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

Example 36 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener 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 37 with ScoreIterationListener

use of org.deeplearning4j.optimize.listeners.ScoreIterationListener 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 38 with ScoreIterationListener

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

the class TestSparkComputationGraph method testBasic.

@Test
public void testBasic() throws Exception {
    JavaSparkContext sc = this.sc;
    RecordReader rr = new CSVRecordReader(0, ",");
    rr.initialize(new FileSplit(new ClassPathResource("iris.txt").getTempFileFromArchive()));
    MultiDataSetIterator iter = new RecordReaderMultiDataSetIterator.Builder(1).addReader("iris", rr).addInput("iris", 0, 3).addOutputOneHot("iris", 4, 3).build();
    List<MultiDataSet> list = new ArrayList<>(150);
    while (iter.hasNext()) list.add(iter.next());
    ComputationGraphConfiguration config = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).learningRate(0.1).graphBuilder().addInputs("in").addLayer("dense", new DenseLayer.Builder().nIn(4).nOut(2).build(), "in").addLayer("out", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(3).build(), "dense").setOutputs("out").pretrain(false).backprop(true).build();
    ComputationGraph cg = new ComputationGraph(config);
    cg.init();
    TrainingMaster tm = new ParameterAveragingTrainingMaster(true, numExecutors(), 1, 10, 1, 0);
    SparkComputationGraph scg = new SparkComputationGraph(sc, cg, tm);
    scg.setListeners(Collections.singleton((IterationListener) new ScoreIterationListener(1)));
    JavaRDD<MultiDataSet> rdd = sc.parallelize(list);
    scg.fitMultiDataSet(rdd);
    //Try: fitting using DataSet
    DataSetIterator iris = new IrisDataSetIterator(1, 150);
    List<DataSet> list2 = new ArrayList<>();
    while (iris.hasNext()) list2.add(iris.next());
    JavaRDD<DataSet> rddDS = sc.parallelize(list2);
    scg.fit(rddDS);
}

Example 39 with ScoreIterationListener

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

the class TestSparkMultiLayerParameterAveraging method testIterationCounts.

@Test
public void testIterationCounts() throws Exception {
    int dataSetObjSize = 5;
    int batchSizePerExecutor = 25;
    List<DataSet> list = new ArrayList<>();
    int minibatchesPerWorkerPerEpoch = 10;
    DataSetIterator iter = new MnistDataSetIterator(dataSetObjSize, batchSizePerExecutor * numExecutors() * minibatchesPerWorkerPerEpoch, false);
    while (iter.hasNext()) {
        list.add(iter.next());
    }
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().updater(Updater.RMSPROP).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).list().layer(0, new org.deeplearning4j.nn.conf.layers.DenseLayer.Builder().nIn(28 * 28).nOut(50).activation(Activation.TANH).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(50).nOut(10).activation(Activation.SOFTMAX).build()).pretrain(false).backprop(true).build();
    for (int avgFreq : new int[] { 1, 5, 10 }) {
        System.out.println("--- Avg freq " + avgFreq + " ---");
        SparkDl4jMultiLayer sparkNet = new SparkDl4jMultiLayer(sc, conf.clone(), new ParameterAveragingTrainingMaster.Builder(numExecutors(), dataSetObjSize).batchSizePerWorker(batchSizePerExecutor).averagingFrequency(avgFreq).repartionData(Repartition.Always).build());
        sparkNet.setListeners(new ScoreIterationListener(1));
        JavaRDD<DataSet> rdd = sc.parallelize(list);
        assertEquals(0, sparkNet.getNetwork().getLayerWiseConfigurations().getIterationCount());
        sparkNet.fit(rdd);
        assertEquals(minibatchesPerWorkerPerEpoch, sparkNet.getNetwork().getLayerWiseConfigurations().getIterationCount());
        sparkNet.fit(rdd);
        assertEquals(2 * minibatchesPerWorkerPerEpoch, sparkNet.getNetwork().getLayerWiseConfigurations().getIterationCount());
        sparkNet.getTrainingMaster().deleteTempFiles(sc);
    }
}

Example 40 with ScoreIterationListener

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

the class TestRenders method testHistogramComputationGraph.

@Test
public void testHistogramComputationGraph() throws Exception {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).graphBuilder().addInputs("input").addLayer("cnn1", new ConvolutionLayer.Builder(2, 2).stride(2, 2).nIn(1).nOut(3).build(), "input").addLayer("cnn2", new ConvolutionLayer.Builder(4, 4).stride(2, 2).padding(1, 1).nIn(1).nOut(3).build(), "input").addLayer("max1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).build(), "cnn1", "cnn2").addLayer("output", new OutputLayer.Builder().nIn(7 * 7 * 6).nOut(10).build(), "max1").setOutputs("output").inputPreProcessor("cnn1", new FeedForwardToCnnPreProcessor(28, 28, 1)).inputPreProcessor("cnn2", new FeedForwardToCnnPreProcessor(28, 28, 1)).inputPreProcessor("output", new CnnToFeedForwardPreProcessor(7, 7, 6)).pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    graph.setListeners(new HistogramIterationListener(1), new ScoreIterationListener(1));
    DataSetIterator mnist = new MnistDataSetIterator(32, 640, false, true, false, 12345);
    graph.fit(mnist);
}