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.

    }


    @Test
    public void testConvolutions() {
  Environment.getInstance().setUseWeightsSharedMemory(true);
  Conv2DConnection c = new ConnectionFactory().conv2d(new Layer(), new Layer(), 3, 3, 2, 2, 2, 1, 1);


  c.getWeights().setElements(new float[] {1, 2, 3, 4, 1, 2, 3, 4});


  ValuesProvider vp = TensorFactory.tensorProvider(c, 1, true);
  TensorIterator it = vp.get(c.getInputLayer()).iterator();

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    }


    @Test
    public void testConvolutions2() {
  Environment.getInstance().setUseWeightsSharedMemory(true);
  Conv2DConnection c = new ConnectionFactory().conv2d(new Layer(), new Layer(), 3, 3, 2, 2, 2, 2, 1);
  c.getWeights().setElements(new float[] {1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4});


  ValuesProvider vp = TensorFactory.tensorProvider(c, 1, true);
  TensorIterator it = vp.get(c.getInputLayer()).iterator();
  for (int i = 0; i < vp.get(c.getInputLayer()).getSize(); i++) {

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  assertEquals(244, o.get(1, 0, 0, 0), 0);
    }


    @Test
    public void testMaxPooling() {
  Subsampling2DConnection c = new Subsampling2DConnection(new Layer(), new Layer(), 4, 4, 2, 2, 2);
  List<Connections> connections = new ArrayList<Connections>();
  connections.add(c);


  ConnectionCalculator calc = new AparapiMaxPooling2D();

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  assertEquals(32, o.get(1, 1, 1, 1), 0);
    }


    @Test
    public void testAveragePooling() {
  Subsampling2DConnection c = new Subsampling2DConnection(new Layer(), new Layer(), 4, 4, 2, 2, 2);
  List<Connections> connections = new ArrayList<Connections>();
  connections.add(c);


  AparapiAveragePooling2D calc = new AparapiAveragePooling2D();

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  assertEquals(29.5, o.get(1, 1, 1, 1), 0);
    }


    @Test
    public void testStochasticPooling() {
  Subsampling2DConnection c = new Subsampling2DConnection(new Layer(), new Layer(), 3, 3, 3, 3, 1);
  List<Connections> connections = new ArrayList<Connections>();
  connections.add(c);


  ValuesProvider vp = TensorFactory.tensorProvider(c, 2, true);
  float[] src = new float[] { 1.6f, 1.6f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.4f, 2.4f };

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    }


    @Test
    public void testMaxPoolingBackpropagation() {
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.SEQ);
  Subsampling2DConnection c = new Subsampling2DConnection(new Layer(), new Layer(), 4, 4, 2, 2, 2);


  List<Connections> connections = new ArrayList<Connections>();
  connections.add(c);


  // max pooling

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  assertEquals(true, bpo.get(1, 3, 2, 1) == a.get(1, 3, 2, 1));
    }


    @Test
    public void testAveragePoolingBackpropagation() {
  Subsampling2DConnection c = new Subsampling2DConnection(new Layer(), new Layer(), 4, 4, 2, 2, 2);


  // average pooling
  ConnectionCalculator calc = new AparapiAveragePooling2D();


  List<Connections> connections = new ArrayList<Connections>();

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


  Layer prev = null;
  int prevUnitCount = layers[0][0] * layers[0][1] * layers[0][2];
  result.addLayer(prev = new Layer());
  for (int i = 1; i < layers.length; i++) {
      int[] l = layers[i];
      Layer newLayer = null;
      Layer biasLayer = null;
      if (l.length == 1) {
    cf.fullyConnected(prev, newLayer = new Layer(), prevUnitCount, l[0]);
    if (addBias) {
        cf.fullyConnected(biasLayer = new Layer(), newLayer, 1, l[0]);
    }


    prevUnitCount = l[0];
      } else if (l.length == 4 || l.length == 2) {
    Integer inputFMRows = null;
    Integer inputFMCols = null;
    Integer filters = null;
    if (i == 1) {
        inputFMRows = layers[0][0];
        inputFMCols = layers[0][1];
        filters = layers[0][2];
    } else {
        for (Connections c : prev.getConnections()) {
      if (c.getOutputLayer() == prev) {
          if (c instanceof Conv2DConnection) {
        Conv2DConnection cc = (Conv2DConnection) c;
        inputFMRows = cc.getOutputFeatureMapRows();
        inputFMCols = cc.getOutputFeatureMapColumns();
        filters = cc.getOutputFilters();
        break;
          } else if (c instanceof Subsampling2DConnection) {
        Subsampling2DConnection sc = (Subsampling2DConnection) c;
        inputFMRows = sc.getOutputFeatureMapRows();
        inputFMCols = sc.getOutputFeatureMapColumns();
        filters = sc.getFilters();
        break;
          }
      }
        }
    }


    if (l.length == 4) {
        Conv2DConnection c = cf.conv2d(prev, newLayer = new Layer(), inputFMRows, inputFMCols, filters, l[0], l[1], l[2], l[3]);
        if (addBias) {
      cf.conv2d(biasLayer = new Layer(), newLayer, c.getOutputFeatureMapRows(), c.getOutputFeatureMapColumns(), 1, 1, 1, l[2], l[3]);
        }


        prevUnitCount = c.getOutputUnitCount();
    } else if (l.length == 2) {
        Subsampling2DConnection c = cf.subsampling2D(prev, newLayer = new Layer(), inputFMRows, inputFMCols, l[0], l[1], filters);
        prevUnitCount = c.getOutputUnitCount();
    }
      }


      result.addLayer(newLayer);

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  if (nn.getProperties() == null) {
      nn.setProperties(new Properties());
  }
  nn.getProperties().setParameter(Constants.CONNECTION_FACTORY, cf);


  addFullyConnectedLayer(nn, new Layer(), cf, layers[0], layers[0], addBias);
  for (int i = 1; i < layers.length; i++) {
      addFullyConnectedLayer(nn, new Layer(), cf, layers[i - 1], layers[i], addBias);
  }
    }

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  if (nn.addLayer(layer) && nn.getOutputLayer() != layer) {
      result = cf.fullyConnected(nn.getOutputLayer(), layer, inputUnitCount, outputUnitCount);
  }


  if (addBias && nn.getInputLayer() != layer) {
      nn.addConnections(cf.fullyConnected(new Layer(), layer, 1, outputUnitCount));
  }


  return result;
    }

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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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