Examples with StatsListener org.deeplearning4j.ui.stats.StatsListener used on opensource projects

Example 1 with StatsListener

use of org.deeplearning4j.ui.stats.StatsListener in project deeplearning4j by deeplearning4j.

the class ParallelWrapperMain method runMain.

public void runMain(String... args) throws Exception {
    JCommander jcmdr = new JCommander(this);
    try {
        jcmdr.parse(args);
    } catch (ParameterException e) {
        System.err.println(e.getMessage());
        //User provides invalid input -> print the usage info
        jcmdr.usage();
        try {
            Thread.sleep(500);
        } catch (Exception e2) {
        }
        System.exit(1);
    }
    Model model = ModelGuesser.loadModelGuess(modelPath);
    // ParallelWrapper will take care of load balancing between GPUs.
    ParallelWrapper wrapper = new ParallelWrapper.Builder(model).prefetchBuffer(prefetchSize).workers(workers).averagingFrequency(averagingFrequency).averageUpdaters(averageUpdaters).reportScoreAfterAveraging(reportScore).useLegacyAveraging(legacyAveraging).build();
    if (dataSetIteratorFactoryClazz != null) {
        DataSetIteratorProviderFactory dataSetIteratorProviderFactory = (DataSetIteratorProviderFactory) Class.forName(dataSetIteratorFactoryClazz).newInstance();
        DataSetIterator dataSetIterator = dataSetIteratorProviderFactory.create();
        if (uiUrl != null) {
            // it's important that the UI can report results from parallel training
            // there's potential for StatsListener to fail if certain properties aren't set in the model
            StatsStorageRouter remoteUIRouter = new RemoteUIStatsStorageRouter("http://" + uiUrl);
            wrapper.setListeners(remoteUIRouter, new StatsListener(null));
        }
        wrapper.fit(dataSetIterator);
        ModelSerializer.writeModel(model, new File(modelOutputPath), true);
    } else if (multiDataSetIteratorFactoryClazz != null) {
        MultiDataSetProviderFactory multiDataSetProviderFactory = (MultiDataSetProviderFactory) Class.forName(multiDataSetIteratorFactoryClazz).newInstance();
        MultiDataSetIterator iterator = multiDataSetProviderFactory.create();
        if (uiUrl != null) {
            // it's important that the UI can report results from parallel training
            // there's potential for StatsListener to fail if certain properties aren't set in the model
            StatsStorageRouter remoteUIRouter = new RemoteUIStatsStorageRouter("http://" + uiUrl);
            wrapper.setListeners(remoteUIRouter, new StatsListener(null));
        }
        wrapper.fit(iterator);
        ModelSerializer.writeModel(model, new File(modelOutputPath), true);
    } else {
        throw new IllegalStateException("Please provide a datasetiteraator or multi datasetiterator class");
    }
}

Example 2 with StatsListener

use of org.deeplearning4j.ui.stats.StatsListener 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);
}

Example 3 with StatsListener

use of org.deeplearning4j.ui.stats.StatsListener in project deeplearning4j by deeplearning4j.

the class TestPlayUI method testUI_RBM.

@Test
@Ignore
public void testUI_RBM() throws Exception {
    //RBM - for unsupervised layerwise pretraining
    StatsStorage ss = new InMemoryStatsStorage();
    UIServer uiServer = UIServer.getInstance();
    uiServer.attach(ss);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).learningRate(1e-5).list().layer(0, new RBM.Builder().nIn(4).nOut(3).build()).layer(1, new RBM.Builder().nIn(3).nOut(3).build()).layer(2, new OutputLayer.Builder().nIn(3).nOut(3).build()).pretrain(true).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 < 50; i++) {
        net.fit(iter);
        Thread.sleep(100);
    }
    Thread.sleep(100000);
}

Example 4 with StatsListener

use of org.deeplearning4j.ui.stats.StatsListener in project deeplearning4j by deeplearning4j.

the class TestRemoteReceiver method testRemoteFull.

@Test
@Ignore
public void testRemoteFull() throws Exception {
    //Use this in conjunction with startRemoteUI()
    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();
    StatsStorageRouter 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 5 with StatsListener

use of org.deeplearning4j.ui.stats.StatsListener in project deeplearning4j by deeplearning4j.

the class TestPlayUI method testUICompGraph.

@Test
@Ignore
public void testUICompGraph() throws Exception {
    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").pretrain(false).backprop(true).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);
        Thread.sleep(100);
    }
    Thread.sleep(100000);
}