Examples of BackPropagationAutoencoder

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

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


  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;
    }

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

  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);
    }

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

      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);
    }

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


  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);

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

  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);
    }

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


  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);

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

  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);
    }

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

    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);
    }

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