Examples of com.github.neuralnetworks.calculation.ConnectionCalculator.calculate()

com.github.neuralnetworks.calculation.ConnectionCalculator.calculate()
@param connections - list of connections to calculate @param valuesProvider - values provider for the connections @param targetLayer - the target layer, to which "output" is associated


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


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


  assertEquals(3, o.get(0, 0), 0);
  assertEquals(4, o.get(1, 0), 0);
  assertEquals(7, o.get(2, 0), 0);
  assertEquals(8, o.get(3, 0), 0);

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  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);

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  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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  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(3, o.get(0, 0, 0, 0), 0);
  assertEquals(4, o.get(0, 0, 1, 0), 0);

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  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);


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


  ValuesProvider vp = TensorFactory.tensorProvider(c, 2, true);
  TensorFactory.copy(activations.get(c.getOutputLayer()), vp.get(c.getOutputLayer()));


  BackpropagationMaxPooling2D bp = new BackpropagationMaxPooling2D();

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  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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