Examples with DataSetIterator org.nd4j.linalg.dataset.api.iterator.DataSetIterator used on opensource projects

Example 36 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class TestComputationGraphNetwork method testScoringDataSet.

@Test
public void testScoringDataSet() {
    ComputationGraphConfiguration configuration = getIrisGraphConfiguration();
    ComputationGraph graph = new ComputationGraph(configuration);
    graph.init();
    MultiLayerConfiguration mlc = getIrisMLNConfiguration();
    MultiLayerNetwork net = new MultiLayerNetwork(mlc);
    net.init();
    DataSetIterator iris = new IrisDataSetIterator(150, 150);
    DataSet ds = iris.next();
    //Now: set parameters of both networks to be identical. Then feedforward, and check we get the same score
    Nd4j.getRandom().setSeed(12345);
    int nParams = getNumParams();
    INDArray params = Nd4j.rand(1, nParams);
    graph.setParams(params.dup());
    net.setParams(params.dup());
    double scoreMLN = net.score(ds, false);
    double scoreCG = graph.score(ds, false);
    assertEquals(scoreMLN, scoreCG, 1e-4);
}

Example 37 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class TestComputationGraphNetwork method testCompGraphUnderscores.

@Test
public void testCompGraphUnderscores() {
    //Problem: underscores in names could be problematic for ComputationGraphUpdater, HistogramIterationListener
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder().addInputs("input").addLayer("first_layer", new DenseLayer.Builder().nIn(4).nOut(5).build(), "input").addLayer("output_layer", new OutputLayer.Builder().nIn(5).nOut(3).build(), "first_layer").setOutputs("output_layer").pretrain(false).backprop(true).build();
    ComputationGraph net = new ComputationGraph(conf);
    net.init();
    DataSetIterator iris = new IrisDataSetIterator(10, 150);
    while (iris.hasNext()) {
        net.fit(iris.next());
    }
}

Example 38 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class ComputationGraphTestRNN method checkMaskArrayClearance.

@Test
public void checkMaskArrayClearance() {
    for (boolean tbptt : new boolean[] { true, false }) {
        //Simple "does it throw an exception" type test...
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().iterations(1).seed(12345).graphBuilder().addInputs("in").addLayer("out", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY).nIn(1).nOut(1).build(), "in").setOutputs("out").backpropType(tbptt ? BackpropType.TruncatedBPTT : BackpropType.Standard).tBPTTForwardLength(8).tBPTTBackwardLength(8).build();
        ComputationGraph net = new ComputationGraph(conf);
        net.init();
        MultiDataSet data = new MultiDataSet(new INDArray[] { Nd4j.linspace(1, 10, 10).reshape(1, 1, 10) }, new INDArray[] { Nd4j.linspace(2, 20, 10).reshape(1, 1, 10) }, new INDArray[] { Nd4j.ones(10) }, new INDArray[] { Nd4j.ones(10) });
        net.fit(data);
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
        DataSet ds = new DataSet(data.getFeatures(0), data.getLabels(0), data.getFeaturesMaskArray(0), data.getLabelsMaskArray(0));
        net.fit(ds);
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
        net.fit(data.getFeatures(), data.getLabels(), data.getFeaturesMaskArrays(), data.getLabelsMaskArrays());
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
        MultiDataSetIterator iter = new IteratorMultiDataSetIterator(Collections.singletonList((org.nd4j.linalg.dataset.api.MultiDataSet) data).iterator(), 1);
        net.fit(iter);
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
        DataSetIterator iter2 = new IteratorDataSetIterator(Collections.singletonList(ds).iterator(), 1);
        net.fit(iter2);
        assertNull(net.getInputMaskArrays());
        assertNull(net.getLabelMaskArrays());
        for (Layer l : net.getLayers()) {
            assertNull(l.getMaskArray());
        }
    }
}

Example 39 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class GradientCheckTests method testRbm.

@Test
public void testRbm() {
    //As above (testGradientMLP2LayerIrisSimple()) but with L2, L1, and both L2/L1 applied
    //Need to run gradient through updater, so that L2 can be applied
    RBM.HiddenUnit[] hiddenFunc = { RBM.HiddenUnit.BINARY, RBM.HiddenUnit.RECTIFIED };
    //If true: run some backprop steps first
    boolean[] characteristic = { false, true };
    LossFunction[] lossFunctions = { LossFunction.MSE, LossFunction.KL_DIVERGENCE };
    //i.e., lossFunctions[i] used with outputActivations[i] here
    String[] outputActivations = { "softmax", "sigmoid" };
    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();
    double[] l2vals = { 0.4, 0.0, 0.4 };
    //i.e., use l2vals[i] with l1vals[i]
    double[] l1vals = { 0.0, 0.5, 0.5 };
    for (RBM.HiddenUnit hidunit : hiddenFunc) {
        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];
                    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(true).l2(l2).l1(l1).learningRate(1.0).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).seed(12345L).list().layer(0, new RBM.Builder(hidunit, RBM.VisibleUnit.BINARY).nIn(4).nOut(3).weightInit(WeightInit.UNIFORM).updater(Updater.SGD).build()).layer(1, new OutputLayer.Builder(lf).nIn(3).nOut(3).weightInit(WeightInit.XAVIER).updater(Updater.SGD).activation(outputActivation).build()).pretrain(false).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=" + hidunit.toString() + ", 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=" + hidunit.toString() + ", 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=" + hidunit.toString() + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst + ", l2=" + l2 + ", l1=" + l1;
                    assertTrue(msg, gradOK);
                }
            }
        }
    }
}

Example 40 with DataSetIterator

use of org.nd4j.linalg.dataset.api.iterator.DataSetIterator in project deeplearning4j by deeplearning4j.

the class CenterLossOutputLayerTest method testMNISTConfig.

@Test
//Should be run manually
@Ignore
public void testMNISTConfig() throws Exception {
    // Test batch size
    int batchSize = 64;
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);
    ComputationGraph net = getCNNMnistConfig();
    net.init();
    net.setListeners(new ScoreIterationListener(1));
    for (int i = 0; i < 50; i++) {
        net.fit(mnistTrain.next());
        Thread.sleep(1000);
    }
    Thread.sleep(100000);
}