Package com.github.neuralnetworks.architecture

Examples of com.github.neuralnetworks.architecture.NeuralNetworkImpl.addConnections()

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  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(nn.getInputLayer());
  i1.set(1, 0, 0);
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  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);
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  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);
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  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);
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  // 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);
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  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);
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  mlp.addLayer(input);

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

  FullyConnected fc2 = cf.fullyConnected(input, leaf2, 2, 3);
  fc2.getWeights().forEach(i -> fc2.getWeights().getElements()[i] = 0.2f);
  mlp.addConnections(fc2);
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  fc1.getWeights().forEach(i -> fc1.getWeights().getElements()[i] = 0.1f);
  mlp.addConnections(fc1);

  FullyConnected fc2 = cf.fullyConnected(input, leaf2, 2, 3);
  fc2.getWeights().forEach(i -> fc2.getWeights().getElements()[i] = 0.2f);
  mlp.addConnections(fc2);

  FullyConnected fc3 = cf.fullyConnected(leaf1, output, 3, 1);
  fc3.getWeights().forEach(i -> fc3.getWeights().getElements()[i] = 0.3f);
  mlp.addConnections(fc3);
  FullyConnected fc4 = cf.fullyConnected(leaf2, output, 3, 1);
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  fc2.getWeights().forEach(i -> fc2.getWeights().getElements()[i] = 0.2f);
  mlp.addConnections(fc2);

  FullyConnected fc3 = cf.fullyConnected(leaf1, output, 3, 1);
  fc3.getWeights().forEach(i -> fc3.getWeights().getElements()[i] = 0.3f);
  mlp.addConnections(fc3);
  FullyConnected fc4 = cf.fullyConnected(leaf2, output, 3, 1);
  fc4.getWeights().forEach(i -> fc4.getWeights().getElements()[i] = 0.4f);
  mlp.addConnections(fc4);

  mlp.setLayerCalculator(NNFactory.lcWeightedSum(mlp, null));
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  FullyConnected fc3 = cf.fullyConnected(leaf1, output, 3, 1);
  fc3.getWeights().forEach(i -> fc3.getWeights().getElements()[i] = 0.3f);
  mlp.addConnections(fc3);
  FullyConnected fc4 = cf.fullyConnected(leaf2, output, 3, 1);
  fc4.getWeights().forEach(i -> fc4.getWeights().getElements()[i] = 0.4f);
  mlp.addConnections(fc4);

  mlp.setLayerCalculator(NNFactory.lcWeightedSum(mlp, null));

  Set<Layer> calculated = new HashSet<>();
  calculated.add(mlp.getInputLayer());
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