Package com.github.neuralnetworks.architecture

Examples of com.github.neuralnetworks.architecture.Subsampling2DConnection

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  assertEquals(2.08, o.get(0, 1), 0.01);
    }

    @Test
    public void testMaxPoolingBackpropagation() {
  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);
  Matrix o = new Matrix(8, 2);

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

  // max pooling
  ValuesProvider vp = new ValuesProvider();
  vp.addValues(c.getInputLayer(), a1);
  vp.addValues(c.getOutputLayer(), o);

  ConnectionCalculator calc = new AparapiMaxPooling2D();
  calc.calculate(connections, vp, c.getOutputLayer());

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

  Matrix bpo = new Matrix(32, 2);

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

  BackpropagationMaxPooling2D bp = new BackpropagationMaxPooling2D();
  bp.setActivations(activations);
  bp.calculate(connections, vp, c.getInputLayer());

  assertEquals(true, bpo.get(5, 0) == a1.get(5, 0));
  assertEquals(true, bpo.get(7, 0) == a1.get(7, 0));
  assertEquals(true, bpo.get(13, 0) == a1.get(13, 0));
  assertEquals(true, bpo.get(14, 0) == a1.get(14, 0));
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    }

    @Test
    public void testAveragePoolingBackpropagation() {
  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();
  bp.setActivations(activations);

  vp = new ValuesProvider();
  vp.addValues(c.getOutputLayer(), o);
  Matrix bpo = new Matrix(32, 2);
  vp.addValues(c.getInputLayer(), bpo);

  bp.calculate(connections, vp, c.getInputLayer());

  assertEquals(true, bpo.get(0, 0) == o.get(0, 0) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(1, 0) == o.get(0, 0) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(4, 0) == o.get(0, 0) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(5, 0) == o.get(0, 0) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(10, 0) == o.get(3, 0) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(11, 0) == o.get(3, 0) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(14, 0) == o.get(3, 0) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(15, 0) == o.get(3, 0) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(0, 1) == o.get(0, 1) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(1, 1) == o.get(0, 1) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(4, 1) == o.get(0, 1) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(5, 1) == o.get(0, 1) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(10, 1) == o.get(3, 1) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(11, 1) == o.get(3, 1) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(14, 1) == o.get(3, 1) / c.getSubsamplingRegionLength());
  assertEquals(true, bpo.get(15, 1) == o.get(3, 1) / c.getSubsamplingRegionLength());
    }
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        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 = new Conv2DConnection(prev, newLayer = new Layer(), inputFMRows, inputFMCols, filters, l[0], l[1], l[2], l[3]);
        if (addBias) {
      new Conv2DConnection(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 = new Subsampling2DConnection(prev, newLayer = new Layer(), inputFMRows, inputFMCols, l[0], l[1], filters);
        prevUnitCount = c.getOutputUnitCount();
    }
      }

      result.addLayer(newLayer);
      if (biasLayer != null) {
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     * @return whether layer is in fact subsampling layer (based on the
     *         connections)
     */
    public static boolean isSubsampling(Layer layer) {
  Conv2DConnection conv = null;
  Subsampling2DConnection ss = null;
  for (Connections c : layer.getConnections()) {
      if (c instanceof Conv2DConnection) {
    conv = (Conv2DConnection) c;
      } else if (c instanceof Subsampling2DConnection) {
    ss = (Subsampling2DConnection) c;
      }
  }

  if (ss != null && (ss.getOutputLayer() == layer || conv == null)) {
      return true;
  }

  return false;
    }
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     * @return whether layer is in fact convolutional layer (based on the
     *         connections)
     */
    public static boolean isConvolutional(Layer layer) {
  Conv2DConnection conv = null;
  Subsampling2DConnection ss = null;
  for (Connections c : layer.getConnections()) {
      if (c instanceof Conv2DConnection) {
    conv = (Conv2DConnection) c;
      } else if (c instanceof Subsampling2DConnection) {
    ss = (Subsampling2DConnection) c;
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    }

    @Override
    public void calculate(List<Connections> connections, ValuesProvider valuesProvider, Layer targetLayer) {
  if (connections.size() > 0) {
      Subsampling2DConnection c = (Subsampling2DConnection) connections.get(0);
      if (targetLayer == c.getOutputLayer()) {
    Environment.getInstance().getExecutionStrategy().execute(this, targetLayer.getUnitCount(connections));
      } else {
    Environment.getInstance().getExecutionStrategy().execute(this, Util.getOppositeLayer(c, targetLayer).getUnitCount(connections));
      }
  }
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  assertEquals(3, conv.getOutputFeatureMapColumns(), 0);
  assertEquals(3, conv.getOutputFeatureMapRows(), 0);
  assertEquals(2, conv.getOutputFilters(), 0);

