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

  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);


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


      bae.train();

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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), 0.5f), 0.02f, 0.7f, 0f, 0f, 0f, 150, 1, 2000);
  BackPropagationAutoencoder secondTrainer = TrainerFactory.backPropagationAutoencoder(lastNN, null, null, null, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f), 0.5f), 0.02f, 0.7f, 0f, 0f, 0f, 150, 1, 2000);


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


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


  // 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), 0.5f), 0.02f, 0.7f, 0f, 0f, 0f, 150, 1, 2000);


  // 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

  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);


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


  // early stopping

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

  // create training and testing input providers
  XorInputProvider trainingInput = new XorInputProvider(10000);
  XorInputProvider testingInput = new XorInputProvider(4);


  // create backpropagation trainer for the network
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, trainingInput, testingInput, new XorOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 1f, 0.5f, 0f, 0f);


  // 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

  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));


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

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

  MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
  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, 0f, 1, 1000, 1);


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


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

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

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


  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 0f, 1, 1000, 2);


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


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

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

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


  // Backpropagation trainer that also works for convolutional and subsampling layers
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(nn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f), 0.5f), 0.01f, 0.5f, 0f, 0f, 0f, 1, 1000, 1);


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


  // training

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

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


  // Backpropagation trainer that also works for convolutional and subsampling layers
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(nn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 0f, 1, 1, 1);


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


  // cpu execution

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

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


  // Backpropagation trainer that also works for convolutional and subsampling layers
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(nn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 0f, 1, 1, 1);


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


  // cpu execution

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