org.deeplearning4j.nn.multilayer.MultiLayerNetwork.setListeners()方法的使用及代码示例

x33g5p2x  于2022-01-25 转载在 其他  
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本文整理了Java中org.deeplearning4j.nn.multilayer.MultiLayerNetwork.setListeners()方法的一些代码示例，展示了MultiLayerNetwork.setListeners()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。MultiLayerNetwork.setListeners()方法的具体详情如下：
包路径：org.deeplearning4j.nn.multilayer.MultiLayerNetwork
类名称：MultiLayerNetwork
方法名：setListeners

MultiLayerNetwork.setListeners介绍

暂无

代码示例

代码示例来源：origin: deeplearning4j/dl4j-examples

network.setListeners(new StatsListener(statsStorage), new ScoreIterationListener(10));

代码示例来源：origin: deeplearning4j/dl4j-examples

net.setListeners(new PerformanceListener(10, true));

代码示例来源：origin: deeplearning4j/dl4j-examples

model.setListeners(new ScoreIterationListener(100));
long timeX = System.currentTimeMillis();

代码示例来源：origin: deeplearning4j/dl4j-examples

model.setListeners(new ScoreIterationListener(100));
long timeX = System.currentTimeMillis();

代码示例来源：origin: deeplearning4j/dl4j-examples

model.init();
model.setListeners(new ScoreIterationListener(100));

代码示例来源：origin: deeplearning4j/dl4j-examples

net.setListeners(new ScoreIterationListener(1), new IterationListener() {
  @Override
  public void iterationDone(Model model, int iteration, int epoch) {

代码示例来源：origin: deeplearning4j/dl4j-examples

net.setListeners(new ScoreIterationListener(1));

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

@Override
public void setListeners(IterationListener... listeners) {
  Collection<IterationListener> cListeners = new ArrayList<>();
  //Check: user might have done setListeners(null) thinking this would clear the current listeners.
  //This results in an IterationListener[1] with a single null value -> results in a NPE later
  if (listeners != null && listeners.length > 0) {
    for (IterationListener i : listeners) {
      if (i != null)
        cListeners.add(i);
    }
  }
  setListeners(cListeners);
}

代码示例来源：origin: org.deeplearning4j/deeplearning4j-nn

/**
 * This method ADDS additional IterationListener to existing listeners
 *
 * @param listeners
 */
@Override
public void addListeners(IterationListener... listeners) {
  if (this.listeners == null) {
    setListeners(listeners);
    return;
  }
  for (IterationListener listener : listeners) {
    this.listeners.add(listener);
    if (listener instanceof TrainingListener) {
      this.trainingListeners.add((TrainingListener) listener);
    }
  }
  if (solver != null) {
    solver.setListeners(this.listeners);
  }
}

代码示例来源：origin: sjsdfg/dl4j-tutorials

public static void main(final String[] args){
  //Switch these two options to do different functions with different networks
  final MathFunction fn = new SinXDivXMathFunction();
  final MultiLayerConfiguration conf = getDeepDenseLayerNetworkConfiguration();
  //Generate the training data
  final INDArray x = Nd4j.linspace(-10,10,nSamples).reshape(nSamples, 1);
  final DataSetIterator iterator = getTrainingData(x,fn,batchSize,rng);
  //Create the network
  final MultiLayerNetwork net = new MultiLayerNetwork(conf);
  net.init();
  net.setListeners(new ScoreIterationListener(1));
  //Train the network on the full data set, and evaluate in periodically
  final INDArray[] networkPredictions = new INDArray[nEpochs/ plotFrequency];
  for( int i=0; i<nEpochs; i++ ){
    iterator.reset();
    net.fit(iterator);
    if((i+1) % plotFrequency == 0) networkPredictions[i/ plotFrequency] = net.output(x, false);
  }
  //Plot the target data and the network predictions
  plot(fn,x,fn.getFunctionValues(x),networkPredictions);
}

代码示例来源：origin: apache/opennlp-sandbox

public static MultiLayerNetwork train(WordVectors wordVectors, ObjectStream<NameSample> samples,
                   int epochs, int windowSize, String[] labels) throws IOException {
 int vectorSize = 300;
 int layerSize = 256;
 MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
   .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
   .updater(new RmsProp(0.01)).l2(0.001)
   .weightInit(WeightInit.XAVIER)
   .list()
   .layer(0, new GravesLSTM.Builder().nIn(vectorSize).nOut(layerSize)
     .activation(Activation.TANH).build())
   .layer(1, new RnnOutputLayer.Builder().activation(Activation.SOFTMAX)
     .lossFunction(LossFunctions.LossFunction.MCXENT).nIn(layerSize).nOut(3).build())
   .pretrain(false).backprop(true).build();
 MultiLayerNetwork net = new MultiLayerNetwork(conf);
 net.init();
 net.setListeners(new ScoreIterationListener(5));
 // TODO: Extract labels on the fly from the data
 DataSetIterator train = new NameSampleDataSetIterator(samples, wordVectors, windowSize, labels);
 System.out.println("Starting training");
 for (int i = 0; i < epochs; i++) {
  net.fit(train);
  train.reset();
  System.out.println(String.format("Finished epoch %d", i));
 }
 return net;
}

