Examples of AparapiAveragePooling2D

com.github.neuralnetworks.calculation.neuronfunctions.AparapiAveragePooling2D
Average pooling

Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiAveragePooling2D

  Subsampling2DConnection c = new Subsampling2DConnection(new Layer(), new Layer(), 4, 4, 2, 2, 2);
  Matrix i1 = new Matrix(new float[] { 0.5f, 1, 1, 2, 1.5f, 3, 2, 4, 2.5f, 5, 3, 6, 3.5f, 7, 4f, 8, 4.5f, 9, 5f, 10, 5.5f, 11, 6f, 12, 6.5f, 13, 7f, 14, 8f, 16, 7.5f, 15, 8.5f, 17, 9f, 18, 9.5f, 19, 10f, 20, 10.5f, 21, 11f, 22, 11.5f, 23, 12f, 24, 12.5f, 25, 13f, 26, 13.5f, 27, 14f, 28, 14.5f, 29, 15f, 30, 16f, 32, 15.5f, 31 }, 2);
  List<Connections> connections = new ArrayList<Connections>();
  connections.add(c);


  AparapiAveragePooling2D calc = new AparapiAveragePooling2D();
  Matrix o = new Matrix(8, 2);


  ValuesProvider vp = new ValuesProvider();
  vp.addValues(c.getInputLayer(), i1);
  vp.addValues(c.getOutputLayer(), o);


  calc.calculate(connections, vp, c.getOutputLayer());


  assertEquals(1.75, o.get(0, 0), 0);
  assertEquals(2.75, o.get(1, 0), 0);
  assertEquals(5.75, o.get(2, 0), 0);
  assertEquals(6.75, o.get(3, 0), 0);

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Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiAveragePooling2D

  Environment.getInstance().setExecutionMode(EXECUTION_MODE.SEQ);
  Subsampling2DConnection c = new Subsampling2DConnection(new Layer(), new Layer(), 4, 4, 2, 2, 2);
  Matrix a1 = new Matrix(new float[] { 0.5f, 1, 1, 2, 1.5f, 3, 2, 4, 2.5f, 5, 3, 6, 3.5f, 7, 4f, 8, 4.5f, 9, 5f, 10, 5.5f, 11, 6f, 12, 6.5f, 13, 7f, 14, 8f, 16, 7.5f, 15, 8.5f, 17, 9f, 18, 9.5f, 19, 10f, 20, 10.5f, 21, 11f, 22, 11.5f, 23, 12f, 24, 12.5f, 25, 13f, 26, 13.5f, 27, 14f, 28, 14.5f, 29, 15f, 30, 16f, 32, 15.5f, 31 }, 2);


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


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


  ValuesProvider vp = new ValuesProvider();
  vp.addValues(c.getInputLayer(), a1);
  Matrix o = new Matrix(8, 2);
  vp.addValues(c.getOutputLayer(), o);


  calc.calculate(connections, vp, c.getOutputLayer());


  ValuesProvider activations = new ValuesProvider();
  activations.addValues(c.getInputLayer(), a1);


  BackpropagationAveragePooling2D bp = new BackpropagationAveragePooling2D();

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Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiAveragePooling2D

    }
    
    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.neuronfunctions.AparapiAveragePooling2D

    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();


  ValuesProvider vp = TensorFactory.tensorProvider(c, 2, true);
  float[] src = new float[] { 0.5f, 1, 1, 2, 1.5f, 3, 2, 4, 2.5f, 5, 3, 6, 3.5f, 7, 4f, 8, 4.5f, 9, 5f, 10, 5.5f, 11, 6f, 12, 6.5f, 13, 7f, 14, 8f, 16, 7.5f, 15, 8.5f, 17, 9f, 18, 9.5f, 19, 10f, 20, 10.5f, 21, 11f, 22, 11.5f, 23, 12f, 24, 12.5f, 25, 13f, 26, 13.5f, 27, 14f, 28, 14.5f, 29, 15f, 30, 16f, 32, 15.5f, 31 };
  System.arraycopy(src, 0, vp.get(c.getInputLayer()).getElements(), vp.get(c.getInputLayer()).getStartIndex(), src.length);


  calc.calculate(connections, vp, c.getOutputLayer());


  Tensor o = vp.get(c.getOutputLayer());


  assertEquals(1.75, o.get(0, 0, 0, 0), 0);
  assertEquals(2.75, o.get(0, 0, 1, 0), 0);

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Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiAveragePooling2D

    @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>();
  connections.add(c);


  ValuesProvider activations = TensorFactory.tensorProvider(c, 2, true);
  float[] src = new float[] { 0.5f, 1, 1, 2, 1.5f, 3, 2, 4, 2.5f, 5, 3, 6, 3.5f, 7, 4f, 8, 4.5f, 9, 5f, 10, 5.5f, 11, 6f, 12, 6.5f, 13, 7f, 14, 8f, 16, 7.5f, 15, 8.5f, 17, 9f, 18, 9.5f, 19, 10f, 20, 10.5f, 21, 11f, 22, 11.5f, 23, 12f, 24, 12.5f, 25, 13f, 26, 13.5f, 27, 14f, 28, 14.5f, 29, 15f, 30, 16f, 32, 15.5f, 31 };
  System.arraycopy(src, 0, activations.get(c.getInputLayer()).getElements(), activations.get(c.getInputLayer()).getStartIndex(), src.length);


  calc.calculate(connections, activations, c.getOutputLayer());


  BackpropagationAveragePooling2D bp = new BackpropagationAveragePooling2D();
  bp.setActivations(activations);


  ValuesProvider vp = TensorFactory.tensorProvider(c, 2, true);

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Examples of com.github.neuralnetworks.calculation.neuronfunctions.AparapiAveragePooling2D

    }
    
    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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