DeepAutoEncoderExample.java

package org.deeplearning4j.examples.unsupervised.deepbelief;

import org.deeplearning4j.datasets.fetchers.MnistDataFetcher;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.RBM;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.Collections;

/**
 * ***** NOTE: This example has not been tuned. It requires additional work to produce sensible results *****
 *
 * @author Adam Gibson
 */
public class DeepAutoEncoderExample {

    private static Logger log = LoggerFactory.getLogger(DeepAutoEncoderExample.class);

    public static void main(String[] args) throws Exception {
        final int numRows = 28;
        final int numColumns = 28;
        int seed = 123;
        int numSamples = MnistDataFetcher.NUM_EXAMPLES;
        int batchSize = 1000;
        int iterations = 1;
        int listenerFreq = iterations/5;

        log.info("Load data....");
        DataSetIterator iter = new MnistDataSetIterator(batchSize,numSamples,true);

        log.info("Build model....");
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(iterations)
                .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
                .list()
                .layer(0, new RBM.Builder().nIn(numRows * numColumns).nOut(1000).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
                .layer(1, new RBM.Builder().nIn(1000).nOut(500).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
                .layer(2, new RBM.Builder().nIn(500).nOut(250).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
                .layer(3, new RBM.Builder().nIn(250).nOut(100).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
                .layer(4, new RBM.Builder().nIn(100).nOut(30).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build()) //encoding stops
                .layer(5, new RBM.Builder().nIn(30).nOut(100).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build()) //decoding starts
                .layer(6, new RBM.Builder().nIn(100).nOut(250).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
                .layer(7, new RBM.Builder().nIn(250).nOut(500).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
                .layer(8, new RBM.Builder().nIn(500).nOut(1000).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build())
                .layer(9, new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.SIGMOID).nIn(1000).nOut(numRows*numColumns).build())
                .pretrain(true).backprop(true)
                .build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();

        model.setListeners(new ScoreIterationListener(listenerFreq));

        log.info("Train model....");
        while(iter.hasNext()) {
            DataSet next = iter.next();
            model.fit(new DataSet(next.getFeatureMatrix(),next.getFeatureMatrix()));
        }


    }




}