Package com.github.neuralnetworks.input

Examples of com.github.neuralnetworks.input.MultipleNeuronsOutputError

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  MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte", 1, 2, new MnistTargetMultiNeuronOutputConverter());
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte", 1000, 1, new MnistTargetMultiNeuronOutputConverter());
  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);

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

  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, trainInputProvider, testInputProvider,  new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 1, false);

  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);
  t.train();
  t.test();
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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);

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

  Trainer<?> t = TrainerFactory.backPropagationAutoencoder(nn, trainInputProvider, testInputProvider,  new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 0f);

  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);
  t.train();
  nn.removeLayer(nn.getOutputLayer());
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  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);

  // log data
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  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte", 1000, 1, new MnistTargetMultiNeuronOutputConverter());
  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);

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

  // cpu execution mode
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  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte", 1, 1, new MnistTargetMultiNeuronOutputConverter());
  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);

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

  // cpu execution
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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);

  // log data
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  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte", 1, 1, new MnistTargetMultiNeuronOutputConverter());
  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);

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

  // cpu execution
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  // layer pre-training
  deepTrainer.train();

  // fine tuning backpropagation
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(dbn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 0f, 150, 150, 1000);

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

  // training
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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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