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

Example 91 with IrisDataSetIterator

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

the class TestEarlyStoppingSparkCompGraph method getIris.

private JavaRDD<DataSet> getIris() {
    JavaSparkContext sc = getContext();
    IrisDataSetIterator iter = new IrisDataSetIterator(1, 150);
    List<DataSet> list = new ArrayList<>(150);
    while (iter.hasNext()) list.add(iter.next());
    return sc.parallelize(list);
}

Example 92 with IrisDataSetIterator

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

the class TestMiscFunctions method testFeedForwardWithKey.

@Test
public void testFeedForwardWithKey() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build()).layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).activation(Activation.SOFTMAX).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    DataSet ds = iter.next();
    List<INDArray> expected = new ArrayList<>();
    List<Tuple2<Integer, INDArray>> mapFeatures = new ArrayList<>();
    int count = 0;
    int arrayCount = 0;
    Random r = new Random(12345);
    while (count < 150) {
        //1 to 5 inclusive examples
        int exampleCount = r.nextInt(5) + 1;
        if (count + exampleCount > 150)
            exampleCount = 150 - count;
        INDArray subset = ds.getFeatures().get(NDArrayIndex.interval(count, count + exampleCount), NDArrayIndex.all());
        expected.add(net.output(subset, false));
        mapFeatures.add(new Tuple2<>(arrayCount, subset));
        arrayCount++;
        count += exampleCount;
    }
    JavaPairRDD<Integer, INDArray> rdd = sc.parallelizePairs(mapFeatures);
    SparkDl4jMultiLayer multiLayer = new SparkDl4jMultiLayer(sc, net, null);
    Map<Integer, INDArray> map = multiLayer.feedForwardWithKey(rdd, 16).collectAsMap();
    for (int i = 0; i < expected.size(); i++) {
        INDArray exp = expected.get(i);
        INDArray act = map.get(i);
        assertEquals(exp, act);
    }
}

Example 93 with IrisDataSetIterator

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

the class TestMiscFunctions method testFeedForwardWithKeyGraph.

@Test
public void testFeedForwardWithKeyGraph() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in1", "in2").addLayer("0", new DenseLayer.Builder().nIn(4).nOut(3).build(), "in1").addLayer("1", new DenseLayer.Builder().nIn(4).nOut(3).build(), "in2").addLayer("2", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(6).nOut(3).activation(Activation.SOFTMAX).build(), "0", "1").setOutputs("2").build();
    ComputationGraph net = new ComputationGraph(conf);
    net.init();
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    DataSet ds = iter.next();
    List<INDArray> expected = new ArrayList<>();
    List<Tuple2<Integer, INDArray[]>> mapFeatures = new ArrayList<>();
    int count = 0;
    int arrayCount = 0;
    Random r = new Random(12345);
    while (count < 150) {
        //1 to 5 inclusive examples
        int exampleCount = r.nextInt(5) + 1;
        if (count + exampleCount > 150)
            exampleCount = 150 - count;
        INDArray subset = ds.getFeatures().get(NDArrayIndex.interval(count, count + exampleCount), NDArrayIndex.all());
        expected.add(net.outputSingle(false, subset, subset));
        mapFeatures.add(new Tuple2<>(arrayCount, new INDArray[] { subset, subset }));
        arrayCount++;
        count += exampleCount;
    }
    JavaPairRDD<Integer, INDArray[]> rdd = sc.parallelizePairs(mapFeatures);
    SparkComputationGraph graph = new SparkComputationGraph(sc, net, null);
    Map<Integer, INDArray[]> map = graph.feedForwardWithKey(rdd, 16).collectAsMap();
    for (int i = 0; i < expected.size(); i++) {
        INDArray exp = expected.get(i);
        INDArray act = map.get(i)[0];
        assertEquals(exp, act);
    }
}

Example 94 with IrisDataSetIterator

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

the class TestSparkMultiLayerParameterAveraging method testFromSvmLight.

@Test
public void testFromSvmLight() 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()));
        }
    });
    DataSet d = new IrisDataSetIterator(150, 150).next();
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(123).optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).iterations(100).miniBatch(true).maxNumLineSearchIterations(10).list().layer(0, new RBM.Builder(RBM.HiddenUnit.RECTIFIED, RBM.VisibleUnit.GAUSSIAN).nIn(4).nOut(100).weightInit(WeightInit.XAVIER).activation(Activation.RELU).lossFunction(LossFunctions.LossFunction.RMSE_XENT).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(false).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    System.out.println("Initializing network");
    SparkDl4jMultiLayer master = new SparkDl4jMultiLayer(sc, getBasicConf(), 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());
}

Example 95 with IrisDataSetIterator

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

the class TestListeners method testStatsCollection.

@Test
public void testStatsCollection() {
    JavaSparkContext sc = getContext();
    int nExecutors = numExecutors();
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(123).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).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()).pretrain(false).backprop(true).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).batchSizePerWorker(5).averagingFrequency(6).build();
    SparkDl4jMultiLayer net = new SparkDl4jMultiLayer(sc, conf, tm);
    //In-memory
    StatsStorage ss = new MapDBStatsStorage();
    net.setListeners(ss, Collections.singletonList(new StatsListener(null)));
    List<DataSet> list = new IrisDataSetIterator(120, 150).next().asList();
    //120 examples, 4 executors, 30 examples per executor -> 6 updates of size 5 per executor
    JavaRDD<DataSet> rdd = sc.parallelize(list);
    net.fit(rdd);
    List<String> sessions = ss.listSessionIDs();
    System.out.println("Sessions: " + sessions);
    assertEquals(1, sessions.size());
    String sid = sessions.get(0);
    List<String> typeIDs = ss.listTypeIDsForSession(sid);
    List<String> workers = ss.listWorkerIDsForSession(sid);
    System.out.println(sid + "\t" + typeIDs + "\t" + workers);
    List<Persistable> lastUpdates = ss.getLatestUpdateAllWorkers(sid, StatsListener.TYPE_ID);
    System.out.println(lastUpdates);
    System.out.println("Static info:");
    for (String wid : workers) {
        Persistable staticInfo = ss.getStaticInfo(sid, StatsListener.TYPE_ID, wid);
        System.out.println(sid + "\t" + wid);
    }
    assertEquals(1, typeIDs.size());
    assertEquals(numExecutors(), workers.size());
    String firstWorker = workers.get(0);
    String firstWorkerSubstring = workers.get(0).substring(0, firstWorker.length() - 1);
    for (String wid : workers) {
        String widSubstring = wid.substring(0, wid.length() - 1);
        assertEquals(firstWorkerSubstring, widSubstring);
        String counterVal = wid.substring(wid.length() - 1, wid.length());
        int cv = Integer.parseInt(counterVal);
        assertTrue(0 <= cv && cv < numExecutors());
    }
}