Package com.github.neuralnetworks.input

Examples of com.github.neuralnetworks.input.MultipleNeuronsOutputError

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  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);

      // log data to console
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  // 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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  // 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);

  // log data
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  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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  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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  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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  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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  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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  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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  assertTrue(lc.getConnectionCalculator(l) instanceof AparapiSubsampling2D);

  l = l.getConnections().get(0).getOutputLayer();
  assertTrue(lc.getConnectionCalculator(l) instanceof AparapiSigmoid);

  bpt = TrainerFactory.backPropagation(nn, null, null, new MultipleNeuronsOutputError(), null, 0.02f, 0.5f, 0f, 0f, 0f, 1, 1, 1);
  bplc = (BackPropagationLayerCalculatorImpl) bpt.getBPLayerCalculator();

  l = nn.getInputLayer();
  assertTrue(lc.getConnectionCalculator(l) instanceof BackpropagationMaxPooling2D);
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Related Classes of com.github.neuralnetworks.input.MultipleNeuronsOutputError

  • com.github.neuralnetworks.samples.test.CifarTest
  • com.github.neuralnetworks.samples.test.IrisTest
  • com.github.neuralnetworks.samples.test.MnistTest
  • com.github.neuralnetworks.tensor.Matrix
  • com.github.neuralnetworks.test.AETest
  • com.github.neuralnetworks.test.CNNTest
  • com.github.neuralnetworks.test.RBMTest
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