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

Example 21 with Evaluation

use of org.deeplearning4j.eval.Evaluation 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 22 with Evaluation

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

the class DenseTest method testMLPMultiLayerBackprop.

@Test
public void testMLPMultiLayerBackprop() {
    MultiLayerNetwork model = getDenseMLNConfig(true, false);
    model.fit(iter);
    MultiLayerNetwork model2 = getDenseMLNConfig(true, false);
    model2.fit(iter);
    iter.reset();
    DataSet test = iter.next();
    assertEquals(model.params(), model2.params());
    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 23 with Evaluation

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

Example 24 with Evaluation

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

the class OutputLayerTest method testIris.

@Test
public void testIris() {
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT).iterations(5).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());
}

Example 25 with Evaluation

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

the class TestComputationGraphNetwork method testCGEvaluation.

@Test
public void testCGEvaluation() {
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration configuration = getIrisGraphConfiguration();
    ComputationGraph graph = new ComputationGraph(configuration);
    graph.init();
    Nd4j.getRandom().setSeed(12345);
    MultiLayerConfiguration mlnConfig = getIrisMLNConfiguration();
    MultiLayerNetwork net = new MultiLayerNetwork(mlnConfig);
    net.init();
    DataSetIterator iris = new IrisDataSetIterator(75, 150);
    net.fit(iris);
    iris.reset();
    graph.fit(iris);
    iris.reset();
    Evaluation evalExpected = net.evaluate(iris);
    iris.reset();
    Evaluation evalActual = graph.evaluate(iris);
    assertEquals(evalExpected.accuracy(), evalActual.accuracy(), 0e-4);
}