Examples of com.github.neuralnetworks.training.rbm.DBNTrainer

Package com.github.neuralnetworks.training.rbm

Examples of com.github.neuralnetworks.training.rbm.DBNTrainer

com.github.neuralnetworks.training.rbm.DBNTrainer
Default implementation for training of Deep Belief Networks

    public static DNNLayerTrainer dnnLayerTrainer(DNN<?> dnn, Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error) {
  return new DNNLayerTrainer(layerTrainerProperties(dnn, layerTrainers, trainingSet, testingSet, error));
    }


    public static DBNTrainer dbnTrainer(DNN<?> dnn, Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error) {
  return new DBNTrainer(layerTrainerProperties(dnn, layerTrainers, trainingSet, testingSet, error));
    }

View Full Code Here

  Map<NeuralNetwork, OneStepTrainer<?>> map = new HashMap<>();
  map.put(dbn.getFirstNeuralNetwork(), firstTrainer);
  map.put(dbn.getLastNeuralNetwork(), lastTrainer);


  // deep trainer
  DBNTrainer deepTrainer = TrainerFactory.dbnTrainer(dbn, map, trainInputProvider, null, null);


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


  // 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);


  // log data

View Full Code Here

  Map<NeuralNetwork, OneStepTrainer<?>> map = new HashMap<>();
  map.put(dbn.getFirstNeuralNetwork(), firstTrainer);
  map.put(dbn.getLastNeuralNetwork(), lastTrainer);


  // deep trainer
  DBNTrainer deepTrainer = TrainerFactory.dbnTrainer(dbn, map, trainInputProvider, null, null);


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


  // 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

View Full Code Here

    public static DNNLayerTrainer dnnLayerTrainer(DNN<?> dnn, Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error) {
  return new DNNLayerTrainer(layerTrainerProperties(dnn, layerTrainers, trainingSet, testingSet, error));
    }


    public static DBNTrainer dbnTrainer(DNN<?> dnn, Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error) {
  return new DBNTrainer(layerTrainerProperties(dnn, layerTrainers, trainingSet, testingSet, error));
    }

View Full Code Here

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Related Classes of com.github.neuralnetworks.training.rbm.DBNTrainer

com.github.neuralnetworks.architecture.types.RBM

com.github.neuralnetworks.samples.test.IrisTest

com.github.neuralnetworks.training.TrainerFactory

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