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

Example 31 with ScoreIterationListener

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

the class BackPropMLPTest method testMLPTrivial.

@Test
public void testMLPTrivial() {
    //Simplest possible case: 1 hidden layer, 1 hidden neuron, batch size of 1.
    MultiLayerNetwork network = new MultiLayerNetwork(getIrisMLPSimpleConfig(new int[] { 1 }, Activation.SIGMOID));
    network.setListeners(new ScoreIterationListener(1));
    network.init();
    DataSetIterator iter = new IrisDataSetIterator(1, 10);
    while (iter.hasNext()) network.fit(iter.next());
}

Example 32 with ScoreIterationListener

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

the class GravesLSTMOutputTest method testSameLabelsOutputWithTBPTT.

@Test
public void testSameLabelsOutputWithTBPTT() {
    MultiLayerNetwork network = new MultiLayerNetwork(getNetworkConf(40, true));
    network.init();
    network.setListeners(new ScoreIterationListener(1));
    for (int i = 0; i < window / 100; i++) {
        INDArray d = data.get(NDArrayIndex.interval(100 * i, 100 * (i + 1)), NDArrayIndex.all());
        network.fit(reshapeInput(d.dup()), reshapeInput(d.dup()));
    }
    Evaluation ev = eval(network);
}

Example 33 with ScoreIterationListener

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

the class BackTrackLineSearchTest method testBackTrackLineLBFGS.

@Test
public void testBackTrackLineLBFGS() {
    OptimizationAlgorithm optimizer = OptimizationAlgorithm.LBFGS;
    DataSet data = irisIter.next();
    data.normalizeZeroMeanZeroUnitVariance();
    MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.RELU, 5, optimizer));
    network.init();
    IterationListener listener = new ScoreIterationListener(1);
    network.setListeners(Collections.singletonList(listener));
    double oldScore = network.score(data);
    network.fit(data.getFeatureMatrix(), data.getLabels());
    double score = network.score();
    assertTrue(score < oldScore);
}

Example 34 with ScoreIterationListener

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

the class BackTrackLineSearchTest method testBackTrackLineHessian.

@Test(expected = Exception.class)
public void testBackTrackLineHessian() {
    OptimizationAlgorithm optimizer = OptimizationAlgorithm.HESSIAN_FREE;
    DataSet data = irisIter.next();
    MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.RELU, 100, optimizer));
    network.init();
    IterationListener listener = new ScoreIterationListener(1);
    network.setListeners(Collections.singletonList(listener));
    network.fit(data.getFeatureMatrix(), data.getLabels());
}

Example 35 with ScoreIterationListener

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

the class TestPlayUI method testUIMultipleSessions.

@Test
@Ignore
public void testUIMultipleSessions() throws Exception {
    for (int session = 0; session < 3; session++) {
        StatsStorage ss = new InMemoryStatsStorage();
        UIServer uiServer = UIServer.getInstance();
        uiServer.attach(ss);
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).list().layer(0, new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build()).layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(4).nOut(3).build()).pretrain(false).backprop(true).build();
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        net.setListeners(new StatsListener(ss), new ScoreIterationListener(1));
        DataSetIterator iter = new IrisDataSetIterator(150, 150);
        for (int i = 0; i < 20; i++) {
            net.fit(iter);
            Thread.sleep(100);
        }
    }
    Thread.sleep(1000000);
}