Examples of LayerCalculatorImpl

com.github.neuralnetworks.calculation.LayerCalculatorImpl
Implementation of the LayerCalculator interface for calculating single target layer It takes advantage of the fact that the neural network is a graph with layers as nodes and connections between layers as links of the graph The results are propagated within the graph

Examples of com.github.neuralnetworks.calculation.LayerCalculatorImpl


  return lc;
    }


    public static LayerCalculatorImpl lcSoftRelu(NeuralNetworkImpl nn, ConnectionCalculator outputCC) {
  LayerCalculatorImpl lc = new LayerCalculatorImpl();
  for (Layer l : nn.getLayers()) {
      if (!Util.isBias(l)) {
    if (nn.getOutputLayer() == l) {
        if (outputCC != null) {
      lc.addConnectionCalculator(l, outputCC);
        } else {
      lc.addConnectionCalculator(l, new AparapiSoftReLU());
        }
    } else if (Util.isConvolutional(l)) {
        lc.addConnectionCalculator(l, new AparapiConv2DSoftReLU());
    } else {
        lc.addConnectionCalculator(l, new AparapiSoftReLU());
    }
      } else {
    lc.addConnectionCalculator(l, new ConstantConnectionCalculator());
      }
  }


  return lc;
    }

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Examples of com.github.neuralnetworks.calculation.LayerCalculatorImpl


  return lc;
    }


    public static LayerCalculatorImpl lcRelu(NeuralNetworkImpl nn, ConnectionCalculator outputCC) {
  LayerCalculatorImpl lc = new LayerCalculatorImpl();
  for (Layer l : nn.getLayers()) {
      if (!Util.isBias(l)) {
    if (nn.getOutputLayer() == l) {
        if (outputCC != null) {
      lc.addConnectionCalculator(l, outputCC);
        } else {
      lc.addConnectionCalculator(l, new AparapiReLU());
        }
    } else if (Util.isConvolutional(l)) {
        lc.addConnectionCalculator(l, new AparapiConv2DReLU());
    } else {
        lc.addConnectionCalculator(l, new AparapiReLU());
    }
      } else {
    lc.addConnectionCalculator(l, new ConstantConnectionCalculator());
      }
  }


  return lc;
    }

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Examples of com.github.neuralnetworks.calculation.LayerCalculatorImpl


  return lc;
    }


    public static LayerCalculatorImpl lcTanh(NeuralNetworkImpl nn, ConnectionCalculator outputCC) {
  LayerCalculatorImpl lc = new LayerCalculatorImpl();
  for (Layer l : nn.getLayers()) {
      if (!Util.isBias(l)) {
    if (outputCC != null && nn.getOutputLayer() == l) {
        lc.addConnectionCalculator(l, outputCC);
    } else if (Util.isConvolutional(l)) {
        lc.addConnectionCalculator(l, new AparapiConv2DTanh());
    } else {
        lc.addConnectionCalculator(l, new AparapiTanh());
    }
      } else {
    lc.addConnectionCalculator(l, new ConstantConnectionCalculator());
      }
  }


  return lc;
    }

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Examples of com.github.neuralnetworks.calculation.LayerCalculatorImpl

  return lc;
    }


    public static void lcMaxPooling(NeuralNetworkImpl nn) {
  if (nn.getLayerCalculator() instanceof LayerCalculatorImpl) {
      LayerCalculatorImpl lc = (LayerCalculatorImpl) nn.getLayerCalculator();
      nn.getLayers().stream().filter(l -> Util.isSubsampling(l)).forEach(l -> lc.addConnectionCalculator(l, new AparapiMaxPooling2D()));
  } else {
      throw new IllegalArgumentException("LayerCalculator type not supported");
  }
    }

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Examples of com.github.neuralnetworks.calculation.LayerCalculatorImpl

  }
    }
    
    public static void lcAveragePooling(NeuralNetworkImpl nn) {
  if (nn.getLayerCalculator() instanceof LayerCalculatorImpl) {
      LayerCalculatorImpl lc = (LayerCalculatorImpl) nn.getLayerCalculator();
      nn.getLayers().stream().filter(l -> Util.isSubsampling(l)).forEach(l -> lc.addConnectionCalculator(l, new AparapiAveragePooling2D()));
  } else {
      throw new IllegalArgumentException("LayerCalculator type not supported");
  }
    }

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Examples of com.github.neuralnetworks.calculation.LayerCalculatorImpl

  }
    }
    
    public static void lcStochasticPooling(NeuralNetworkImpl nn) {
  if (nn.getLayerCalculator() instanceof LayerCalculatorImpl) {
      LayerCalculatorImpl lc = (LayerCalculatorImpl) nn.getLayerCalculator();
      nn.getLayers().stream().filter(l -> Util.isSubsampling(l)).forEach(l -> lc.addConnectionCalculator(l, new AparapiStochasticPooling2D()));
  } else {
      throw new IllegalArgumentException("LayerCalculator type not supported");
  }
    }

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