBuilder.backprop(true);

    // create network
    MultiLayerConfiguration conf = listBuilder.build();
    net = new MultiLayerNetwork(conf);
    net.init();

    //        net.setListeners(new ScoreIterationListener(configuration.getListenerFrequency()));
    //Then add the StatsListener to collect this information from the network, as it trains
    List list = new ArrayList();
    list.add(new StatsListener(statsStorage, configuration.getListenerFrequency()));
    list.add(new ScoreIterationListener(configuration.getListenerFrequency()));
    net.setListeners(list);

    /*
       * CREATE OUR TRAINING DATA
     */
    input = Nd4j.zeros(maze.length * maze.length * hibertMaze.getHibertMazeGUI().getAlPlayers().size()
            * hibertMaze.getHibertMazeGUI().getAlFoods().size(), NUMBER_OF_FEATURE_INPUT);
    labels = Nd4j.zeros(maze.length * maze.length * hibertMaze.getHibertMazeGUI().getAlPlayers().size()
            * hibertMaze.getHibertMazeGUI().getAlFoods().size(), NUMBER_OF_FEATURE_OUTPUT);
    positionMoving = new PositionMoving(maze);
    int counter = 0;
    for (int i = 0; i < maze.length; i++) {
        for (int j = 0; j < maze[i].length; j++) {
            int mazeFieldValue = maze[i][j];
            // input neuron for current-char is 1 at "samplePos"
            for (Player player : hibertMaze.getHibertMazeGUI().getAlPlayers()) {
                for (Food food : hibertMaze.getHibertMazeGUI().getAlFoods()) {
                    Position position = new Position(i, j);
                    input.putScalar(counter, 0, i);
                    input.putScalar(counter, 1, j);
                    input.putScalar(counter, 2, mazeFieldValue);
                    input.putScalar(counter, 3, player.getPlayerNumber());
                    input.putScalar(counter, 4, i);
                    input.putScalar(counter, 5, j);
                    input.putScalar(counter, 6, food.getFoodNumber());
                    input.putScalar(counter, 7, food.getPosition().getX());
                    input.putScalar(counter, 8, food.getPosition().getY());
                    input.putScalar(counter, 9, positionMoving.moveNorth(position) ? 1.00d : 0.00d);
                    input.putScalar(counter, 10, positionMoving.moveSouth(position) ? 1.00d : 0.00d);
                    input.putScalar(counter, 11, positionMoving.moveEast(position) ? 1.00d : 0.00d);
                    input.putScalar(counter, 12, positionMoving.moveWest(position) ? 1.00d : 0.00d);
                    input.putScalar(counter, 13,
                            player.getPosition().getX() == food.getPosition().getX()
                                    && player.getPosition().getY() == food.getPosition().getY() ? 1.00d
                                            : 0.00d);

                    labels.putScalar(counter, 0, positionMoving.moveNorth(position) ? 1.00d : 0.00d);
                    labels.putScalar(counter, 1, positionMoving.moveSouth(position) ? 1.00d : 0.00d);
                    labels.putScalar(counter, 2, positionMoving.moveEast(position) ? 1.00d : 0.00d);
                    labels.putScalar(counter, 3, positionMoving.moveWest(position) ? 1.00d : 0.00d);
                    counter = counter + 1;
                }
            }
        }
    }
    trainingData = new DataSet(input, labels);
}

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