139dd031a4cc61198e1c07b54a782271bc3426dd,src/main/java/org/encog/ensemble/ml/mlp/factory/MultiLayerPerceptronFactory.java,MultiLayerPerceptronFactory,createML,#number#number#,52
Before Change
BasicNetwork network = new BasicNetwork();
network.addLayer(new BasicLayer(activation,false,inputs)); //(inputs));
for (Integer layerSize: layers)
network.addLayer(new BasicLayer(activation,true,layerSize * sizeMultiplier));
network.addLayer(new BasicLayer(lastLayerActivation,true,outputs));
network.getStructure().finalizeStructure();
network.reset();
After Change
@Override
public MLMethod createML(int inputs, int outputs) {
BasicNetwork network = new BasicNetwork();
if(this.dropoutRates != null)
{
network.addLayer(new BasicLayer(activation,false,inputs, dropoutRates.get(0))); //(inputs));
} else {
network.addLayer(new BasicLayer(activation,false,inputs)); //(inputs));
}
for (int i = 0; i < layers.size(); i++)
{
if(this.dropoutRates != null)
{
network.addLayer(new BasicLayer(activation,true,layers.get(i) * sizeMultiplier, dropoutRates.get(i + 1)));
} else {
network.addLayer(new BasicLayer(activation,true,layers.get(i) * sizeMultiplier));
}
}
if(dropoutRates != null) {
network.addLayer(new BasicLayer(lastLayerActivation,true,outputs, dropoutRates.get(dropoutRates.size() - 1)));
} else {
network.addLayer(new BasicLayer(lastLayerActivation,true,outputs));
}
network.getStructure().finalizeStructure(dropoutRates != null);
network.reset();
return network;
}