Examples of AparapiWeightedSumConnectionCalculator

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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  Matrix bcg = bc.getConnectionGraph();
  bcg.set(0.1f, 0, 0);
  bcg.set(0.2f, 1, 0);

  ConnectionCalculatorFullyConnected aws = new AparapiWeightedSumConnectionCalculator();

  List<Connections> connections = new ArrayList<>();
  connections.add(c1);

  ValuesProvider vp = new ValuesProvider();
  vp.addValues(c1.getInputLayer(), i1);
  vp.addValues(ol, o);

  aws.calculate(connections, vp, ol);

  // most simple case
  assertEquals(14, o.get(0, 0), 0);
  assertEquals(32, o.get(0, 1), 0);
  assertEquals(32, o.get(1, 0), 0);
  assertEquals(77, o.get(1, 1), 0);
  Util.fillArray(o.getElements(), 0);

  // with bias
  connections = new ArrayList<>();
  connections.add(c1);
  connections.add(bc);

  vp = new ValuesProvider();
  vp.addValues(c1.getInputLayer(), i1);
  vp.addValues(ol, o);

  aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  assertEquals(14.1, o.get(0, 0), 0.01);
  assertEquals(32.1, o.get(0, 1), 0.01);
  assertEquals(32.2, o.get(1, 0), 0.01);
  assertEquals(77.2, o.get(1, 1), 0.01);
  Util.fillArray(o.getElements(), 0);

  // combined layers
  connections = new ArrayList<>();
  connections.add(c1);
  connections.add(c2);
  connections.add(bc);

  vp = new ValuesProvider();
  vp.addValues(c1.getInputLayer(), i1);
  vp.addValues(c2.getInputLayer(), i2);
  vp.addValues(ol, o);

  aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  assertEquals(28.1, o.get(0, 0), 0.01);
  assertEquals(64.1, o.get(0, 1), 0.01);
  assertEquals(64.2, o.get(1, 0), 0.01);
  assertEquals(154.2, o.get(1, 1), 0.01);
View Full Code Here

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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  Matrix bcg = bc.getConnectionGraph();
  bcg.set(0.1f, 0, 0);
  bcg.set(0.2f, 1, 0);

  ConnectionCalculatorFullyConnected aws = new AparapiWeightedSumConnectionCalculator();

  List<Connections> connections = new ArrayList<>();
  connections.add(c1);

  ValuesProvider vp = new ValuesProvider();
  vp.addValues(c1.getOutputLayer(), i1);
  vp.addValues(ol, o);

  aws.calculate(connections, vp, ol);

  // most simple case
  assertEquals(14, o.get(0, 0), 0);
  assertEquals(32, o.get(0, 1), 0);
  assertEquals(32, o.get(1, 0), 0);
  assertEquals(77, o.get(1, 1), 0);
  Util.fillArray(o.getElements(), 0);

  // with bias
  connections = new ArrayList<>();
  connections.add(c1);
  connections.add(bc);

  vp = new ValuesProvider();
  vp.addValues(c1.getOutputLayer(), i1);
  vp.addValues(ol, o);

  aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  assertEquals(14.1, o.get(0, 0), 0.01);
  assertEquals(32.1, o.get(0, 1), 0.01);
  assertEquals(32.2, o.get(1, 0), 0.01);
  assertEquals(77.2, o.get(1, 1), 0.01);
  Util.fillArray(o.getElements(), 0);

  // combined layers
  connections = new ArrayList<>();
  connections.add(c1);
  connections.add(c2);
  connections.add(bc);

  vp = new ValuesProvider();
  vp.addValues(c1.getOutputLayer(), i1);
  vp.addValues(c2.getOutputLayer(), i2);
  vp.addValues(ol, o);

  aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  assertEquals(28.1, o.get(0, 0), 0.01);
  assertEquals(64.1, o.get(0, 1), 0.01);
  assertEquals(64.2, o.get(1, 0), 0.01);
  assertEquals(154.2, o.get(1, 1), 0.01);
View Full Code Here

