debug.java

package org.deeplearning4j.rl4j;

import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.rl4j.network.dqn.DQN;
import org.deeplearning4j.rl4j.network.dqn.DQNFactoryStdDense;
import org.deeplearning4j.rl4j.util.Constants;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;

/**
 * @author rubenfiszel ([email protected]) on 8/10/16.
 */
public class Example3 {


    public static MultiLayerNetwork buildModel() {
        NeuralNetConfiguration.ListBuilder confB = new NeuralNetConfiguration.Builder()
                .seed(Constants.NEURAL_NET_SEED)
                .iterations(1)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .learningRate(0.001)
                .updater(Updater.NESTEROVS).momentum(0.9)
                .weightInit(WeightInit.XAVIER)
                .regularization(true)
                .l2(0.01)
                .list()
                .layer(0, new DenseLayer.Builder()
                        .nIn(10)
                        .nOut(Constants.NUM_HIDDEN_NODES)
                        .activation("relu")
                        .build());



        for (int i = 1; i < 4; i++) {
            confB
                    .layer(i, new DenseLayer.Builder()
                            .nIn(Constants.NUM_HIDDEN_NODES)
                            .nOut(Constants.NUM_HIDDEN_NODES)
                            .activation("relu")
                            .build());
        }

        confB
                .layer(4, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
                        .activation("identity")
                        .nIn(Constants.NUM_HIDDEN_NODES)
                        .nOut(10)
                        .build());


        MultiLayerConfiguration mlnconf = confB.pretrain(false).backprop(true).build();
        MultiLayerNetwork model = new MultiLayerNetwork(mlnconf);
        model.init();
        model.setListeners(new ScoreIterationListener(1));
        return model;
    }


    public static void main(String[] args){

        INDArray input1 = Nd4j.readTxt("input0.txt");
        INDArray label1 = Nd4j.readTxt("label0.txt");

        INDArray input2 = Nd4j.readTxt("input1.txt");
        INDArray label2 = Nd4j.readTxt("label1.txt");

        MultiLayerNetwork mln = buildModel();
        mln.fit(input1, label1);
        mln.fit(input2, label2);
        System.out.println("lol");
    }

}