Examples of com.github.neuralnetworks.training.backpropagation.BackPropagationAutoencoder$AutoencoderTrainingInputrovider

Package com.github.neuralnetworks.training.backpropagation

Examples of com.github.neuralnetworks.training.backpropagation.BackPropagationAutoencoder$AutoencoderTrainingInputrovider

com.github.neuralnetworks.training.backpropagation.BackPropagationAutoencoder
BackPropagation for autoencoders (input and target are the same). Supports denoising autoencoders.


  return blc;
    }


    public static BackPropagationAutoencoder backPropagationAutoencoder(NeuralNetworkImpl nn, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, float inputCorruptionRate) {
  BackPropagationAutoencoder t = new BackPropagationAutoencoder(backpropProperties(nn, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay));


  BackPropagationLayerCalculatorImpl bplc = bplc(nn, t.getProperties());
  t.getProperties().setParameter(Constants.BACKPROPAGATION, bplc);


  return t;
    }

View Full Code Here

  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, 1000, 1);
  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 } }, 10, 1);
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();


  // backpropagation for autoencoders
  BackPropagationAutoencoder t = TrainerFactory.backPropagationAutoencoder(ae, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.1f, 0.5f, 0f, 0f, 0f);


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


  // sequential execution for debugging
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.SEQ);


  // training
  t.train();


  // the output layer is removed, thus making the hidden layer the new output
  ae.removeLayer(ae.getOutputLayer());


  // testing
  t.test();


  assertEquals(0, t.getOutputError().getTotalNetworkError(), 0);
    }

View Full Code Here

      TrainingInputProvider trainInputProvider = new IrisInputProvider(1, 15000, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
      TrainingInputProvider testInputProvider = new IrisInputProvider(1, 150, new IrisTargetMultiNeuronOutputConverter(), false, true, false);
      MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();


      // backpropagation autoencoder training
      BackPropagationAutoencoder bae = TrainerFactory.backPropagationAutoencoder(ae, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.25f, 0.5f, 0f, 0f, 0f);


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


      // execution mode
      Environment.getInstance().setExecutionMode(EXECUTION_MODE.SEQ);


      bae.train();


      // the output layer is needed only during the training phase...
      ae.removeLayer(ae.getOutputLayer());


      bae.test();


      // 2 of the iris classes are linearly not separable - an error of 1/3 illustrates that
      assertEquals(0, bae.getOutputError().getTotalNetworkError(), 1/3f);
    }

View Full Code Here


  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)), 0.001f, 0.5f, 0f, 0f, 0f);
  BackPropagationAutoencoder secondTrainer = TrainerFactory.backPropagationAutoencoder(lastNN, null, null, null, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.001f, 0.5f, 0f, 0f, 0f);


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

View Full Code Here

  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();


      // the output layer is needed only during the training phase...
      ae.removeLayer(ae.getOutputLayer());


      bae.test();


      // 2 of the iris classes are linearly not separable - an error of 1/3 illustrates that
      assertEquals(0, bae.getOutputError().getTotalNetworkError(), 2/3f);
    }

View Full Code Here


  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);

View Full Code Here

  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
  //t.addEventListener(new EarlyStoppingListener(t.getTrainingInputProvider(), 1000, 0.1f));


  // training
  t.train();


  // the output layer is removed, thus making the hidden layer the new output
  ae.removeLayer(ae.getOutputLayer());


  // testing
  t.test();


  assertEquals(0, t.getOutputError().getTotalNetworkError(), 0);
    }

View Full Code Here

    public static BackPropagationAutoencoder backPropagationAutoencoder(NeuralNetworkImpl nn, TrainingInputProvider trainingSet, TrainingInputProvider testingSet, OutputError error, NNRandomInitializer rand, float learningRate, float momentum, float l1weightDecay, float l2weightDecay, float inputCorruptionRate, int trainingBatchSize, int testBatchSize, int epochs) {
  Properties p = backpropProperties(nn, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, 0F, trainingBatchSize, testBatchSize, epochs);
  p.setParameter(Constants.CORRUPTION_LEVEL, inputCorruptionRate);
  p.setParameter(Constants.BACKPROPAGATION, bplc(nn, p));


  return new BackPropagationAutoencoder(p);
    }

View Full Code Here

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