PROSAGA码农传奇-深度学习-Deeplearning4j xor示例

<div class =“post-text”itemprop =“text”>
  
    这是我提出的解决方案。
  
   <pre>
 <code>
 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());
}

</code>
 </pre>
  
    去测试：
  
   <pre>
 <code>
 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);
 }
}

</code>
 </pre>
  
    有训练数据：
  
  
    0,1,1
  
  
    1,0,1
  
  
    1,1,0
  
  
    0,0,0
  
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