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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  return new RBM(visibleCount, hiddenCount, addBias, addBias);
    }
   
    public static RBMLayerCalculator rbmWeightedSumWeightedSum(RBM rbm) {
  RBMLayerCalculator lc = new RBMLayerCalculator();
  lc.addConnectionCalculator(rbm.getVisibleLayer(), new AparapiWeightedSumConnectionCalculator());
  lc.addConnectionCalculator(rbm.getHiddenLayer(), new AparapiWeightedSumConnectionCalculator());
  populateBiasLayers(lc, rbm);
  return lc;
    }
View Full Code Here

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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  DBN dbn = NNFactory.dbn(new int [] {3, 3, 2}, true);
  dbn.setLayerCalculator(NNFactory.lcSigmoid(dbn, null));

  LayerCalculatorImpl lc = (LayerCalculatorImpl) dbn.getLayerCalculator();
  RBM firstRBM = dbn.getFirstNeuralNetwork();
  lc.addConnectionCalculator(firstRBM.getHiddenLayer(), new AparapiWeightedSumConnectionCalculator());

  Matrix m1 = firstRBM.getMainConnections().getConnectionGraph();
  m1.set(1, 0, 0);
  m1.set(0, 0, 1);
  m1.set(0, 0, 2);
View Full Code Here

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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  i1.set(3, 2, 0);
  i1.set(4, 0, 1);
  i1.set(5, 1, 1);
  i1.set(6, 2, 1);

  ConnectionCalculatorFullyConnected aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  // most simple case
  Matrix o = vp.get(nn.getOutputLayer());
  assertEquals(14, o.get(0, 0), 0);
  assertEquals(32, o.get(0, 1), 0);
  assertEquals(32, o.get(1, 0), 0);
  assertEquals(77, o.get(1, 1), 0);

  // with bias
  connections = new ArrayList<>();
  connections.add(c1);
  connections.add(bc);

  nn = new NeuralNetworkImpl();
  nn.addConnections(connections.toArray(new Connections[connections.size()]));
  vp = TensorFactory.tensorProvider(nn, 2, true);

  i1 = vp.get(nn.getInputLayer());
  i1.set(1, 0, 0);
  i1.set(2, 1, 0);
  i1.set(3, 2, 0);
  i1.set(4, 0, 1);
  i1.set(5, 1, 1);
  i1.set(6, 2, 1);

  aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  o = vp.get(nn.getOutputLayer());
  assertEquals(14.1, o.get(0, 0), 0.01);
  assertEquals(32.1, o.get(0, 1), 0.01);
  assertEquals(32.2, o.get(1, 0), 0.01);
  assertEquals(77.2, o.get(1, 1), 0.01);

  // combined layers
  connections = new ArrayList<>();
  connections.add(c1);
  connections.add(c2);
  connections.add(bc);
  nn = new NeuralNetworkImpl();
  nn.addConnections(connections.toArray(new Connections[connections.size()]));
  vp = TensorFactory.tensorProvider(nn, 2, true);

  i1 = vp.get(il1);
  i1.set(1, 0, 0);
  i1.set(2, 1, 0);
  i1.set(3, 2, 0);
  i1.set(4, 0, 1);
  i1.set(5, 1, 1);
  i1.set(6, 2, 1);

  Matrix i2 = vp.get(il2);
  i2.set(1, 0, 0);
  i2.set(2, 1, 0);
  i2.set(3, 2, 0);
  i2.set(4, 0, 1);
  i2.set(5, 1, 1);
  i2.set(6, 2, 1);

  aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  o = vp.get(nn.getOutputLayer());
  assertEquals(28.1, o.get(0, 0), 0.01);
  assertEquals(64.1, o.get(0, 1), 0.01);
  assertEquals(64.2, o.get(1, 0), 0.01);
View Full Code Here

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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  Matrix bcg = bc.getWeights();
  bcg.set(0.1f, 0, 0);
  bcg.set(0.2f, 1, 0);

  ConnectionCalculatorFullyConnected aws = new AparapiWeightedSumConnectionCalculator();

  List<Connections> connections = new ArrayList<>();
  connections.add(c1);
  NeuralNetworkImpl nn = new NeuralNetworkImpl();
  nn.addConnections(connections.toArray(new Connections[connections.size()]));
  ValuesProvider vp = TensorFactory.tensorProvider(nn, 2, true);

