/**
* 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);
}
