Examples of com.github.neuralnetworks.architecture.Layer

Package com.github.neuralnetworks.architecture

Examples of com.github.neuralnetworks.architecture.Layer

com.github.neuralnetworks.architecture.Layer
A layer of neurons. Each layer contains a set of connections that link it to other layers. In that sense every neural network is a graph. This is done for maximum versatility. It makes possible the representation of various architectures - committee of machines or parallel networks to be calculated on different GPU devices.

    }


    public static RBM rbm(int visibleCount, int hiddenCount, boolean addBias) {
  RBM result = new RBM();
  ConnectionFactory cf = new ConnectionFactory();
  result.addConnections(cf.fullyConnected(new Layer(), new Layer(), visibleCount, hiddenCount));


  if (addBias) {
      result.addConnections(cf.fullyConnected(new Layer(), result.getVisibleLayer(), 1, visibleCount));
      result.addConnections(cf.fullyConnected(new Layer(), result.getHiddenLayer(), 1, hiddenCount));
  }


  return result;
    }

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  DBN result = new DBN();
  ConnectionFactory cf = new ConnectionFactory();
  result.setProperties(new Properties());
  result.getProperties().setParameter(Constants.CONNECTION_FACTORY, cf);


  result.addLayer(new Layer());
  for (int i = 1; i < layers.length; i++) {
      RBM rbm = new RBM();
      rbm.setProperties(new Properties());
      rbm.getProperties().setParameter(Constants.CONNECTION_FACTORY, cf);


      rbm.addConnections(cf.fullyConnected(result.getOutputLayer(), new Layer(), layers[i - 1], layers[i]));


      if (addBias) {
    rbm.addConnections(cf.fullyConnected(new Layer(), rbm.getVisibleLayer(), 1, layers[i - 1]));
    rbm.addConnections(cf.fullyConnected(new Layer(), rbm.getHiddenLayer(), 1, layers[i]));
      }


      result.addNeuralNetwork(rbm);
  }

View Full Code Here

  }


  ConnectionFactory cf = new ConnectionFactory();
  Properties properties = new Properties();
  properties.setParameter(Constants.CONNECTION_FACTORY, cf);
  StackedAutoencoder result = new StackedAutoencoder(new Layer());
  result.setProperties(properties);


  for (int i = 1; i < layers.length; i++) {
      Autoencoder ae = new Autoencoder();
      ae.setProperties(new Properties());
      ae.getProperties().setParameter(Constants.CONNECTION_FACTORY, cf);


      ae.addLayer(result.getOutputLayer());
      NNFactory.addFullyConnectedLayer(ae, new Layer(), cf, layers[i - 1], layers[i], addBias);
      NNFactory.addFullyConnectedLayer(ae, new Layer(), cf, layers[i], layers[i - 1], addBias);


      result.addNeuralNetwork(ae);
  }


  return result;

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  Map<Layer, Set<int[]>> dims = getLayersDimensions(nn, miniBatchSize);


  // create tensors
  List<Layer> layers = new ArrayList<>(dims.keySet());
  for (int i = 0; i < layers.size(); i++) {
      Layer l = layers.get(i);
      for (int[] d : dims.get(l)) {
    result.add(l, true, d);
      }
  }

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      Map<Layer, Set<int[]>> dims = getLayersDimensions(nn, miniBatchSize);
      
      // create tensors
      List<Layer> layers = new ArrayList<>(dims.keySet());
      for (int i = 0; i < layers.size(); i++) {
    Layer l = layers.get(i);
    for (int[] d : dims.get(l)) {
        if (result.get(l, d) == null) {
      result.add(l, true, d);
        }
    }

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  ValuesProvider result = new ValuesProvider(sibling);


  // create tensors
  List<Layer> layers = new ArrayList<>(dims.keySet());
  for (int i = 0; i < layers.size(); i++) {
      Layer l = layers.get(i);
      for (int[] d : dims.get(l)) {
    result.add(l, true, d);
      }
  }

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Related Classes of com.github.neuralnetworks.architecture.Layer

com.github.neuralnetworks.architecture.types.Autoencoder

com.github.neuralnetworks.architecture.types.DBN

com.github.neuralnetworks.architecture.types.NNFactory

com.github.neuralnetworks.architecture.types.RBM

com.github.neuralnetworks.architecture.types.StackedAutoencoder

com.github.neuralnetworks.calculation.BreadthFirstOrderStrategy

com.github.neuralnetworks.calculation.RBMLayerCalculator

com.github.neuralnetworks.calculation.TargetLayerOrderStrategy

com.github.neuralnetworks.tensor.TensorFactory

com.github.neuralnetworks.test.CNNTest

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