  // subsampling dimensions
  Subsampling2DConnection sub = cf.subsampling2D(new Layer(), new Layer(), 5, 5, 2, 2, 3);

  assertEquals(2, sub.getOutputFeatureMapColumns(), 0);
  assertEquals(2, sub.getOutputFeatureMapRows(), 0);
  assertEquals(3, sub.getFilters(), 0);
    }
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  Conv2DConnection cc = (Conv2DConnection) nn.getInputLayer().getConnections().get(0);
  assertEquals(28, cc.getOutputFeatureMapRows(), 0);
  assertEquals(28, cc.getOutputFeatureMapColumns(), 0);
  assertEquals(6, cc.getOutputFilters(), 0);

  Subsampling2DConnection sc = (Subsampling2DConnection) l.getConnections().get(2);
  l = l.getConnections().get(2).getOutputLayer();
  assertEquals(14, sc.getOutputFeatureMapRows(), 0);
  assertEquals(14, sc.getOutputFeatureMapColumns(), 0);
  assertEquals(6, sc.getFilters(), 0);

  cc = (Conv2DConnection) l.getConnections().get(1);
  l = l.getConnections().get(1).getOutputLayer();
  assertEquals(10, cc.getOutputFeatureMapRows(), 0);
  assertEquals(10, cc.getOutputFeatureMapColumns(), 0);
  assertEquals(16, cc.getOutputFilters(), 0);

  sc = (Subsampling2DConnection) l.getConnections().get(2);
  l = l.getConnections().get(2).getOutputLayer();
  assertEquals(5, sc.getOutputFeatureMapRows(), 0);
  assertEquals(5, sc.getOutputFeatureMapColumns(), 0);
  assertEquals(16, sc.getFilters(), 0);

  cc = (Conv2DConnection) l.getConnections().get(1);
  l = l.getConnections().get(1).getOutputLayer();
  assertEquals(1, cc.getOutputFeatureMapRows(), 0);
  assertEquals(1, cc.getOutputFeatureMapColumns(), 0);
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  Layer l = nn.getInputLayer().getConnections().get(0).getOutputLayer();
  assertEquals(24, cc.getOutputFeatureMapRows(), 0);
  assertEquals(24, cc.getOutputFeatureMapColumns(), 0);
  assertEquals(20, cc.getOutputFilters(), 0);

  Subsampling2DConnection sc = (Subsampling2DConnection) l.getConnections().get(2);
  l = l.getConnections().get(2).getOutputLayer();
  assertEquals(12, sc.getOutputFeatureMapRows(), 0);
  assertEquals(12, sc.getOutputFeatureMapColumns(), 0);
  assertEquals(20, sc.getFilters(), 0);

  cc = (Conv2DConnection) l.getConnections().get(1);
  l = l.getConnections().get(1).getOutputLayer();
  assertEquals(8, cc.getOutputFeatureMapRows(), 0);
  assertEquals(8, cc.getOutputFeatureMapColumns(), 0);
  assertEquals(50, cc.getOutputFilters(), 0);

  sc = (Subsampling2DConnection) l.getConnections().get(2);
  l = l.getConnections().get(2).getOutputLayer();
  assertEquals(4, sc.getOutputFeatureMapRows(), 0);
  assertEquals(4, sc.getOutputFeatureMapColumns(), 0);
  assertEquals(50, sc.getFilters(), 0);
  assertEquals(50 * 4 * 4, l.getConnections().get(0).getOutputUnitCount(), 0);

  Layer layer = l.getConnections().get(1).getOutputLayer();
  assertEquals(500, layer.getConnections().get(0).getOutputUnitCount(), 0);

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  Layer l = nn.getInputLayer().getConnections().get(0).getOutputLayer();
  assertEquals(2, cc.getOutputFeatureMapRows(), 0);
  assertEquals(2, cc.getOutputFeatureMapColumns(), 0);
  assertEquals(2, cc.getOutputFilters(), 0);

  Subsampling2DConnection sc = (Subsampling2DConnection) l.getConnections().get(2);
  l = l.getConnections().get(2).getOutputLayer();
  assertEquals(1, sc.getOutputFeatureMapRows(), 0);
  assertEquals(1, sc.getOutputFeatureMapColumns(), 0);
  assertEquals(2, sc.getFilters(), 0);
    }
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Related Classes of com.github.neuralnetworks.architecture.Subsampling2DConnection

  • com.github.neuralnetworks.architecture.types.NNFactory
  • com.github.neuralnetworks.calculation.neuronfunctions.AparapiSubsampling2D
  • com.github.neuralnetworks.tensor.TensorFactory
  • com.github.neuralnetworks.test.CNNTest
  • com.github.neuralnetworks.util.Util
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