MyDBNMnistExample experimentations

MyDBNMnistExample.java

package org.deeplearning4j.examples.deepbelief;

import org.deeplearning4j.datasets.iterator.DataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.RBM;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.ui.weights.HistogramIterationListener;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.SplitTestAndTrain;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.*;

/**
 * Created by agibsonccc on 9/11/14.
 */
public class MyDBNMnistExample {

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

    public static void main(String[] args) throws Exception {
        final int numRows = 28;
        final int numColumns = 28;
        int outputNum = 10;
        int numSamples = 10000;
        int batchSize = 500;
        int iterations = 10;
        int seed = 123;
        int listenerFreq = batchSize / 10;
        int splitTrainNum = (int) (batchSize*.8);
        DataSet mnist;
        SplitTestAndTrain trainTest;
        DataSet trainInput;
        List<INDArray> testInput = new ArrayList<>();
        List<INDArray> testLabels = new ArrayList<>();

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

        log.info("Build model....");
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                //.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
                //.gradientNormalizationThreshold(1.0)
                .iterations(iterations)
                .learningRate(1e-6)
                .regularization(true)
                //.momentum(0.5)
                //.momentumAfter(Collections.singletonMap(3, 0.9))
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .list(2)
                .layer(0, new RBM.Builder()
                        .nIn(numRows*numColumns)
                        .nOut(outputNum)
                        .weightInit(WeightInit.RELU)
                        .activation("relu")
                        .k(1) // default
                        .sparsity(0.0D) // default
                        .visibleUnit(RBM.VisibleUnit.BINARY) // default
                        .hiddenUnit(RBM.HiddenUnit.BINARY) // default
                        .lossFunction(LossFunction.RECONSTRUCTION_CROSSENTROPY) // default
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunction.MCXENT)
                        .activation("softmax")
                        .nIn(outputNum)
                        .nOut(outputNum).build())
                .pretrain(true)
                .backprop(false)
                .build();

        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq), new HistogramIterationListener(listenerFreq)));

        log.info("Train model....");
        while(mnistIter.hasNext()) {
            mnist = mnistIter.next();
            trainTest = mnist.splitTestAndTrain(splitTrainNum, new Random(seed)); // train set that is the result
            trainInput = trainTest.getTrain(); // get feature matrix and labels for training
            testInput.add(trainTest.getTest().getFeatureMatrix());
            testLabels.add(trainTest.getTest().getLabels());
            model.fit(trainInput);
        }

        log.info("Evaluate model....");
        Evaluation eval = new Evaluation(outputNum);
        for(int i = 0; i < testInput.size(); i++) {
            INDArray output = model.output(testInput.get(i));
            eval.eval(testLabels.get(i), output);
        }

        log.info(eval.stats());
        log.info("****************Example finished********************");

    }

}