/** * Reinitialises all the output weights of a cascade network within a PSO. * * <p>{@inheritDoc} */ @Override public void performReaction(E algorithm) { NNDataTrainingProblem problem = (NNDataTrainingProblem) algorithm.getOptimisationProblem(); NeuralNetwork network = problem.getNeuralNetwork(); int precedingLayersSize = network.getArchitecture().getArchitectureBuilder().getLayerConfigurations().get(0).getSize() + network .getArchitecture() .getArchitectureBuilder() .getLayerConfigurations() .get(1) .getSize(); if (network.getArchitecture().getArchitectureBuilder().getLayerConfigurations().get(0).isBias()) precedingLayersSize++; int outputLayerSize = network .getArchitecture() .getArchitectureBuilder() .getLayerConfigurations() .get(2) .getSize(); int nrOfweightsToDo = precedingLayersSize * outputLayerSize; Topology<? extends Entity> entities = algorithm.getTopology(); for (Entity entity : entities) { DynamicParticle particle = (DynamicParticle) entity; Vector position = (Vector) particle.getPosition(); Vector velocity = (Vector) particle.getVelocity(); for (int curElement = position.size() - nrOfweightsToDo; curElement < position.size(); ++curElement) { ((Real) position.get(curElement)).randomize(randomGenerator); velocity.setReal(curElement, 0.0); } } }
@Test public void testBuildArchitecture() { NeuralNetwork network = new NeuralNetwork(); network.getArchitecture().getArchitectureBuilder().addLayer(new LayerConfiguration(5)); network.getArchitecture().getArchitectureBuilder().addLayer(new LayerConfiguration(3)); network.getArchitecture().getArchitectureBuilder().addLayer(new LayerConfiguration(3, false)); network.getArchitecture().getArchitectureBuilder().addLayer(new LayerConfiguration(2)); network.getArchitecture().getArchitectureBuilder().getLayerBuilder().setDomain("R(-3,3)"); network.initialize(); // assert num layers Assert.assertEquals(4, network.getArchitecture().getNumLayers()); // assert fully connected Assert.assertEquals(36, network.getWeights().size()); // assert biasses Assert.assertEquals(true, network.getArchitecture().getLayers().get(0).isBias()); int layerSize = network.getArchitecture().getLayers().get(0).size(); Assert.assertEquals(6, layerSize); Assert.assertEquals( -1, network.getArchitecture().getLayers().get(0).getNeuralInput(layerSize - 1), Maths.EPSILON); Assert.assertEquals(true, network.getArchitecture().getLayers().get(1).isBias()); layerSize = network.getArchitecture().getLayers().get(1).size(); Assert.assertEquals(4, layerSize); Assert.assertEquals( -1, network.getArchitecture().getLayers().get(1).getNeuralInput(layerSize - 1), Maths.EPSILON); Assert.assertEquals(false, network.getArchitecture().getLayers().get(2).isBias()); layerSize = network.getArchitecture().getLayers().get(2).size(); Assert.assertEquals(3, layerSize); Assert.assertEquals(false, network.getArchitecture().getLayers().get(3).isBias()); layerSize = network.getArchitecture().getLayers().get(3).size(); Assert.assertEquals(2, layerSize); }