Package com.github.neuralnetworks.training

Examples of com.github.neuralnetworks.training.TrainingInputProvider

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  // example 0 0 0 1 0 0 means that he has a flu vaccine. It's possible
  // to have combinations between both - for exmample 0 1 0 1 0 0 means
  // that the patient is vaccinated, but he's also coughing. We will
  // consider a patient to be sick when he has at least two of the first
  // three and healthy if he has two of the second three
  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));
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  // example 0 0 0 1 0 0 means that he has a flu vaccine. It's possible
  // to have combinations between both - for exmample 0 1 0 1 0 0 means
  // that the patient is vaccinated, but he's also coughing. We will
  // consider a patient to be sick when he has at least two of the first
  // three and healthy if he has two of the second three
  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));
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  // example 0 0 0 1 0 0 means that he has a flu vaccine. It's possible
  // to have combinations between both - for exmample 0 1 0 1 0 0 means
  // that the patient is vaccinated, but he's also coughing. We will
  // consider a patient to be sick when he has at least two of the first
  // three and healthy if he has two of the second three
  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);
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    public void testRBMCDSigmoidBP() {
  // RBM with 4 visible and 3 hidden units
  RBM rbm = NNFactory.rbm(4, 3, true);

  // training and testing input providers
  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);
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  DBN dbn = NNFactory.dbn(new int[] {4, 4, 3}, true);
  assertEquals(2, dbn.getNeuralNetworks().size(), 0);

  dbn.setLayerCalculator(NNFactory.lcSigmoid(dbn, null));

  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);
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    public void testAE() {
  // create autoencoder with visible layer with 4 neurons and hidden layer with 3 neurons
      Autoencoder ae = NNFactory.autoencoderSigmoid(4, 3, true);

      // training, testing and error
      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);
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  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);
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  // example 0 0 0 1 0 0 means that he has a flu vaccine. It's possible
  // to have combinations between both - for exmample 0 1 0 1 0 0 means
  // that the patient is vaccinated, but he's also coughing. We will
  // consider a patient to be sick when he has at least two of the first
  // three and healthy if he has two of the second three
  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();

  // backpropagation for autoencoders
  BackPropagationAutoencoder t = TrainerFactory.backPropagationAutoencoder(ae, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.7f, 0f, 0f, 0f, 1, 1, 100);
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  // example 0 0 0 1 0 0 means that he has a flu vaccine. It's possible
  // to have combinations between both - for exmample 0 1 0 1 0 0 means
  // that the patient is vaccinated, but he's also coughing. We will
  // consider a patient to be sick when he has at least two of the first
  // three and healthy if he has two of the second three
  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);
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  // example 0 0 0 1 0 0 means that he has a flu vaccine. It's possible
  // to have combinations between both - for exmample 0 1 0 1 0 0 means
  // that the patient is vaccinated, but he's also coughing. We will
  // consider a patient to be sick when he has at least two of the first
  // three and healthy if he has two of the second three
  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);
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Related Classes of com.github.neuralnetworks.training.TrainingInputProvider

  • com.github.neuralnetworks.samples.test.IrisTest
  • com.github.neuralnetworks.tensor.Tensor.TensorIterator
  • com.github.neuralnetworks.test.AETest
  • com.github.neuralnetworks.test.RBMTest
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