org.deeplearning4j.optimize.listeners.ScoreIterationListener Java Examples

The following examples show how to use org.deeplearning4j.optimize.listeners.ScoreIterationListener. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.

Example #1

Source File: TestVertxUI.java From deeplearning4j with Apache License 2.0

6 votes

@Test
public void testUICompGraph() {

    StatsStorage ss = new InMemoryStatsStorage();

    UIServer uiServer = UIServer.getInstance();
    uiServer.attach(ss);

    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
                    .addLayer("L0", new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build(),
                                    "in")
                    .addLayer("L1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
                                    .activation(Activation.SOFTMAX).nIn(4).nOut(3).build(), "L0")
                    .setOutputs("L1").build();

    ComputationGraph net = new ComputationGraph(conf);
    net.init();

    net.setListeners(new StatsListener(ss), new ScoreIterationListener(1));

    DataSetIterator iter = new IrisDataSetIterator(150, 150);

    for (int i = 0; i < 100; i++) {
        net.fit(iter);
    }
}

Example #2

Source File: MultiLayerNeuralNetConfigurationTest.java From deeplearning4j with Apache License 2.0

6 votes

@Test
public void testTrainingListener() {
    MultiLayerNetwork model1 = new MultiLayerNetwork(getConf());
    model1.init();
    model1.addListeners( new ScoreIterationListener(1));

    MultiLayerNetwork model2 = new MultiLayerNetwork(getConf());
    model2.addListeners( new ScoreIterationListener(1));
    model2.init();

    Layer[] l1 = model1.getLayers();
    for (int i = 0; i < l1.length; i++)
        assertTrue(l1[i].getListeners() != null && l1[i].getListeners().size() == 1);

    Layer[] l2 = model2.getLayers();
    for (int i = 0; i < l2.length; i++)
        assertTrue(l2[i].getListeners() != null && l2[i].getListeners().size() == 1);
}

Example #3

Source File: TestRemoteReceiver.java From deeplearning4j with Apache License 2.0

6 votes

@Test
@Ignore
public void testRemoteFull() throws Exception {
    //Use this in conjunction with startRemoteUI()

    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).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())
                    .build();

    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    try(RemoteUIStatsStorageRouter ssr = new RemoteUIStatsStorageRouter("http://localhost:9000")) {
        net.setListeners(new StatsListener(ssr), new ScoreIterationListener(1));

        DataSetIterator iter = new IrisDataSetIterator(150, 150);

        for (int i = 0; i < 500; i++) {
            net.fit(iter);
            //            Thread.sleep(100);
            Thread.sleep(100);
        }
    }
}

Example #4

Source File: CenterLossOutputLayerTest.java From deeplearning4j with Apache License 2.0

6 votes

@Test
@Ignore //Should be run manually
public void testMNISTConfig() throws Exception {
    int batchSize = 64; // Test batch size
    DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, 12345);

    ComputationGraph net = getCNNMnistConfig();
    net.init();
    net.setListeners(new ScoreIterationListener(1));

    for (int i = 0; i < 50; i++) {
        net.fit(mnistTrain.next());
        Thread.sleep(1000);
    }

    Thread.sleep(100000);
}

Example #5

Source File: OutputLayerTest.java From deeplearning4j with Apache License 2.0

6 votes

@Test
public void testSetParams() {
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder()
                    .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
                    .updater(new Sgd(1e-1))
                    .layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder().nIn(4).nOut(3)
                                    .weightInit(WeightInit.ZERO).activation(Activation.SOFTMAX)
                                    .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                    .build();

    long numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    OutputLayer l = (OutputLayer) conf.getLayer().instantiate(conf,
                    Collections.<TrainingListener>singletonList(new ScoreIterationListener(1)), 0, params, true, params.dataType());
    params = l.params();
    l.setParams(params);
    assertEquals(params, l.params());
}

Example #6

Source File: MultiRegression.java From dl4j-tutorials with MIT License

5 votes

public static void main(String[] args){

        //Generate the training data
        DataSetIterator iterator = getTrainingData(batchSize,rng);

        //Create the network
        int numInput = 2;
        int numOutputs = 1;
        MultiLayerNetwork net = new MultiLayerNetwork(new NeuralNetConfiguration.Builder()
                .seed(seed)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .weightInit(WeightInit.XAVIER)
                .updater(new Sgd(learningRate))
                .list()
                .layer(0, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
                        .activation(Activation.IDENTITY)
                        .nIn(numInput).nOut(numOutputs).build())
                .pretrain(false).backprop(true).build()
        );
        net.init();
        net.setListeners(new ScoreIterationListener(1));

        for( int i=0; i<nEpochs; i++ ){
            iterator.reset();
            net.fit(iterator);
        }

        final INDArray input = Nd4j.create(new double[] { 0.111111, 0.3333333333333 }, new int[] { 1, 2 });
        INDArray out = net.output(input, false);
        System.out.println(out);

    }

Example #7

Source File: TestEarlyStoppingCompGraph.java From deeplearning4j with Apache License 2.0

5 votes

@Test
public void testBadTuning() {
    //Test poor tuning (high LR): should terminate on MaxScoreIterationTerminationCondition

    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
                    .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                    .updater(new Sgd(5.0)) //Intentionally huge LR
                    .weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in")
                    .addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).activation(Activation.SOFTMAX)
                                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                    .setOutputs("0").build();
    ComputationGraph net = new ComputationGraph(conf);
    net.setListeners(new ScoreIterationListener(1));

    DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
    EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
    EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>()
                    .epochTerminationConditions(new MaxEpochsTerminationCondition(5000))
                    .iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES),
                                    new MaxScoreIterationTerminationCondition(10)) //Initial score is ~2.5
                    .scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();

    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter);
    EarlyStoppingResult result = trainer.fit();

    assertTrue(result.getTotalEpochs() < 5);
    assertEquals(EarlyStoppingResult.TerminationReason.IterationTerminationCondition,
                    result.getTerminationReason());
    String expDetails = new MaxScoreIterationTerminationCondition(10).toString();
    assertEquals(expDetails, result.getTerminationDetails());

    assertEquals(0, result.getBestModelEpoch());
    assertNotNull(result.getBestModel());
}

Example #8