代码示例来源：origin: rahul-raj/Deeplearning4J

model.init();
model.setListeners(new ScoreIterationListener(100));

代码示例来源：origin: sjsdfg/dl4j-tutorials

public static void main(String[] args){
  //Generate the training data
  DataSetIterator iterator = getTrainingData(batchSize,rng);
  //Create the network
  int numInput = 2;
  int numOutputs = 1;
  MultiLayerNetwork net = new MultiLayerNetwork(new NeuralNetConfiguration.Builder()
      .seed(seed)
      .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
      .weightInit(WeightInit.XAVIER)
      .updater(new Sgd(learningRate))
      .list()
      .layer(0, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
          .activation(Activation.IDENTITY)
          .nIn(numInput).nOut(numOutputs).build())
      .pretrain(false).backprop(true).build()
  );
  net.init();
  net.setListeners(new ScoreIterationListener(1));
  for( int i=0; i<nEpochs; i++ ){
    iterator.reset();
    net.fit(iterator);
  }
  final INDArray input = Nd4j.create(new double[] { 0.111111, 0.3333333333333 }, new int[] { 1, 2 });
  INDArray out = net.output(input, false);
  System.out.println(out);
}

代码示例来源：origin: mccorby/FederatedAndroidTrainer

model.setListeners(mIterationListener);  //print the score with every iteration

代码示例来源：origin: apache/opennlp-sandbox

net.setListeners(new ScoreIterationListener(1));
return net;

代码示例来源：origin: sjsdfg/dl4j-tutorials

);
net.init();
net.setListeners(new ScoreIterationListener(1));

代码示例来源：origin: sjsdfg/dl4j-tutorials

public static void main(String[] args) {
  List<Data> data = readFile("");
  RegIterator trainIter = new RegIterator(data, 1, 5, 5);
  // 构建模型
  MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
      .seed(1234)
      .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
      .weightInit(WeightInit.XAVIER)
      .updater(new Nesterovs(0.01, 0.9))
      .list().layer(0, new GravesLSTM.Builder().activation(Activation.TANH).nIn(1).nOut(32)
          .build())
      .layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE)
          .activation(Activation.IDENTITY).nIn(32).nOut(1).build())
      .build();
  MultiLayerNetwork network = new MultiLayerNetwork(conf);
  network.setListeners(new ScoreIterationListener(1));
  network.init();
  int epoch = 10;
  for (int i = 0; i < epoch; i++) {
    while (trainIter.hasNext()) {
      DataSet dataSets = trainIter.next();
      network.fit(dataSets);
    }
    trainIter.reset();
  }
}

代码示例来源：origin: mccorby/FederatedAndroidTrainer

public void buildModel() {
  if (model == null) {
    int iterations = 1000;
    long seed = 6;
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
        .seed(seed)
        .iterations(iterations)
        .activation(Activation.TANH)
        .weightInit(WeightInit.XAVIER)
        .learningRate(0.1)
        .regularization(true).l2(1e-4)
        .list()
        .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(3)
            .build())
        .layer(1, new DenseLayer.Builder().nIn(3).nOut(3)
            .build())
        .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MEAN_SQUARED_LOGARITHMIC_ERROR)
            .activation(Activation.SOFTMAX)
            .nIn(3).nOut(numClasses).build())
        .backprop(true).pretrain(false)
        .build();
    //run the model
    model = new MultiLayerNetwork(conf);
    model.init();
    model.setListeners(iterationListener);
  }
}

代码示例来源：origin: mccorby/FederatedAndroidTrainer

public void buildModel() {
  if (model == null) {
    int iterations = 1000;
    long seed = 6;
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
        .seed(seed)
        .iterations(iterations)
        .activation(Activation.TANH)
        .weightInit(WeightInit.XAVIER)
        .learningRate(0.1)
        .regularization(true).l2(1e-4)
        .list()
        .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(3)
            .build())
        .layer(1, new DenseLayer.Builder().nIn(3).nOut(3)
            .build())
        .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
            .activation(Activation.SOFTMAX)
            .nIn(3).nOut(numClasses).build())
        .backprop(true).pretrain(false)
        .build();
    //run the model
    model = new MultiLayerNetwork(conf);
    model.init();
    model.setListeners(iterationListener);
  }
}

代码示例来源：origin: mccorby/FederatedAndroidTrainer

public void buildModel() {
  //Create the network
  int numInput = 2;
  int numOutputs = 1;
  int nHidden = 10;
  mNetwork = new MultiLayerNetwork(new NeuralNetConfiguration.Builder()
      .seed(mSeed)
      .iterations(ITERATIONS)
      .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
      .learningRate(LEARNING_RATE)
      .weightInit(WeightInit.XAVIER)
      .updater(Updater.NESTEROVS)
      .list()
      .layer(0, new DenseLayer.Builder().nIn(numInput).nOut(nHidden)
          .activation(Activation.TANH)
          .name("input")
          .build())
      .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
          .activation(Activation.IDENTITY)
          .name("output")
          .nIn(nHidden).nOut(numOutputs).build())
      .pretrain(false)
      .backprop(true)
      .build()
  );
  mNetwork.init();
  mNetwork.setListeners(mIterationListener);
}

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