Examples with SplitTestAndTrain org.nd4j.linalg.dataset.SplitTestAndTrain used on opensource projects

Example 1 with SplitTestAndTrain

use of org.nd4j.linalg.dataset.SplitTestAndTrain in project deeplearning4j by deeplearning4j.

the class MultiLayerTest method testDbn.

@Test
public void testDbn() throws Exception {
    Nd4j.MAX_SLICES_TO_PRINT = -1;
    Nd4j.MAX_ELEMENTS_PER_SLICE = -1;
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().iterations(100).momentum(0.9).optimizationAlgo(OptimizationAlgorithm.LBFGS).regularization(true).l2(2e-4).list().layer(0, new RBM.Builder(RBM.HiddenUnit.GAUSSIAN, RBM.VisibleUnit.GAUSSIAN).nIn(4).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(0, 1)).activation(Activation.TANH).lossFunction(LossFunctions.LossFunction.KL_DIVERGENCE).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(3).nOut(3).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(0, 1)).activation(Activation.SOFTMAX).build()).build();
    MultiLayerNetwork d = new MultiLayerNetwork(conf);
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    DataSet next = iter.next();
    Nd4j.writeTxt(next.getFeatureMatrix(), "iris.txt", "\t");
    next.normalizeZeroMeanZeroUnitVariance();
    SplitTestAndTrain testAndTrain = next.splitTestAndTrain(110);
    DataSet train = testAndTrain.getTrain();
    d.fit(train);
    DataSet test = testAndTrain.getTest();
    Evaluation eval = new Evaluation();
    INDArray output = d.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    log.info("Score " + eval.stats());
}

Example 2 with SplitTestAndTrain

use of org.nd4j.linalg.dataset.SplitTestAndTrain in project deeplearning4j by deeplearning4j.

the class MultiLayerTest method testBackProp.

@Test
public void testBackProp() {
    Nd4j.getRandom().setSeed(123);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).iterations(5).seed(123).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER).activation(Activation.TANH).build()).layer(1, new DenseLayer.Builder().nIn(3).nOut(2).weightInit(WeightInit.XAVIER).activation(Activation.TANH).build()).layer(2, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(2).nOut(3).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.setListeners(new ScoreIterationListener(1));
    DataSetIterator iter = new IrisDataSetIterator(150, 150);
    DataSet next = iter.next();
    next.normalizeZeroMeanZeroUnitVariance();
    SplitTestAndTrain trainTest = next.splitTestAndTrain(110);
    network.setInput(trainTest.getTrain().getFeatureMatrix());
    network.setLabels(trainTest.getTrain().getLabels());
    network.init();
    network.fit(trainTest.getTrain());
    DataSet test = trainTest.getTest();
    Evaluation eval = new Evaluation();
    INDArray output = network.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    log.info("Score " + eval.stats());
}

Example 3 with SplitTestAndTrain

use of org.nd4j.linalg.dataset.SplitTestAndTrain 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 4 with SplitTestAndTrain

use of org.nd4j.linalg.dataset.SplitTestAndTrain 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 5 with SplitTestAndTrain

use of org.nd4j.linalg.dataset.SplitTestAndTrain 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());
}