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.

  if (layers.length <= 1) {
      throw new IllegalArgumentException("more than one layer is required");
  }


  DBN result = new DBN();
  result.addLayer(new Layer());
  for (int i = 1; i < layers.length; i++) {
      result.addLevel(new Layer(), layers[i - 1], layers[i], addBias);
  }


  return result;
    }

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    public static StackedAutoencoder sae(int[] layers, boolean addBias) {
  if (layers == null || layers.length <= 1) {
      throw new IllegalArgumentException("more than one layer is required");
  }


  StackedAutoencoder result = new StackedAutoencoder(new Layer());
  for (int i = 1; i < layers.length; i++) {
      result.addLevel(new Layer(), layers[i - 1], layers[i], addBias);
  }


  return result;
    }

View Full Code Here


    @Test
    public void testWeightedSumFF() {
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.SEQ);


  Layer il1 = new Layer();
  Layer ol = new Layer();
  Layer il2 = new Layer();


  Tensor weights = TensorFactory.tensor(2, 2, 3);


  FullyConnected c1 = new FullyConnected(il1, ol, TensorFactory.tensor(weights, new int[][]{{0, 0, 0}, {0, 1, 2}}));
  FullyConnected c2 = new FullyConnected(il2, ol, TensorFactory.tensor(weights, new int[][]{{1, 0, 0}, {1, 1, 2}}));
  FullyConnected bc = new FullyConnected(new Layer(), ol, 1, 2);


  Matrix cg = c1.getWeights();
  cg.set(1, 0, 0);
  cg.set(2, 0, 1);
  cg.set(3, 0, 2);

View Full Code Here


    @Test
    public void testWeightedSumBP() {
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.GPU);


  Layer il1 = new Layer();
  Layer ol = new Layer();
  Layer il2 = new Layer();


  Tensor weights = TensorFactory.tensor(2, 3, 2);
  FullyConnected c1 = new FullyConnected(ol, il1, TensorFactory.tensor(weights, new int[][]{{0, 0, 0}, {0, 2, 1}}));
  FullyConnected c2 = new FullyConnected(ol, il2, TensorFactory.tensor(weights, new int[][]{{1, 0, 0}, {1, 2, 1}}));
  FullyConnected bc = new FullyConnected(new Layer(), ol, 1, 2);


  Matrix cg = c1.getWeights();
  cg.set(1, 0, 0);
  cg.set(2, 1, 0);
  cg.set(3, 2, 0);

View Full Code Here

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


  Environment.getInstance().setUseWeightsSharedMemory(true);
  ConnectionFactory cf = new ConnectionFactory();
  NeuralNetworkImpl mlp = new NeuralNetworkImpl();
  Layer input = new Layer();
  Layer leaf1 = new Layer();
  Layer leaf2 = new Layer();
  Layer output = new Layer();


  mlp.addLayer(input);


  FullyConnected fc1 = cf.fullyConnected(input, leaf1, 2, 3);
  fc1.getWeights().forEach(i -> fc1.getWeights().getElements()[i] = 0.1f);

View Full Code Here

    @Test
    public void testRemoveLayer() {
  Environment.getInstance().setUseWeightsSharedMemory(true);
  NeuralNetworkImpl mlp = NNFactory.mlp(new int[] {3, 4, 5}, true);
  assertEquals(5, mlp.getLayers().size(), 0);
  Layer currentOutput = mlp.getOutputLayer();
  mlp.removeLayer(mlp.getOutputLayer());
  assertEquals(3, mlp.getLayers().size(), 0);
  assertEquals(true, currentOutput != mlp.getOutputLayer());
    }

