Deeplearning4j xor Beispiel

Question

Ich versuche, ein XOR-Netzwerk mit deeplearning4j zu trainieren, aber ich glaube, ich habe nicht wirklich verstanden, wie man den Datensatz benutzt.

Ich wollte ein NN mit zwei Eingängen, zwei versteckten Neuronen und einem Ausgabe-Neuron erstellen.

hier ist das, was ich habe:

package org.deeplearning4j.examples.xor; 

import org.deeplearning4j.eval.Evaluation; 
import org.deeplearning4j.nn.api.Layer; 
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.distribution.UniformDistribution; 
import org.deeplearning4j.nn.conf.layers.GravesLSTM; 
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer; 
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; 
import org.deeplearning4j.nn.weights.WeightInit; 
import org.deeplearning4j.optimize.listeners.ScoreIterationListener; 
import org.nd4j.linalg.api.ndarray.INDArray; 
import org.nd4j.linalg.dataset.DataSet; 
import org.nd4j.linalg.factory.Nd4j; 
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction; 

public class XorExample { 
    public static void main(String[] args) { 

     INDArray input = Nd4j.zeros(4, 2); 
     INDArray labels = Nd4j.zeros(4, 1); 

     input.putScalar(new int[] { 0, 0 }, 0); 
     input.putScalar(new int[] { 0, 1 }, 0); 

     input.putScalar(new int[] { 1, 0 }, 1); 
     input.putScalar(new int[] { 1, 1 }, 0); 

     input.putScalar(new int[] { 2, 0 }, 0); 
     input.putScalar(new int[] { 2, 1 }, 1); 

     input.putScalar(new int[] { 3, 0 }, 1); 
     input.putScalar(new int[] { 3, 1 }, 1); 

     labels.putScalar(new int[] { 0, 0 }, 0); 
     labels.putScalar(new int[] { 1, 0 }, 1); 
     labels.putScalar(new int[] { 2, 0 }, 1); 
     labels.putScalar(new int[] { 3, 0 }, 0); 

     DataSet ds = new DataSet(input,labels); 

     //Set up network configuration: 
     MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() 
      .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1) 
      .learningRate(0.1) 
      .list(2) 
      .layer(0, new GravesLSTM.Builder().nIn(2).nOut(2) 
        .updater(Updater.RMSPROP) 
        .activation("tanh").weightInit(WeightInit.DISTRIBUTION) 
        .dist(new UniformDistribution(-0.08, 0.08)).build()) 
      .layer(1, new RnnOutputLayer.Builder(LossFunction.MCXENT).activation("softmax")  //MCXENT + softmax for classification 
        .updater(Updater.RMSPROP) 
        .nIn(2).nOut(1).weightInit(WeightInit.DISTRIBUTION) 
        .dist(new UniformDistribution(-0.08, 0.08)).build()) 
      .pretrain(false).backprop(true) 
      .build(); 

      MultiLayerNetwork net = new MultiLayerNetwork(conf); 
      net.init(); 
      net.setListeners(new ScoreIterationListener(1)); 

      //Print the number of parameters in the network (and for each layer) 
      Layer[] layers = net.getLayers(); 
      int totalNumParams = 0; 
      for(int i=0; i<layers.length; i++){ 
       int nParams = layers[i].numParams(); 
       System.out.println("Number of parameters in layer " + i + ": " + nParams); 
       totalNumParams += nParams; 
      } 
      System.out.println("Total number of network parameters: " + totalNumParams); 

      net.fit(ds); 


      Evaluation eval = new Evaluation(3); 
      INDArray output = net.output(ds.getFeatureMatrix()); 
      eval.eval(ds.getLabels(), output); 
      System.out.println(eval.stats()); 

    } 
} 

sieht die Ausgabe wie die

Mär 20, 2016 7:03:06 PM com.github.fommil.jni.JniLoader liberalLoad 
INFORMATION: successfully loaded C:\Users\LuckyPC\AppData\Local\Temp\jniloader5209513403648831212netlib-native_system-win-x86_64.dll 
Number of parameters in layer 0: 46 
Number of parameters in layer 1: 3 
Total number of network parameters: 49 
o.d.o.s.BaseOptimizer - Objective function automatically set to minimize. Set stepFunction in neural net configuration to change default settings. 
o.d.o.l.ScoreIterationListener - Score at iteration 0 is 0.6931495070457458 
Exception in thread "main" java.lang.IllegalArgumentException: Unable to getFloat row of non 2d matrix 
    at org.nd4j.linalg.api.ndarray.BaseNDArray.getRow(BaseNDArray.java:3640) 
    at org.deeplearning4j.eval.Evaluation.eval(Evaluation.java:107) 
    at org.deeplearning4j.examples.xor.XorExample.main(XorExample.java:80) 

Answer 1

Ich glaube, es ist ein XOR Beispiel direkt aus ihrem git-Repository!

Der Code ist gut dokumentiert und kann das Repository finden Sie hier: https://github.com/deeplearning4j/dl4j-0.4-examples.git

Answer 2

Hier ist eine Lösung, die ich mit aufkam.

public static void main(String[] args) throws IOException, InterruptedException { 

    CSVDataSet dataSet = new CSVDataSet(new File("./train.csv")); 
    CSVDataSetIterator trainingSetIterator = new CSVDataSetIterator(dataSet, dataSet.size()); 

    MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder() 
      .weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(0, 1)).iterations(1150) 
      .learningRate(1).seed(1) 
      .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(Updater.SGD) 
      .list(2) 
      .backprop(true).pretrain(false) 
      .layer(0, new DenseLayer.Builder().nIn(2).nOut(3).updater(Updater.SGD).build()) 
      .layer(1, new OutputLayer.Builder().nIn(3).nOut(1).build()).build(); 

    MultiLayerNetwork network = new MultiLayerNetwork(configuration); 
    network.setListeners(new HistogramIterationListener(10), new ScoreIterationListener(100)); 
    network.init(); 

    long start = System.currentTimeMillis(); 
    network.fit(trainingSetIterator); 
    System.out.println(System.currentTimeMillis() - start); 

    try(DataOutputStream dos = new DataOutputStream(Files.newOutputStream(Paths.get("xor-coefficients.bin")))){ 
     Nd4j.write(network.params(), dos); 
    } 
    FileUtils.write(new File("xor-network-conf.json"), network.getLayerWiseConfigurations().toJson()); 
} 

Zum Test:

MultiLayerConfiguration configuration = MultiLayerConfiguration.fromJson(FileUtils.readFileToString(new File("xor-network-conf.json"))); 

    try (DataInputStream dis = new DataInputStream(new FileInputStream("xor-coefficients.bin"))) { 
     INDArray parameters = Nd4j.read(dis); 

     MultiLayerNetwork network = new MultiLayerNetwork(configuration, parameters); 
     network.init(); 

     List<INDArray> inputs = ImmutableList.of(Nd4j.create(new double[]{1, 0}), 
       Nd4j.create(new double[]{0, 1}), 
       Nd4j.create(new double[]{1, 1}), 
       Nd4j.create(new double[]{0, 0})); 

     List<INDArray> networkResults = inputs.stream().map(network::output).collect(toList()); 
     System.out.println(networkResults); 
    } 
} 

mit Trainingsdaten:

0,1,1

1,0,1

1,1,0

0,0,0