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


  // create training and testing input providers
  SimpleInputProvider input = new SimpleInputProvider(new float[][] { {0, 0}, {0, 1}, {1, 0}, {1, 1} }, new float[][] { {0}, {1}, {1}, {0} });


  // create backpropagation trainer for the network
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, input, input, new XorOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.1f, 0.9f, 0f, 0f, 0f, 1, 1, 100000);


  // add logging
  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 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);
  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 } });
  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, 1, 100, false);


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


  // training

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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);
  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 } });
  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, 1, 100, true);


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


  // training

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


    @Test
    public void testRandomInitializer() {
  Environment.getInstance().setUseWeightsSharedMemory(true);
  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)) {
    Tensor t = ((FullyConnected) l.getConnections().get(0)).getWeights();
    float[] elements = t.getElements();
    t.forEach(i -> assertEquals(0.5, elements[i], 0f));
      } else {
    Tensor t = ((FullyConnected) l.getConnections().get(0)).getWeights();
    float[] elements = t.getElements();
    t.forEach(i -> assertTrue(elements[i] >= -0.1f && elements[i] <= 0.1f && elements[i] != 0));
      }
  }


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


  for (Layer l : nn.getLayers()) {
      if (Util.isBias(l)) {
    Tensor t = ((FullyConnected) l.getConnections().get(0)).getWeights();
    float[] elements = t.getElements();

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