Examples with ScoreIterationListener org.deeplearning4j.optimize.listeners.ScoreIterationListener used on opensource projects

Example 61 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener 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 62 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener 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 63 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener 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 64 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener in project deeplearning4j by deeplearning4j.

the class MultiLayerTest method testBatchNorm.

@Test
public void testBatchNorm() {
    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 BatchNormalization.Builder().nOut(2).build()).layer(3, 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.setLabels(trainTest.getTrain().getLabels());
    network.init();
    network.fit(trainTest.getTrain());
}

Example 65 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener in project deeplearning4j by deeplearning4j.

the class RBMTests method testRBMMLN.

// Use to verify the model decreases in value
@Ignore
@Test
public void testRBMMLN() {
    INDArray features = Nd4j.rand(new int[] { 100, 10 });
    System.out.println("Training RBM network, initialized with Xavier");
    MultiLayerNetwork rbm = getRBMMLNNet(true, true, features, 10, 10, WeightInit.XAVIER);
    rbm.setListeners(new ScoreIterationListener(1));
    rbm.fit(features);
    System.out.println("Training RBM network, initialized with correct solution");
    MultiLayerNetwork rbm2 = getRBMMLNNet(true, true, features, 10, 10, WeightInit.UNIFORM);
    rbm.setListeners(new ScoreIterationListener(1));
    rbm2.setParam("0_W", Nd4j.diag(Nd4j.onesLike(Nd4j.diag(rbm2.getParam("0_W")))));
    rbm2.setParam("1_W", Nd4j.diag(Nd4j.onesLike(Nd4j.diag(rbm2.getParam("1_W")))));
    rbm2.fit(features, features);
}