Examples with Evaluation org.deeplearning4j.eval.Evaluation used on opensource projects

Example 26 with Evaluation

use of org.deeplearning4j.eval.Evaluation in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerTest method testCNNMLNBackprop.

@Test
public void testCNNMLNBackprop() throws Exception {
    int numSamples = 10;
    int batchSize = 10;
    DataSetIterator mnistIter = new MnistDataSetIterator(batchSize, numSamples, true);
    MultiLayerNetwork model = getCNNMLNConfig(true, false);
    model.fit(mnistIter);
    MultiLayerNetwork model2 = getCNNMLNConfig(true, false);
    model2.fit(mnistIter);
    DataSet test = mnistIter.next();
    Evaluation eval = new Evaluation();
    INDArray output = model.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    double f1Score = eval.f1();
    Evaluation eval2 = new Evaluation();
    INDArray output2 = model2.output(test.getFeatureMatrix());
    eval2.eval(test.getLabels(), output2);
    double f1Score2 = eval2.f1();
    assertEquals(f1Score, f1Score2, 1e-4);
}

Example 27 with Evaluation

use of org.deeplearning4j.eval.Evaluation in project deeplearning4j by deeplearning4j.

the class GravesLSTMOutputTest method testSameLabelsOutput.

@Test
public void testSameLabelsOutput() {
    MultiLayerNetwork network = new MultiLayerNetwork(getNetworkConf(40, false));
    network.init();
    network.setListeners(new ScoreIterationListener(1));
    network.fit(reshapeInput(data.dup()), data.dup());
    Evaluation ev = eval(network);
    Assert.assertTrue(ev.f1() > 0.90);
}

Example 28 with Evaluation

use of org.deeplearning4j.eval.Evaluation in project deeplearning4j by deeplearning4j.

the class RBMTests method testBackprop.

@Test
public void testBackprop() throws Exception {
    int numSamples = 10;
    int batchSize = 10;
    DataSetIterator mnistIter = new MnistDataSetIterator(batchSize, numSamples, true);
    DataSet input = mnistIter.next();
    INDArray features = input.getFeatures();
    mnistIter.reset();
    MultiLayerNetwork rbm = getRBMMLNNet(true, true, features, 50, 10, WeightInit.UNIFORM);
    rbm.fit(mnistIter);
    MultiLayerNetwork rbm2 = getRBMMLNNet(true, true, features, 50, 10, WeightInit.UNIFORM);
    rbm2.fit(mnistIter);
    DataSet test = mnistIter.next();
    Evaluation eval = new Evaluation();
    INDArray output = rbm.output(test.getFeatureMatrix());
    eval.eval(test.getLabels(), output);
    double f1Score = eval.f1();
    Evaluation eval2 = new Evaluation();
    INDArray output2 = rbm2.output(test.getFeatureMatrix());
    eval2.eval(test.getLabels(), output2);
    double f1Score2 = eval2.f1();
    assertEquals(f1Score, f1Score2, 1e-4);
}

Example 29 with Evaluation

use of org.deeplearning4j.eval.Evaluation in project deeplearning4j by deeplearning4j.

the class TestSparkMultiLayerParameterAveraging method testEvaluation.

@Test
public void testEvaluation() {
    SparkDl4jMultiLayer sparkNet = getBasicNetwork();
    MultiLayerNetwork netCopy = sparkNet.getNetwork().clone();
    Evaluation evalExpected = new Evaluation();
    INDArray outLocal = netCopy.output(input, Layer.TrainingMode.TEST);
    evalExpected.eval(labels, outLocal);
    Evaluation evalActual = sparkNet.evaluate(sparkData);
    assertEquals(evalExpected.accuracy(), evalActual.accuracy(), 1e-3);
    assertEquals(evalExpected.f1(), evalActual.f1(), 1e-3);
    assertEquals(evalExpected.getNumRowCounter(), evalActual.getNumRowCounter(), 1e-3);
    assertMapEquals(evalExpected.falseNegatives(), evalActual.falseNegatives());
    assertMapEquals(evalExpected.falsePositives(), evalActual.falsePositives());
    assertMapEquals(evalExpected.trueNegatives(), evalActual.trueNegatives());
    assertMapEquals(evalExpected.truePositives(), evalActual.truePositives());
    assertEquals(evalExpected.precision(), evalActual.precision(), 1e-3);
    assertEquals(evalExpected.recall(), evalActual.recall(), 1e-3);
    assertEquals(evalExpected.getConfusionMatrix(), evalActual.getConfusionMatrix());
}

Example 30 with Evaluation

use of org.deeplearning4j.eval.Evaluation 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());
}