Examples of addInputModifier()

Examples of com.github.neuralnetworks.input.CSVInputProvider.addInputModifier()

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  // training and testing data providers
  String inputPath = Thread.currentThread().getContextClassLoader().getResource("IRISinput.txt").getPath();
  String targetPath = Thread.currentThread().getContextClassLoader().getResource("IRIStarget.txt").getPath();

  TrainingInputProviderImpl trainInputProvider = new CSVInputProvider(new File(inputPath), new File(targetPath));
  trainInputProvider.addInputModifier(new ScalingInputFunction(trainInputProvider));

  TrainingInputProviderImpl testInputProvider = new CSVInputProvider(new File(inputPath), new File(targetPath));
  testInputProvider.addInputModifier(new ScalingInputFunction(testInputProvider));

  OutputError outputError = new MultipleNeuronsOutputError();
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Examples of com.github.neuralnetworks.input.CSVInputProvider.addInputModifier()

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  TrainingInputProviderImpl trainInputProvider = new CSVInputProvider(new File(inputPath), new File(targetPath));
  trainInputProvider.addInputModifier(new ScalingInputFunction(trainInputProvider));

  TrainingInputProviderImpl testInputProvider = new CSVInputProvider(new File(inputPath), new File(targetPath));
  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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Examples of com.github.neuralnetworks.samples.cifar.CIFARInputProvider.CIFAR10TestingInputProvider.addInputModifier()

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  // specify your own path
  CIFAR10TestingInputProvider testInputProvider = new CIFAR10TestingInputProvider("cifar-10-batches-bin"); // specify your own path
  testInputProvider.getProperties().setGroupByChannel(true);
  testInputProvider.getProperties().setScaleColors(true);
  testInputProvider.addInputModifier(new ScalingInputFunction(255));

  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new RandomInitializerImpl(new Random(), -0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 0f, 1, 1000, 1);

  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));
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Examples of com.github.neuralnetworks.samples.cifar.CIFARInputProvider.CIFAR10TrainingInputProvider.addInputModifier()

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  NeuralNetworkImpl mlp = NNFactory.mlpSigmoid(new int[] { 3072, 10 }, true);

  CIFAR10TrainingInputProvider trainInputProvider = new CIFAR10TrainingInputProvider("cifar-10-batches-bin"); // specify your own path
  trainInputProvider.getProperties().setGroupByChannel(true);
  trainInputProvider.getProperties().setScaleColors(true);
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));

  // specify your own path
  CIFAR10TestingInputProvider testInputProvider = new CIFAR10TestingInputProvider("cifar-10-batches-bin"); // specify your own path
  testInputProvider.getProperties().setGroupByChannel(true);
  testInputProvider.getProperties().setScaleColors(true);
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Examples of com.github.neuralnetworks.samples.iris.IrisInputProvider.addInputModifier()

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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
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Examples of com.github.neuralnetworks.samples.iris.IrisInputProvider.addInputModifier()

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  // 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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Examples of com.github.neuralnetworks.samples.iris.IrisInputProvider.addInputModifier()

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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
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Examples of com.github.neuralnetworks.samples.iris.IrisInputProvider.addInputModifier()

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  // 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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Examples of com.github.neuralnetworks.samples.iris.IrisInputProvider.addInputModifier()

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  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
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Examples of com.github.neuralnetworks.samples.iris.IrisInputProvider.addInputModifier()

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  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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