LenetMinstExampleCustom.java

package org.deeplearning4j.examples.convolution;

import org.canova.api.records.reader.RecordReader;
import org.canova.api.records.reader.impl.CSVRecordReader;
import org.canova.api.split.FileSplit;
import org.deeplearning4j.datasets.canova.RecordReaderDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
import org.deeplearning4j.nn.conf.layers.setup.ConvolutionLayerSetup;
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.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

//import org.deeplearning4j.nn.conf.LearningRatePolicy;
import java.io.File;

/**
 * Created by agibsonccc on 9/16/15.
 */
public class LenetMnistExampleCustom {
    private static final Logger log = LoggerFactory.getLogger(LenetMnistExampleCustom.class);

    public static void main(String[] args) throws Exception {

        /*
        int nChannels = 1;
        int outputNum = 10;
        int batchSize = 64;
        int nEpochs = 10;
        int iterations = 1;
        int seed = 123;
        */

        int iterations = 1;
        int nChannels = 1;

        int seed = 123;
        double learningRate = 0.01;
        int batchSize = 3500;
        int nEpochs = 30;

//        int numInputs = 2;
        int outputNum = 2;
//        int numHiddenNodes = 20;


        log.info("Load data....");
//        DataSetIterator dataSetIteratorTrain = new MnistDataSetIterator(batchSize,true,12345);
//        DataSetIterator dataSetIteratorTest = new MnistDataSetIterator(batchSize,false,12345);

        //Load the training data:
//        RecordReader rr = new CSVRecordReader();
//        rr.initialize(new FileSplit(new File("src/main/resources/classification/linear_data_train.csv")));
//        org.deeplearning4j.datasets.iterator.DataSetIterator dataSetIteratorTrain = new RecordReaderDataSetIterator(rr,batchSize,0,2);


        RecordReader rrTrain = new CSVRecordReader();
        rrTrain.initialize(new FileSplit(new File("src/main/resources/classification/train.csv")));
        org.deeplearning4j.datasets.iterator.DataSetIterator dataSetIteratorTrain = new RecordReaderDataSetIterator(rrTrain,batchSize,0,2);

        //Load the test/evaluation data:
        RecordReader rrTest = new CSVRecordReader();
        rrTest.initialize(new FileSplit(new File("src/main/resources/classification/test.csv")));
        org.deeplearning4j.datasets.iterator.DataSetIterator dataSetIteratorTest = new RecordReaderDataSetIterator(rrTest,batchSize,0,2);

        log.info("Build model....");
        MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .iterations(iterations)
                .regularization(true).l2(0.0005)
                .learningRate(learningRate)//.biasLearningRate(0.02)
                //.learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(0.001).lrPolicyPower(0.75)
                .weightInit(WeightInit.XAVIER)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .updater(Updater.NESTEROVS).momentum(0.9)
                .list()
                .layer(0, new ConvolutionLayer.Builder(5, 5)
                        .nIn(nChannels)
                        .stride(1, 1)
                        .nOut(20)
//                        .nOut(outputNum)
                        .activation("identity")
                        .build())
                .layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
                        .kernelSize(2,2)
                        .stride(2,2)
                        .build())
                .layer(2, new ConvolutionLayer.Builder(5, 5)
                        .nIn(nChannels)
                        .stride(1, 1)
                        .nOut(50)
                        .activation("identity")
                        .build())
                .layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
                        .kernelSize(2,2)
                        .stride(2,2)
                        .build())
                .layer(4, new DenseLayer.Builder().activation("relu")
                        .nOut(500).build())
                .layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .nOut(outputNum)
                        .activation("softmax")
                        .build())
                .backprop(true).pretrain(false);
        new ConvolutionLayerSetup(builder,100, 90,1);

        MultiLayerConfiguration conf = builder.build();
        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();


        log.info("Train model....");
        model.setListeners(new ScoreIterationListener(1));
        for( int i=0; i<nEpochs; i++ ) {
            model.fit(dataSetIteratorTrain);

            log.info("*** Completed epoch {} ***", i);

            log.info("Evaluate model....");
            Evaluation eval = new Evaluation(outputNum);
            while(dataSetIteratorTest.hasNext()){
                DataSet ds = dataSetIteratorTest.next();
                INDArray output = model.output(ds.getFeatureMatrix(), false);
                eval.eval(ds.getLabels(), output);
            }
            log.info(eval.stats());
            dataSetIteratorTest.reset();
        }
        log.info("****************Example finished********************");
    }
}