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

Example 6 with StatsListener

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

the class TestListeners method testStatsCollection.

@Test
public void testStatsCollection() {
    JavaSparkContext sc = getContext();
    int nExecutors = numExecutors();
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(123).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).list().layer(0, new DenseLayer.Builder().nIn(4).nOut(100).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(100).nOut(3).activation(Activation.SOFTMAX).weightInit(WeightInit.XAVIER).build()).pretrain(false).backprop(true).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).batchSizePerWorker(5).averagingFrequency(6).build();
    SparkDl4jMultiLayer net = new SparkDl4jMultiLayer(sc, conf, tm);
    //In-memory
    StatsStorage ss = new MapDBStatsStorage();
    net.setListeners(ss, Collections.singletonList(new StatsListener(null)));
    List<DataSet> list = new IrisDataSetIterator(120, 150).next().asList();
    //120 examples, 4 executors, 30 examples per executor -> 6 updates of size 5 per executor
    JavaRDD<DataSet> rdd = sc.parallelize(list);
    net.fit(rdd);
    List<String> sessions = ss.listSessionIDs();
    System.out.println("Sessions: " + sessions);
    assertEquals(1, sessions.size());
    String sid = sessions.get(0);
    List<String> typeIDs = ss.listTypeIDsForSession(sid);
    List<String> workers = ss.listWorkerIDsForSession(sid);
    System.out.println(sid + "\t" + typeIDs + "\t" + workers);
    List<Persistable> lastUpdates = ss.getLatestUpdateAllWorkers(sid, StatsListener.TYPE_ID);
    System.out.println(lastUpdates);
    System.out.println("Static info:");
    for (String wid : workers) {
        Persistable staticInfo = ss.getStaticInfo(sid, StatsListener.TYPE_ID, wid);
        System.out.println(sid + "\t" + wid);
    }
    assertEquals(1, typeIDs.size());
    assertEquals(numExecutors(), workers.size());
    String firstWorker = workers.get(0);
    String firstWorkerSubstring = workers.get(0).substring(0, firstWorker.length() - 1);
    for (String wid : workers) {
        String widSubstring = wid.substring(0, wid.length() - 1);
        assertEquals(firstWorkerSubstring, widSubstring);
        String counterVal = wid.substring(wid.length() - 1, wid.length());
        int cv = Integer.parseInt(counterVal);
        assertTrue(0 <= cv && cv < numExecutors());
    }
}

Example 7 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);
}

Example 8 with StatsListener

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

the class TestPlayUI method testUI_VAE.

@Test
@Ignore
public void testUI_VAE() throws Exception {
    //Variational autoencoder - 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 VariationalAutoencoder.Builder().nIn(4).nOut(3).encoderLayerSizes(10, 11).decoderLayerSizes(12, 13).weightInit(WeightInit.XAVIER).pzxActivationFunction("identity").reconstructionDistribution(new GaussianReconstructionDistribution()).activation(Activation.LEAKYRELU).updater(Updater.SGD).build()).layer(1, new VariationalAutoencoder.Builder().nIn(3).nOut(3).encoderLayerSizes(7).decoderLayerSizes(8).weightInit(WeightInit.XAVIER).pzxActivationFunction("identity").reconstructionDistribution(new GaussianReconstructionDistribution()).activation(Activation.LEAKYRELU).updater(Updater.SGD).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);
}