Package com.github.neuralnetworks.util

Examples of com.github.neuralnetworks.util.Properties

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  return new BackPropagationAutoencoder(p);
    }

    protected static Properties backpropProperties(NeuralNetworkImpl nn, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, float dropoutRate, int trainingBatchSize, int testBatchSize, int epochs) {
  Properties p = new Properties();
  p.setParameter(Constants.NEURAL_NETWORK, nn);
  p.setParameter(Constants.TRAINING_INPUT_PROVIDER, trainingSet);
  p.setParameter(Constants.TESTING_INPUT_PROVIDER, testingSet);
  p.setParameter(Constants.LEARNING_RATE, learningRate);
  p.setParameter(Constants.MOMENTUM, momentum);
  p.setParameter(Constants.L1_WEIGHT_DECAY, l1weightDecay);
  p.setParameter(Constants.L2_WEIGHT_DECAY, l2weightDecay);
  p.setParameter(Constants.OUTPUT_ERROR_DERIVATIVE, new MSEDerivative());
  p.setParameter(Constants.WEIGHT_UDPATES, weightUpdates(nn));
  p.setParameter(Constants.OUTPUT_ERROR, error);
  p.setParameter(Constants.RANDOM_INITIALIZER, rand);
  p.setParameter(Constants.TRAINING_BATCH_SIZE, trainingBatchSize);
  p.setParameter(Constants.TEST_BATCH_SIZE, testBatchSize);
  p.setParameter(Constants.EPOCHS, epochs);
  p.setParameter(Constants.DROPOUT_RATE, dropoutRate);

  return p;
    }
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  RBMLayerCalculator lc = NNFactory.rbmSigmoidSigmoid(rbm, trainingBatchSize);
  return new AparapiCDTrainer(rbmProperties(rbm, lc, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, gibbsSampling, trainingBatchSize, epochs, isPersistentCD));
    }

    protected static Properties rbmProperties(RBM rbm, RBMLayerCalculator lc, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, int gibbsSampling, int trainingBatchSize, int epochs, boolean isPersistentCD) {
  Properties p = new Properties();
  p.setParameter(Constants.NEURAL_NETWORK, rbm);
  p.setParameter(Constants.TRAINING_INPUT_PROVIDER, trainingSet);
  p.setParameter(Constants.TESTING_INPUT_PROVIDER, testingSet);
  p.setParameter(Constants.LEARNING_RATE, learningRate);
  p.setParameter(Constants.MOMENTUM, momentum);
  p.setParameter(Constants.L1_WEIGHT_DECAY, l1weightDecay);
  p.setParameter(Constants.L2_WEIGHT_DECAY, l2weightDecay);
  p.setParameter(Constants.GIBBS_SAMPLING_COUNT, gibbsSampling);
  p.setParameter(Constants.OUTPUT_ERROR, error);
  p.setParameter(Constants.RANDOM_INITIALIZER, rand);
  p.setParameter(Constants.PERSISTENT_CD, isPersistentCD);
  p.setParameter(Constants.LAYER_CALCULATOR, lc);
  p.setParameter(Constants.TRAINING_BATCH_SIZE, trainingBatchSize);
  p.setParameter(Constants.EPOCHS, epochs);

  return p;
    }
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  return result;
    }

    protected static Properties layerTrainerProperties(DNN<?> dnn, Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error) {
  Properties p = new Properties();
  p.setParameter(Constants.NEURAL_NETWORK, dnn);
  p.setParameter(Constants.TRAINING_INPUT_PROVIDER, trainingSet);
  p.setParameter(Constants.TESTING_INPUT_PROVIDER, testingSet);
  p.setParameter(Constants.OUTPUT_ERROR, error);
  p.setParameter(Constants.LAYER_TRAINERS, layerTrainers);

  return p;
    }
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Related Classes of com.github.neuralnetworks.util.Properties

  • com.github.neuralnetworks.architecture.NeuralNetworkImpl
  • com.github.neuralnetworks.architecture.types.NNFactory
  • com.github.neuralnetworks.training.TrainerFactory
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