org.nd4j.linalg.learning.config.AMSGrad Java Examples

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Example #1

Source File: AMSGradUpdater.java From nd4j with Apache License 2.0

4 votes

public AMSGradUpdater(AMSGrad config) {
    this.config = config;
}

Example #2

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

4 votes

public AMSGradUpdater(AMSGrad config) {
    this.config = config;
}

Example #3

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

4 votes

@Test @Ignore
public void testEpochUpdating() throws Exception {
    //Ensure that epoch counter is incremented properly on the workers

    File temp = testDir.newFolder();

    //TODO this probably won't work everywhere...
    String controller = Inet4Address.getLocalHost().getHostAddress();
    String networkMask = controller.substring(0, controller.lastIndexOf('.')) + ".0" + "/16";

    VoidConfiguration voidConfiguration = VoidConfiguration.builder()
            .unicastPort(40123) // Should be open for IN/OUT communications on all Spark nodes
            .networkMask(networkMask) // Local network mask
            .controllerAddress(controller)
            .meshBuildMode(MeshBuildMode.PLAIN) // everyone is connected to the master
            .build();
    SharedTrainingMaster tm = new SharedTrainingMaster.Builder(voidConfiguration, 2, new AdaptiveThresholdAlgorithm(1e-3), 16)
            .rngSeed(12345)
            .collectTrainingStats(false)
            .batchSizePerWorker(16) // Minibatch size for each worker
            .workersPerNode(2) // Workers per node
            .exportDirectory("file:///" + temp.getAbsolutePath().replaceAll("\\\\", "/"))
            .build();


    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
            .seed(12345)
            .updater(new AMSGrad(0.001))
            .graphBuilder()
            .addInputs("in")
            .layer("out", new OutputLayer.Builder().nIn(784).nOut(10).activation(Activation.SOFTMAX)
                    .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
            .setOutputs("out")
            .build();


    SparkComputationGraph sparkNet = new SparkComputationGraph(sc, conf, tm);
    sparkNet.setListeners(new TestListener());

    DataSetIterator iter = new MnistDataSetIterator(16, true, 12345);
    int count = 0;
    List<String> paths = new ArrayList<>();
    List<DataSet> ds = new ArrayList<>();
    File f = testDir.newFolder();
    while (iter.hasNext() && count++ < 8) {
        DataSet d = iter.next();
        File out = new File(f, count + ".bin");
        d.save(out);
        String path = "file:///" + out.getAbsolutePath().replaceAll("\\\\", "/");
        paths.add(path);
        ds.add(d);
    }

    JavaRDD<String> pathRdd = sc.parallelize(paths);
    for( int i=0; i<3; i++ ) {
        ThresholdAlgorithm ta = tm.getThresholdAlgorithm();
        sparkNet.fitPaths(pathRdd);
        //Check also that threshold algorithm was updated/averaged
        ThresholdAlgorithm taAfter = tm.getThresholdAlgorithm();
        assertTrue("Threshold algorithm should have been updated with different instance after averaging", ta != taAfter);
        AdaptiveThresholdAlgorithm ataAfter = (AdaptiveThresholdAlgorithm) taAfter;
        assertFalse(Double.isNaN(ataAfter.getLastSparsity()));
        assertFalse(Double.isNaN(ataAfter.getLastThreshold()));
    }

    Set<Integer> expectedEpochs = new HashSet<>(Arrays.asList(0, 1, 2));
    assertEquals(expectedEpochs, TestListener.epochs);
}