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;
	}