Examples of NNRandomInitializer

com.github.neuralnetworks.training.random.NNRandomInitializer
Random Initializer for neural networks weights - all the connections between neurons are traversed and initialized

Examples of com.github.neuralnetworks.training.random.NNRandomInitializer

    }


    @Test
    public void testRandomInitializer() {
  NeuralNetworkImpl nn = NNFactory.mlp(new int[] { 3, 2 }, true);
  NNRandomInitializer rand = new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.1f, 0.1f), 0.5f);
  rand.initialize(nn);


  for (Layer l : nn.getLayers()) {
      if (Util.isBias(l)) {
    GraphConnections gc = (GraphConnections) l.getConnections().get(0);
    for (float v : gc.getConnectionGraph().getElements()) {
        assertEquals(0.5, v, 0f);
    }
      } else {
    GraphConnections gc = (GraphConnections) l.getConnections().get(0);
    for (float v : gc.getConnectionGraph().getElements()) {
        assertTrue(v >= -0.1f && v <= 0.1f && v != 0);
    }
      }
  }


  rand = new NNRandomInitializer(new MersenneTwisterRandomInitializer(2f, 3f), new MersenneTwisterRandomInitializer(-2f, -1f));
  rand.initialize(nn);


  for (Layer l : nn.getLayers()) {
      if (Util.isBias(l)) {
    GraphConnections gc = (GraphConnections) l.getConnections().get(0);
    for (float v : gc.getConnectionGraph().getElements()) {

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer

  TrainingInputProvider trainInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, null, 1000, 1);
  TrainingInputProvider testInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, new float[][] { { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 } }, 10, 1);
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();


  // Contrastive divergence training
  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 1, false);
  t.setLayerCalculator(NNFactory.rbmSigmoidSigmoid(rbm));


  // log data
  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), true, false));

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer

  TrainingInputProvider trainInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, null, 1000, 1);
  TrainingInputProvider testInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, new float[][] { { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 } }, 10, 1);
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();


  // Persistent Contrastive divergence trainer
  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 1, true);
  t.setLayerCalculator(NNFactory.rbmSigmoidSigmoid(rbm));


  // log data
  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), true, false));

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer

  TrainingInputProvider trainInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, null, 1000, 1);
  TrainingInputProvider testInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, new float[][] { { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 } }, 10, 1);
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();


  // backpropagation for autoencoders
  BackPropagationAutoencoder t = TrainerFactory.backPropagationAutoencoder(ae, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.1f, 0.5f, 0f, 0f, 0f);


  // log data
  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), true, false));


  // sequential execution for debugging

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer

  IrisInputProvider trainInputProvider = new IrisInputProvider(150, 300000, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
  IrisInputProvider testInputProvider = new IrisInputProvider(1, 150, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
  OutputError outputError = new MultipleNeuronsOutputError();


  // trainer
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, trainInputProvider, testInputProvider, outputError, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f), 0.5f), 0.02f, 0.7f, 0f, 0f);


  // log data
  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName()));


  // early stopping

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer

  TrainingInputProvider trainInputProvider = new IrisInputProvider(1, 150000, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
  TrainingInputProvider testInputProvider = new IrisInputProvider(1, 150, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();


  // trainers
  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 1, true);


  // log data
  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName()));


  // execution mode

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer


  TrainingInputProvider trainInputProvider = new IrisInputProvider(150, 150000, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
  TrainingInputProvider testInputProvider = new IrisInputProvider(1, 150, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
 
  // rbm trainers for each layer
  AparapiCDTrainer firstTrainer = TrainerFactory.cdSigmoidTrainer(dbn.getFirstNeuralNetwork(), null, null, null, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 1, true);
  AparapiCDTrainer lastTrainer = TrainerFactory.cdSigmoidTrainer(dbn.getLastNeuralNetwork(), null, null, null, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 1, true);


  Map<NeuralNetwork, OneStepTrainer<?>> map = new HashMap<>();
  map.put(dbn.getFirstNeuralNetwork(), firstTrainer);
  map.put(dbn.getLastNeuralNetwork(), lastTrainer);


  // deep trainer
  DBNTrainer deepTrainer = TrainerFactory.dbnTrainer(dbn, map, trainInputProvider, null, null);


  Environment.getInstance().setExecutionMode(EXECUTION_MODE.SEQ);


  // layer pre-training
  deepTrainer.train();


  // fine tuning backpropagation
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(dbn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f);


  // log data
  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName()));


  // training

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer

      TrainingInputProvider trainInputProvider = new IrisInputProvider(1, 15000, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
      TrainingInputProvider testInputProvider = new IrisInputProvider(1, 150, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
      MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();


      // backpropagation autoencoder training
      BackPropagationAutoencoder bae = TrainerFactory.backPropagationAutoencoder(ae, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.25f, 0.5f, 0f, 0f, 0f);


      // log data to console
      bae.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName()));


      // execution mode

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer


  Autoencoder lastNN = sae.getLastNeuralNetwork();
  lastNN.setLayerCalculator(NNFactory.lcSigmoid(lastNN, null));


  // trainers for each of the stacked networks
  BackPropagationAutoencoder firstTrainer = TrainerFactory.backPropagationAutoencoder(firstNN, null, null, null, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.001f, 0.5f, 0f, 0f, 0f);
  BackPropagationAutoencoder secondTrainer = TrainerFactory.backPropagationAutoencoder(lastNN, null, null, null, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.001f, 0.5f, 0f, 0f, 0f);


  Map<NeuralNetwork, OneStepTrainer<?>> map = new HashMap<>();
  map.put(firstNN, firstTrainer);
  map.put(lastNN, secondTrainer);


  // data and error providers
  TrainingInputProvider trainInputProvider = new IrisInputProvider(150, 1500000, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
  TrainingInputProvider testInputProvider = new IrisInputProvider(1, 150, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();


  // deep trainer
  DNNLayerTrainer deepTrainer = TrainerFactory.dnnLayerTrainer(sae, map, trainInputProvider, testInputProvider, error);


  // execution mode
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.SEQ);


  // layerwise pre-training
  deepTrainer.train();


  // fine tuning backpropagation
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(sae, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f);


  // log data
  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName()));


  bpt.train();

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Examples of com.github.neuralnetworks.training.random.NNRandomInitializer

  MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte", 1, 1, new MnistTargetMultiNeuronOutputConverter());
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte", 1000, 1, new MnistTargetMultiNeuronOutputConverter());
  testInputProvider.addInputModifier(new ScalingInputFunction(255));


  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f);


  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));


  Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);

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