Package com.github.neuralnetworks.samples.iris

Examples of com.github.neuralnetworks.samples.iris.IrisInputProvider

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    public void testMLPSigmoidBP() {
  // create the network
  NeuralNetworkImpl mlp = NNFactory.mlpSigmoid(new int[] { 4, 2, 3 }, true);

  // training and testing data providers
  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);
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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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  // create the network
  NeuralNetworkImpl mlp = NNFactory.mlpSigmoid(new int[] { 4, 2, 3 }, true);

  // training and testing data providers
  IrisInputProvider trainInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  trainInputProvider.addInputModifier(new ScalingInputFunction(trainInputProvider));
  IrisInputProvider testInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  testInputProvider.addInputModifier(new ScalingInputFunction(testInputProvider));
  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, 0f, 150, 1, 2000);
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  // RBM with 4 visible and 3 hidden units
  RBM rbm = NNFactory.rbm(4, 3, true);

  // training and testing input providers
  IrisInputProvider trainInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  trainInputProvider.addInputModifier(new ScalingInputFunction(trainInputProvider));
  IrisInputProvider testInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  testInputProvider.addInputModifier(new ScalingInputFunction(testInputProvider));
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();

  // trainers
  AparapiCDTrainer t = TrainerFactory.cdSigmoidBinaryTrainer(rbm, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 1, 1, 100, 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));

  IrisInputProvider trainInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  trainInputProvider.addInputModifier(new ScalingInputFunction(trainInputProvider));

  IrisInputProvider testInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  testInputProvider.addInputModifier(new ScalingInputFunction(testInputProvider));

  // rbm trainers for each layer
  AparapiCDTrainer firstTrainer = TrainerFactory.cdSigmoidBinaryTrainer(dbn.getFirstNeuralNetwork(), null, null, null, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 1, 150, 1000, true);
  AparapiCDTrainer lastTrainer = TrainerFactory.cdSigmoidBinaryTrainer(dbn.getLastNeuralNetwork(), null, null, null, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 1, 150, 1000, true);
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  Environment.getInstance().setUseWeightsSharedMemory(true);
  Environment.getInstance().setUseDataSharedMemory(true);
      Autoencoder ae = NNFactory.autoencoderSigmoid(4, 3, true);

      // training, testing and error
      IrisInputProvider trainInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  trainInputProvider.addInputModifier(new ScalingInputFunction(trainInputProvider));

  IrisInputProvider testInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  testInputProvider.addInputModifier(new ScalingInputFunction(testInputProvider));

      MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();

      // backpropagation autoencoder training
      BackPropagationAutoencoder bae = 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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  // create stacked autoencoder with input layer of size 4, hidden layer of the first AE with size 4 and hidden layer of the second AE with size 3
  Environment.getInstance().setUseWeightsSharedMemory(true);
  StackedAutoencoder sae = NNFactory.saeSigmoid(new int[] { 4, 4, 3 }, true);

  // data and error providers
  IrisInputProvider trainInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  trainInputProvider.addInputModifier(new ScalingInputFunction(trainInputProvider));

  IrisInputProvider testInputProvider = new IrisInputProvider(new IrisTargetMultiNeuronOutputConverter(), false);
  testInputProvider.addInputModifier(new ScalingInputFunction(testInputProvider));

  // stacked networks
  Autoencoder firstNN = sae.getFirstNeuralNetwork();
  firstNN.setLayerCalculator(NNFactory.lcSigmoid(firstNN, null));
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Related Classes of com.github.neuralnetworks.samples.iris.IrisInputProvider

  • com.github.neuralnetworks.samples.test.IrisTest
  • com.github.neuralnetworks.training.TrainingInputDataImpl
  • com.github.neuralnetworks.util.Matrix
  • java.util.Random
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