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

Example 56 with IrisDataSetIterator

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

the class TestEarlyStoppingCompGraph method testEarlyStoppingIris.

@Test
public void testEarlyStoppingIris() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in").addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in").setOutputs("0").pretrain(false).backprop(true).build();
    ComputationGraph net = new ComputationGraph(conf);
    net.setListeners(new ScoreIterationListener(1));
    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>().epochTerminationConditions(new MaxEpochsTerminationCondition(5)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES)).scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();
    IEarlyStoppingTrainer<ComputationGraph> trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter);
    EarlyStoppingResult<ComputationGraph> result = trainer.fit();
    System.out.println(result);
    assertEquals(5, result.getTotalEpochs());
    assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
    Map<Integer, Double> scoreVsIter = result.getScoreVsEpoch();
    assertEquals(5, scoreVsIter.size());
    String expDetails = esConf.getEpochTerminationConditions().get(0).toString();
    assertEquals(expDetails, result.getTerminationDetails());
    ComputationGraph out = result.getBestModel();
    assertNotNull(out);
    //Check that best score actually matches (returned model vs. manually calculated score)
    ComputationGraph bestNetwork = result.getBestModel();
    irisIter.reset();
    double score = bestNetwork.score(irisIter.next());
    assertEquals(result.getBestModelScore(), score, 1e-2);
}

Example 57 with IrisDataSetIterator

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

the class TestEarlyStoppingCompGraph method testBadTuning.

@Test
public void testBadTuning() {
    //Test poor tuning (high LR): should terminate on MaxScoreIterationTerminationCondition
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).learningRate(//Intentionally huge LR
    5.0).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in").addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in").setOutputs("0").pretrain(false).backprop(true).build();
    ComputationGraph net = new ComputationGraph(conf);
    net.setListeners(new ScoreIterationListener(1));
    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>().epochTerminationConditions(new MaxEpochsTerminationCondition(5000)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES), //Initial score is ~2.5
    new MaxScoreIterationTerminationCondition(10)).scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();
    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter);
    EarlyStoppingResult result = trainer.fit();
    assertTrue(result.getTotalEpochs() < 5);
    assertEquals(EarlyStoppingResult.TerminationReason.IterationTerminationCondition, result.getTerminationReason());
    String expDetails = new MaxScoreIterationTerminationCondition(10).toString();
    assertEquals(expDetails, result.getTerminationDetails());
    assertEquals(0, result.getBestModelEpoch());
    assertNotNull(result.getBestModel());
}

Example 58 with IrisDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator 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 59 with IrisDataSetIterator

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

the class EvaluationToolsTests method testRocMultiToHtml.

@Test
public void testRocMultiToHtml() throws Exception {
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(4).activation(Activation.TANH).build()).layer(1, new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    NormalizerStandardize ns = new NormalizerStandardize();
    DataSet ds = iter.next();
    ns.fit(ds);
    ns.transform(ds);
    for (int i = 0; i < 30; i++) {
        net.fit(ds);
    }
    ROCMultiClass roc = new ROCMultiClass(20);
    iter.reset();
    INDArray f = ds.getFeatures();
    INDArray l = ds.getLabels();
    INDArray out = net.output(f);
    roc.eval(l, out);
    String str = EvaluationTools.rocChartToHtml(roc, Arrays.asList("setosa", "versicolor", "virginica"));
//        System.out.println(str);
}

Example 60 with IrisDataSetIterator

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

the class GradientCheckTests method testAutoEncoder.

@Test
public void testAutoEncoder() {
    //As above (testGradientMLP2LayerIrisSimple()) but with L2, L1, and both L2/L1 applied
    //Need to run gradient through updater, so that L2 can be applied
    String[] activFns = { "sigmoid", "tanh" };
    //If true: run some backprop steps first
    boolean[] characteristic = { false, true };
    LossFunction[] lossFunctions = { LossFunction.MCXENT, LossFunction.MSE };
    //i.e., lossFunctions[i] used with outputActivations[i] here
    String[] outputActivations = { "softmax", "tanh" };
    DataNormalization scaler = new NormalizerMinMaxScaler();
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    scaler.fit(iter);
    iter.setPreProcessor(scaler);
    DataSet ds = iter.next();
    INDArray input = ds.getFeatureMatrix();
    INDArray labels = ds.getLabels();
    NormalizerStandardize norm = new NormalizerStandardize();
    norm.fit(ds);
    norm.transform(ds);
    double[] l2vals = { 0.2, 0.0, 0.2 };
    //i.e., use l2vals[i] with l1vals[i]
    double[] l1vals = { 0.0, 0.3, 0.3 };
    for (String afn : activFns) {
        for (boolean doLearningFirst : characteristic) {
            for (int i = 0; i < lossFunctions.length; i++) {
                for (int k = 0; k < l2vals.length; k++) {
                    LossFunction lf = lossFunctions[i];
                    String outputActivation = outputActivations[i];
                    double l2 = l2vals[k];
                    double l1 = l1vals[k];
                    Nd4j.getRandom().setSeed(12345);
                    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(true).learningRate(1.0).l2(l2).l1(l1).optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).seed(12345L).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.SGD).list().layer(0, new AutoEncoder.Builder().nIn(4).nOut(3).activation(afn).build()).layer(1, new OutputLayer.Builder(lf).nIn(3).nOut(3).activation(outputActivation).build()).pretrain(true).backprop(true).build();
                    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
                    mln.init();
                    if (doLearningFirst) {
                        //Run a number of iterations of learning
                        mln.setInput(ds.getFeatures());
                        mln.setLabels(ds.getLabels());
                        mln.computeGradientAndScore();
                        double scoreBefore = mln.score();
                        for (int j = 0; j < 10; j++) mln.fit(ds);
                        mln.computeGradientAndScore();
                        double scoreAfter = mln.score();
                        //Can't test in 'characteristic mode of operation' if not learning
                        String msg = "testGradMLP2LayerIrisSimple() - score did not (sufficiently) decrease during learning - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst + ", l2=" + l2 + ", l1=" + l1 + " (before=" + scoreBefore + ", scoreAfter=" + scoreAfter + ")";
                        assertTrue(msg, scoreAfter < scoreBefore);
                    }
                    if (PRINT_RESULTS) {
                        System.out.println("testGradientMLP2LayerIrisSimpleRandom() - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst + ", l2=" + l2 + ", l1=" + l1);
                        for (int j = 0; j < mln.getnLayers(); j++) System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
                    }
                    boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
                    String msg = "testGradMLP2LayerIrisSimple() - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst + ", l2=" + l2 + ", l1=" + l1;
                    assertTrue(msg, gradOK);
                }
            }
        }
    }
}