Examples with IterationListener org.deeplearning4j.optimize.api.IterationListener used on opensource projects

Example 6 with IterationListener

use of org.deeplearning4j.optimize.api.IterationListener in project deeplearning4j by deeplearning4j.

the class BackTrackLineSearchTest method testBackTrackLineHessian.

@Test(expected = Exception.class)
public void testBackTrackLineHessian() {
    OptimizationAlgorithm optimizer = OptimizationAlgorithm.HESSIAN_FREE;
    DataSet data = irisIter.next();
    MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.RELU, 100, optimizer));
    network.init();
    IterationListener listener = new ScoreIterationListener(1);
    network.setListeners(Collections.singletonList(listener));
    network.fit(data.getFeatureMatrix(), data.getLabels());
}

Example 7 with IterationListener

use of org.deeplearning4j.optimize.api.IterationListener in project deeplearning4j by deeplearning4j.

the class TestSparkComputationGraph method testBasic.

@Test
public void testBasic() throws Exception {
    JavaSparkContext sc = this.sc;
    RecordReader rr = new CSVRecordReader(0, ",");
    rr.initialize(new FileSplit(new ClassPathResource("iris.txt").getTempFileFromArchive()));
    MultiDataSetIterator iter = new RecordReaderMultiDataSetIterator.Builder(1).addReader("iris", rr).addInput("iris", 0, 3).addOutputOneHot("iris", 4, 3).build();
    List<MultiDataSet> list = new ArrayList<>(150);
    while (iter.hasNext()) list.add(iter.next());
    ComputationGraphConfiguration config = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(0.1).graphBuilder().addInputs("in").addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(2).build(), "in").addLayer("out", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).build(), "dense").setOutputs("out").pretrain(false).backprop(true).build();
    ComputationGraph cg = new ComputationGraph(config);
    cg.init();
    TrainingMaster tm = new ParameterAveragingTrainingMaster(true, numExecutors(), 1, 10, 1, 0);
    SparkComputationGraph scg = new SparkComputationGraph(sc, cg, tm);
    scg.setListeners(Collections.singleton((IterationListener) new ScoreIterationListener(1)));
    JavaRDD<MultiDataSet> rdd = sc.parallelize(list);
    scg.fitMultiDataSet(rdd);
    //Try: fitting using DataSet
    DataSetIterator iris = new IrisDataSetIterator(1, 150);
    List<DataSet> list2 = new ArrayList<>();
    while (iris.hasNext()) list2.add(iris.next());
    JavaRDD<DataSet> rddDS = sc.parallelize(list2);
    scg.fit(rddDS);
}

Example 8 with IterationListener

use of org.deeplearning4j.optimize.api.IterationListener in project deeplearning4j by deeplearning4j.

the class EvalTest method testIris.

@Test
public void testIris() {
    // Network config
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).iterations(1).seed(42).learningRate(1e-6).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(2).activation(Activation.TANH).weightInit(WeightInit.XAVIER).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()).build();
    // Instantiate model
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(1)));
    // Train-test split
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    DataSet next = iter.next();
    next.shuffle();
    SplitTestAndTrain trainTest = next.splitTestAndTrain(5, new Random(42));
    // Train
    DataSet train = trainTest.getTrain();
    train.normalizeZeroMeanZeroUnitVariance();
    // Test
    DataSet test = trainTest.getTest();
    test.normalizeZeroMeanZeroUnitVariance();
    INDArray testFeature = test.getFeatureMatrix();
    INDArray testLabel = test.getLabels();
    // Fitting model
    model.fit(train);
    // Get predictions from test feature
    INDArray testPredictedLabel = model.output(testFeature);
    // Eval with class number
    //// Specify class num here
    Evaluation eval = new Evaluation(3);
    eval.eval(testLabel, testPredictedLabel);
    double eval1F1 = eval.f1();
    double eval1Acc = eval.accuracy();
    // Eval without class number
    //// No class num
    Evaluation eval2 = new Evaluation();
    eval2.eval(testLabel, testPredictedLabel);
    double eval2F1 = eval2.f1();
    double eval2Acc = eval2.accuracy();
    //Assert the two implementations give same f1 and accuracy (since one batch)
    assertTrue(eval1F1 == eval2F1 && eval1Acc == eval2Acc);
    Evaluation evalViaMethod = model.evaluate(new ListDataSetIterator(Collections.singletonList(test)));
    checkEvaluationEquality(eval, evalViaMethod);
    System.out.println(eval.getConfusionMatrix().toString());
    System.out.println(eval.getConfusionMatrix().toCSV());
    System.out.println(eval.getConfusionMatrix().toHTML());
    System.out.println(eval.confusionToString());
}

Example 9 with IterationListener

use of org.deeplearning4j.optimize.api.IterationListener in project deeplearning4j by deeplearning4j.

the class DataSetIteratorTest method runCifar.

public void runCifar(boolean preProcessCifar) throws Exception {
    final int height = 32;
    final int width = 32;
    int channels = 3;
    int outputNum = CifarLoader.NUM_LABELS;
    int numSamples = 10;
    int batchSize = 5;
    int iterations = 1;
    int seed = 123;
    int listenerFreq = iterations;
    CifarDataSetIterator cifar = new CifarDataSetIterator(batchSize, numSamples, new int[] { height, width, channels }, preProcessCifar, true);
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(seed).iterations(iterations).gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list().layer(0, new ConvolutionLayer.Builder(5, 5).nIn(channels).nOut(6).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()).layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] { 2, 2 }).build()).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(outputNum).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()).backprop(true).pretrain(false).setInputType(InputType.convolutionalFlat(height, width, channels));
    MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
    model.init();
    model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq)));
    model.fit(cifar);
    cifar.test(10);
    Evaluation eval = new Evaluation(cifar.getLabels());
    while (cifar.hasNext()) {
        DataSet testDS = cifar.next(batchSize);
        INDArray output = model.output(testDS.getFeatureMatrix());
        eval.eval(testDS.getLabels(), output);
    }
    System.out.println(eval.stats(true));
}

Example 10 with IterationListener

use of org.deeplearning4j.optimize.api.IterationListener in project deeplearning4j by deeplearning4j.

the class MultiLayerNeuralNetConfigurationTest method testIterationListener.

@Test
public void testIterationListener() {
    MultiLayerNetwork model1 = new MultiLayerNetwork(getConf());
    model1.init();
    model1.setListeners(Collections.singletonList((IterationListener) new ScoreIterationListener(1)));
    MultiLayerNetwork model2 = new MultiLayerNetwork(getConf());
    model2.setListeners(Collections.singletonList((IterationListener) new ScoreIterationListener(1)));
    model2.init();
    Layer[] l1 = model1.getLayers();
    for (int i = 0; i < l1.length; i++) assertTrue(l1[i].getListeners() != null && l1[i].getListeners().size() == 1);
    Layer[] l2 = model2.getLayers();
    for (int i = 0; i < l2.length; i++) assertTrue(l2[i].getListeners() != null && l2[i].getListeners().size() == 1);
}