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

Example 61 with IrisDataSetIterator

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

the class GradientCheckTestsComputationGraph method testBasicIrisWithMerging.

@Test
public void testBasicIrisWithMerging() {
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).learningRate(1.0).graphBuilder().addInputs("input").addLayer("l1", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), "input").addLayer("l2", new DenseLayer.Builder().nIn(4).nOut(5).activation(Activation.TANH).build(), "input").addVertex("merge", new MergeVertex(), "l1", "l2").addLayer("outputLayer", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(5 + 5).nOut(3).build(), "merge").setOutputs("outputLayer").pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    int numParams = (4 * 5 + 5) + (4 * 5 + 5) + (10 * 3 + 3);
    assertEquals(numParams, graph.numParams());
    Nd4j.getRandom().setSeed(12345);
    int nParams = graph.numParams();
    INDArray newParams = Nd4j.rand(1, nParams);
    graph.setParams(newParams);
    DataSet ds = new IrisDataSetIterator(150, 150).next();
    INDArray min = ds.getFeatureMatrix().min(0);
    INDArray max = ds.getFeatureMatrix().max(0);
    ds.getFeatureMatrix().subiRowVector(min).diviRowVector(max.sub(min));
    INDArray input = ds.getFeatureMatrix();
    INDArray labels = ds.getLabels();
    if (PRINT_RESULTS) {
        System.out.println("testBasicIrisWithMerging()");
        for (int j = 0; j < graph.getNumLayers(); j++) System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
    }
    boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] { input }, new INDArray[] { labels });
    String msg = "testBasicIrisWithMerging()";
    assertTrue(msg, gradOK);
}

Example 62 with IrisDataSetIterator

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

the class GradientCheckTests method testGradientMLP2LayerIrisSimple.

@Test
public void testGradientMLP2LayerIrisSimple() {
    //Parameterized test, testing combinations of:
    // (a) activation function
    // (b) Whether to test at random initialization, or after some learning (i.e., 'characteristic mode of operation')
    // (c) Loss function (with specified output activations)
    //activation functions such as relu and hardtanh: may randomly fail due to discontinuities
    String[] activFns = { "sigmoid", "tanh", "softplus" };
    //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();
    for (String afn : activFns) {
        for (boolean doLearningFirst : characteristic) {
            for (int i = 0; i < lossFunctions.length; i++) {
                LossFunction lf = lossFunctions[i];
                String outputActivation = outputActivations[i];
                MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(false).optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).learningRate(1.0).seed(12345L).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).activation(afn).updater(Updater.SGD).build()).layer(1, new OutputLayer.Builder(lf).activation(outputActivation).nIn(3).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.SGD).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=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst + " (before=" + scoreBefore + ", scoreAfter=" + scoreAfter + ")";
                    assertTrue(msg, scoreAfter < 0.8 * scoreBefore);
                }
                if (PRINT_RESULTS) {
                    System.out.println("testGradientMLP2LayerIrisSimpleRandom() - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst);
                    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;
                assertTrue(msg, gradOK);
            }
        }
    }
}

Example 63 with IrisDataSetIterator

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

the class GradientCheckTests method testGradientMLP2LayerIrisL1L2Simple.

@Test
public void testGradientMLP2LayerIrisL1L2Simple() {
    //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();
    //use l2vals[i] with l1vals[i]
    double[] l2vals = { 0.4, 0.0, 0.4, 0.4 };
    double[] l1vals = { 0.0, 0.0, 0.5, 0.0 };
    double[] biasL2 = { 0.0, 0.0, 0.0, 0.2 };
    double[] biasL1 = { 0.0, 0.0, 0.6, 0.0 };
    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];
                    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().regularization(true).l2(l2).l1(l1).l2Bias(biasL2[k]).l1Bias(biasL1[k]).optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).seed(12345L).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).activation(afn).build()).layer(1, new OutputLayer.Builder(lf).nIn(3).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).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=" + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", doLearningFirst=" + doLearningFirst + ", l2=" + l2 + ", l1=" + l1 + " (before=" + scoreBefore + ", scoreAfter=" + scoreAfter + ")";
                        assertTrue(msg, scoreAfter < 0.8 * 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);
                }
            }
        }
    }
}

Example 64 with IrisDataSetIterator

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

the class OutputLayerTest method testWeightsDifferent.

@Test
public void testWeightsDifferent() {
    Nd4j.MAX_ELEMENTS_PER_SLICE = Integer.MAX_VALUE;
    Nd4j.MAX_SLICES_TO_PRINT = Integer.MAX_VALUE;
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).miniBatch(false).seed(123).iterations(1000).learningRate(1e-1).layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER).updater(Updater.ADAGRAD).lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).activation(Activation.SOFTMAX).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    OutputLayer o = (OutputLayer) conf.getLayer().instantiate(conf, null, 0, params, true);
    o.setBackpropGradientsViewArray(Nd4j.create(1, params.length()));
    int numSamples = 150;
    int batchSize = 150;
    DataSetIterator iter = new IrisDataSetIterator(batchSize, numSamples);
    // Loads data into generator and format consumable for NN
    DataSet iris = iter.next();
    iris.normalizeZeroMeanZeroUnitVariance();
    o.setListeners(new ScoreIterationListener(1));
    SplitTestAndTrain t = iris.splitTestAndTrain(0.8);
    o.fit(t.getTrain());
    log.info("Evaluate model....");
    Evaluation eval = new Evaluation(3);
    eval.eval(t.getTest().getLabels(), o.output(t.getTest().getFeatureMatrix(), true));
    log.info(eval.stats());
}

Example 65 with IrisDataSetIterator

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

the class OutputLayerTest method testIris2.

@Test
public void testIris2() {
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(10).learningRate(1e-1).layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).lossFunction(LossFunctions.LossFunction.MCXENT).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    OutputLayer l = (OutputLayer) conf.getLayer().instantiate(conf, Collections.<IterationListener>singletonList(new ScoreIterationListener(1)), 0, params, true);
    l.setBackpropGradientsViewArray(Nd4j.create(1, params.length()));
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    DataSet next = iter.next();
    next.shuffle();
    SplitTestAndTrain trainTest = next.splitTestAndTrain(110);
    trainTest.getTrain().normalizeZeroMeanZeroUnitVariance();
    l.fit(trainTest.getTrain());
    DataSet test = trainTest.getTest();
    test.normalizeZeroMeanZeroUnitVariance();
    Evaluation eval = new Evaluation();
    INDArray output = l.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    log.info("Score " + eval.stats());
}