Examples with IrisDataSetIterator org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator used on opensource projects

Example 41 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class TestRemoteReceiver method testRemoteFull.

@Test
@Ignore
public void testRemoteFull() throws Exception {
    //Use this in conjunction with startRemoteUI()
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).list().layer(0, new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build()).layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(4).nOut(3).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    StatsStorageRouter ssr = new RemoteUIStatsStorageRouter("http://localhost:9000");
    net.setListeners(new StatsListener(ssr), new ScoreIterationListener(1));
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    for (int i = 0; i < 500; i++) {
        net.fit(iter);
        //            Thread.sleep(100);
        Thread.sleep(100);
    }
}

Example 42 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class TestSparkComputationGraph method testSeedRepeatability.

@Test
public void testSeedRepeatability() throws Exception {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).updater(Updater.RMSPROP).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in").addLayer("0", new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build(), "in").addLayer("1", new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(4).nOut(3).activation(Activation.SOFTMAX).build(), "0").setOutputs("1").pretrain(false).backprop(true).build();
    Nd4j.getRandom().setSeed(12345);
    ComputationGraph n1 = new ComputationGraph(conf);
    n1.init();
    Nd4j.getRandom().setSeed(12345);
    ComputationGraph n2 = new ComputationGraph(conf);
    n2.init();
    Nd4j.getRandom().setSeed(12345);
    ComputationGraph n3 = new ComputationGraph(conf);
    n3.init();
    SparkComputationGraph sparkNet1 = new SparkComputationGraph(sc, n1, new ParameterAveragingTrainingMaster.Builder(1).workerPrefetchNumBatches(5).batchSizePerWorker(5).averagingFrequency(1).repartionData(Repartition.Always).rngSeed(12345).build());
    //Training master IDs are only unique if they are created at least 1 ms apart...
    Thread.sleep(100);
    SparkComputationGraph sparkNet2 = new SparkComputationGraph(sc, n2, new ParameterAveragingTrainingMaster.Builder(1).workerPrefetchNumBatches(5).batchSizePerWorker(5).averagingFrequency(1).repartionData(Repartition.Always).rngSeed(12345).build());
    Thread.sleep(100);
    SparkComputationGraph sparkNet3 = new SparkComputationGraph(sc, n3, new ParameterAveragingTrainingMaster.Builder(1).workerPrefetchNumBatches(5).batchSizePerWorker(5).averagingFrequency(1).repartionData(Repartition.Always).rngSeed(98765).build());
    List<DataSet> data = new ArrayList<>();
    DataSetIterator iter = new IrisDataSetIterator(1, 150);
    while (iter.hasNext()) data.add(iter.next());
    JavaRDD<DataSet> rdd = sc.parallelize(data);
    sparkNet1.fit(rdd);
    sparkNet2.fit(rdd);
    sparkNet3.fit(rdd);
    INDArray p1 = sparkNet1.getNetwork().params();
    INDArray p2 = sparkNet2.getNetwork().params();
    INDArray p3 = sparkNet3.getNetwork().params();
    sparkNet1.getTrainingMaster().deleteTempFiles(sc);
    sparkNet2.getTrainingMaster().deleteTempFiles(sc);
    sparkNet3.getTrainingMaster().deleteTempFiles(sc);
    assertEquals(p1, p2);
    assertNotEquals(p1, p3);
}

Example 43 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator 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 44 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class TestSparkLayer method testIris2.

@Test
public void testIris2() throws Exception {
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(10).learningRate(1e-1).layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(4).nOut(3).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()).build();
    System.out.println("Initializing network");
    SparkDl4jLayer master = new SparkDl4jLayer(sc, conf);
    DataSet d = new IrisDataSetIterator(150, 150).next();
    d.normalizeZeroMeanZeroUnitVariance();
    d.shuffle();
    List<DataSet> next = d.asList();
    JavaRDD<DataSet> data = sc.parallelize(next);
    OutputLayer network2 = (OutputLayer) master.fitDataSet(data);
    Evaluation evaluation = new Evaluation();
    evaluation.eval(d.getLabels(), network2.output(d.getFeatureMatrix()));
    System.out.println(evaluation.stats());
}

Example 45 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator in project deeplearning4j by deeplearning4j.

the class TestSparkMultiLayerParameterAveraging method testFromSvmLightBackprop.

@Test
public void testFromSvmLightBackprop() throws Exception {
    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), new ClassPathResource("svmLight/iris_svmLight_0.txt").getTempFileFromArchive().getAbsolutePath()).toJavaRDD().map(new Function<LabeledPoint, LabeledPoint>() {

        @Override
        public LabeledPoint call(LabeledPoint v1) throws Exception {
            return new LabeledPoint(v1.label(), Vectors.dense(v1.features().toArray()));
        }
    });
    Nd4j.ENFORCE_NUMERICAL_STABILITY = true;
    DataSet d = new IrisDataSetIterator(150, 150).next();
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(123).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(10).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(100).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(100).nOut(3).activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER).build()).backprop(true).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    System.out.println("Initializing network");
    SparkDl4jMultiLayer master = new SparkDl4jMultiLayer(sc, conf, new ParameterAveragingTrainingMaster(true, numExecutors(), 1, 5, 1, 0));
    MultiLayerNetwork network2 = master.fitLabeledPoint(data);
    Evaluation evaluation = new Evaluation();
    evaluation.eval(d.getLabels(), network2.output(d.getFeatureMatrix()));
    System.out.println(evaluation.stats());
}