  Matrix i1 = vp.get(il1);
  i1.set(1, 0, 0);
  i1.set(2, 1, 0);
  i1.set(3, 2, 0);
  i1.set(4, 0, 1);
  i1.set(5, 1, 1);
  i1.set(6, 2, 1);

  aws.calculate(connections, vp, ol);

  // most simple case
  Matrix o = vp.get(ol);
  assertEquals(14, o.get(0, 0), 0);
  assertEquals(32, o.get(0, 1), 0);
  assertEquals(32, o.get(1, 0), 0);
  assertEquals(77, o.get(1, 1), 0);

  // with bias
  connections = new ArrayList<>();
  connections.add(c1);
  connections.add(bc);
  nn = new NeuralNetworkImpl();
  nn.addConnections(connections.toArray(new Connections[connections.size()]));
  vp = TensorFactory.tensorProvider(nn, 2, true);
  i1 = vp.get(il1);
  i1.set(1, 0, 0);
  i1.set(2, 1, 0);
  i1.set(3, 2, 0);
  i1.set(4, 0, 1);
  i1.set(5, 1, 1);
  i1.set(6, 2, 1);

  aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  o = vp.get(ol);
  assertEquals(14.1, o.get(0, 0), 0.01);
  assertEquals(32.1, o.get(0, 1), 0.01);
  assertEquals(32.2, o.get(1, 0), 0.01);
  assertEquals(77.2, o.get(1, 1), 0.01);

  // combined layers
  connections = new ArrayList<>();
  connections.add(c1);
  connections.add(c2);
  connections.add(bc);
  nn = new NeuralNetworkImpl();
  nn.addConnections(connections.toArray(new Connections[connections.size()]));
  vp = TensorFactory.tensorProvider(nn, 2, true);

  i1 = vp.get(il1);
  i1.set(1, 0, 0);
  i1.set(2, 1, 0);
  i1.set(3, 2, 0);
  i1.set(4, 0, 1);
  i1.set(5, 1, 1);
  i1.set(6, 2, 1);

  Matrix i2 = vp.get(il2);
  i2.set(1, 0, 0);
  i2.set(2, 1, 0);
  i2.set(3, 2, 0);
  i2.set(4, 0, 1);
  i2.set(5, 1, 1);
  i2.set(6, 2, 1);

  aws = new AparapiWeightedSumConnectionCalculator();
  aws.calculate(connections, vp, ol);

  o = vp.get(ol);
  assertEquals(28.1, o.get(0, 0), 0.01);
  assertEquals(64.1, o.get(0, 1), 0.01);
  assertEquals(64.2, o.get(1, 0), 0.01);
View Full Code Here

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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  dbn.setLayerCalculator(NNFactory.lcSigmoid(dbn, null));

  RBM firstRBM = dbn.getFirstNeuralNetwork();

  LayerCalculatorImpl lc = (LayerCalculatorImpl) dbn.getLayerCalculator();
  lc.addConnectionCalculator(firstRBM.getHiddenLayer(), new AparapiWeightedSumConnectionCalculator());

  Matrix m1 = firstRBM.getMainConnections().getWeights();
  m1.set(1, 0, 0);
  m1.set(0, 0, 1);
  m1.set(0, 0, 2);
View Full Code Here

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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    if (outputCC != null && nn.getOutputLayer() == l) {
        lc.addConnectionCalculator(l, outputCC);
    } else if (Util.isConvolutional(l)) {
        lc.addConnectionCalculator(l, new ConnectionCalculatorConv());
    } else {
        lc.addConnectionCalculator(l, new AparapiWeightedSumConnectionCalculator());
    }
      } else {
    lc.addConnectionCalculator(l, new ConstantConnectionCalculator());
      }
  }
View Full Code Here

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiWeightedSumConnectionCalculator

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  return result;
    }

    public static RBMLayerCalculator rbmWeightedSumWeightedSum(RBM rbm, int batchSize) {
  return new RBMLayerCalculator(rbm, batchSize, new AparapiWeightedSumConnectionCalculator(), new AparapiWeightedSumConnectionCalculator(), new AparapiWeightedSumConnectionCalculator());
    }
View Full Code Here
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