AND Gate Example

// package nn.example;

import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration.ListBuilder;
import org.deeplearning4j.nn.conf.distribution.UniformDistribution;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer.Builder;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;

public class ANDExample {
	private static final int SEED = 100;
	private static final int ITERATIONS = 350;
	private static final String OUTPUT_ACTIVATION = "sigmoid";
	private static final double LEARNING_RATE = 0.7;
	
	private static final int INPUT_NEURONS = 2;
	private static final int HIDDEN_NEURONS = 4;
	private static final int OUTPUT_NEURONS = 1;
	
	public static void main(String[] args) {		
		INDArray input = Nd4j.zeros(4, 2);
		INDArray labels = Nd4j.zeros(4, 1);
		input.putScalar(new int[] { 0, 0 }, 0);
		input.putScalar(new int[] { 0, 1 }, 0);
		labels.putScalar(new int[] { 0, 0 }, 0);
	
		input.putScalar(new int[] { 1, 0 }, 1);
		input.putScalar(new int[] { 1, 1 }, 0);
		labels.putScalar(new int[] { 1, 0 }, 0);
		
		input.putScalar(new int[] { 2, 0 }, 0);
		input.putScalar(new int[] { 2, 1 }, 1);
		labels.putScalar(new int[] { 2, 0 }, 0);
		
		input.putScalar(new int[] { 3, 0 }, 1);
		input.putScalar(new int[] { 3, 1 }, 1);
		labels.putScalar(new int[] { 3, 0 }, 1);
		
		DataSet ds = new DataSet(input, labels);

		NeuralNetConfiguration.Builder builder = new NeuralNetConfiguration.Builder();
		builder.iterations(ITERATIONS);
		builder.learningRate(LEARNING_RATE);
		builder.seed(SEED);
		builder.useDropConnect(false);
		builder.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT);
		builder.biasInit(0);
		builder.miniBatch(false);

		ListBuilder listBuilder = builder.list();

		// Hidden Layer
		DenseLayer.Builder hiddenLayerBuilder = new DenseLayer.Builder();
		hiddenLayerBuilder.nIn(INPUT_NEURONS);
		hiddenLayerBuilder.nOut(HIDDEN_NEURONS);
		hiddenLayerBuilder.activation("sigmoid");
		hiddenLayerBuilder.weightInit(WeightInit.DISTRIBUTION);
		hiddenLayerBuilder.dist(new UniformDistribution(0, 1));
		listBuilder.layer(0, hiddenLayerBuilder.build());

		// Output Layer
		Builder outputLayerBuilder = new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD);
		outputLayerBuilder.nIn(HIDDEN_NEURONS);
		outputLayerBuilder.nOut(OUTPUT_NEURONS);
		outputLayerBuilder.activation(OUTPUT_ACTIVATION);
		outputLayerBuilder.weightInit(WeightInit.DISTRIBUTION);
		outputLayerBuilder.dist(new UniformDistribution(0, 1));
		listBuilder.layer(1, outputLayerBuilder.build());

		listBuilder.pretrain(false);
		listBuilder.backprop(true);
		
		MultiLayerConfiguration conf = listBuilder.build();
		MultiLayerNetwork net = new MultiLayerNetwork(conf);
		
		net.init();
		
		Layer[] layers = net.getLayers();
		int totalNumParams = 0;
		for (int i = 0; i < layers.length; i++) {
			int nParams = layers[i].numParams();
			System.out.println("Number of parameters in layer " + i + ": " + nParams);
			totalNumParams += nParams;
		}
		System.out.println("Total number of network parameters: " + totalNumParams);
		
		net.setListeners(new ScoreIterationListener(100));
		
		System.out.println(net.output(input));
		net.fit(ds);
		System.out.println(net.output(input));
	}
}