View Full Code Here

  
  Set<Layer> calculated = new HashSet<Layer>();
  calculated.add(mlp.getInputLayer());
  List<ConnectionCandidate> ccc = new TargetLayerOrderStrategy(mlp, mlp.getOutputLayer(), calculated).order();
  assertEquals(4, ccc.size(), 0);
  Layer l = mlp.getInputLayer();
  assertTrue(ccc.get(0).connection == l.getConnections().get(0));
  l = l.getConnections().get(0).getOutputLayer();
  assertTrue(ccc.get(1).connection == l.getConnections().get(1));
  assertTrue(ccc.get(2).connection == l.getConnections().get(2));
  l = l.getConnections().get(2).getOutputLayer();
  assertTrue(ccc.get(3).connection == l.getConnections().get(1));


  ccc = new BreadthFirstOrderStrategy(mlp, mlp.getOutputLayer()).order();
  assertEquals(4, ccc.size(), 0);
  l = mlp.getOutputLayer();
  assertTrue(ccc.get(0).connection == l.getConnections().get(0));
  assertTrue(ccc.get(1).connection == l.getConnections().get(1));


  l = l.getConnections().get(0).getInputLayer();
  assertTrue(ccc.get(2).connection == l.getConnections().get(0));
  assertTrue(ccc.get(3).connection == l.getConnections().get(1));


  // Simple MLP
  mlp = NNFactory.mlp(new int[] {3, 4}, true);


  calculated = new HashSet<Layer>();
  calculated.add(mlp.getInputLayer());
  ccc = new TargetLayerOrderStrategy(mlp, mlp.getOutputLayer(), calculated).order();
  assertEquals(2, ccc.size(), 0);
  l = mlp.getOutputLayer();
  assertTrue(ccc.get(0).connection == l.getConnections().get(0));
  assertTrue(ccc.get(1).connection == l.getConnections().get(1));


  ccc = new BreadthFirstOrderStrategy(mlp, mlp.getOutputLayer()).order();
  assertEquals(2, ccc.size(), 0);
  l = mlp.getOutputLayer();
  assertTrue(ccc.get(0).connection == l.getConnections().get(0));
  assertTrue(ccc.get(1).connection == l.getConnections().get(1));


  // CNN
  NeuralNetworkImpl cnn = NNFactory.convNN(new int[][] { { 3, 3, 2 }, { 2, 2, 1, 1 } }, true);


  calculated = new HashSet<Layer>();
  calculated.add(cnn.getInputLayer());
  ccc = new TargetLayerOrderStrategy(cnn, cnn.getOutputLayer(), calculated).order();
  l = cnn.getOutputLayer();
  assertEquals(2, ccc.size(), 0);
  assertTrue(ccc.get(0).connection == l.getConnections().get(0));
  assertTrue(ccc.get(1).connection == l.getConnections().get(1));


  ccc = new BreadthFirstOrderStrategy(cnn, cnn.getOutputLayer()).order();
  l = cnn.getOutputLayer();
  assertEquals(2, ccc.size(), 0);
  assertTrue(ccc.get(0).connection == l.getConnections().get(0));
  assertTrue(ccc.get(1).connection == l.getConnections().get(1));
    }

View Full Code Here

    private FullyConnected hiddenBiasConnections;




    public RBM(int visibleUnitCount, int hiddenUnitCount, boolean addVisibleBias, boolean addHiddenBias) {
  super();
  init(new Layer(), new Layer(), visibleUnitCount, hiddenUnitCount, addVisibleBias, addHiddenBias);
    }

View Full Code Here

  addLayer(hiddenLayer);


  mainConnections = new FullyConnected(visibleLayer, hiddenLayer, visibleUnitCount, hiddenUnitCount);


  if (addVisibleBias) {
      Layer visibleBiasLayer = new Layer();
      addLayer(visibleBiasLayer);
      visibleBiasConnections = new FullyConnected(visibleBiasLayer, visibleLayer, 1, visibleUnitCount);
  }


  if (addHiddenBias) {
      Layer hiddenBiasLayer = new Layer();
      addLayer(hiddenBiasLayer);
      hiddenBiasConnections = new FullyConnected(hiddenBiasLayer, hiddenLayer, 1, hiddenUnitCount);
  }
    }

View Full Code Here

    public Autoencoder() {
  super();
    }


    public Layer getHiddenBiasLayer() {
  Layer hiddenLayer = getHiddenLayer();
  return hiddenLayer.getConnections().stream().map(c -> Util.getOppositeLayer(c, hiddenLayer)).filter(l -> Util.isBias(l)).findFirst().orElse(null);
    }

View Full Code Here

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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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