Examples with ComputationGraphConfiguration org.deeplearning4j.nn.conf.ComputationGraphConfiguration used on opensource projects

Example 1 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration in project deeplearning4j by deeplearning4j.

the class TestEarlyStoppingCompGraph method testListeners.

@Test
public void testListeners() {
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in").addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in").setOutputs("0").pretrain(false).backprop(true).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(5)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(1, TimeUnit.MINUTES)).scoreCalculator(new DataSetLossCalculatorCG(irisIter, true)).modelSaver(saver).build();
    LoggingEarlyStoppingListener listener = new LoggingEarlyStoppingListener();
    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter, listener);
    trainer.fit();
    assertEquals(1, listener.onStartCallCount);
    assertEquals(5, listener.onEpochCallCount);
    assertEquals(1, listener.onCompletionCallCount);
}

Example 2 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration in project deeplearning4j by deeplearning4j.

the class TestEarlyStoppingCompGraph method testTimeTermination.

@Test
public void testTimeTermination() {
    //test termination after max time
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).learningRate(1e-6).weightInit(WeightInit.XAVIER).graphBuilder().addInputs("in").addLayer("0", new OutputLayer.Builder().nIn(4).nOut(3).lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in").setOutputs("0").pretrain(false).backprop(true).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(10000)).iterationTerminationConditions(new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS), //Initial score is ~2.5
    new MaxScoreIterationTerminationCondition(7.5)).modelSaver(saver).build();
    IEarlyStoppingTrainer trainer = new EarlyStoppingGraphTrainer(esConf, net, irisIter);
    long startTime = System.currentTimeMillis();
    EarlyStoppingResult result = trainer.fit();
    long endTime = System.currentTimeMillis();
    int durationSeconds = (int) (endTime - startTime) / 1000;
    assertTrue(durationSeconds >= 3);
    assertTrue(durationSeconds <= 9);
    assertEquals(EarlyStoppingResult.TerminationReason.IterationTerminationCondition, result.getTerminationReason());
    String expDetails = new MaxTimeIterationTerminationCondition(3, TimeUnit.SECONDS).toString();
    assertEquals(expDetails, result.getTerminationDetails());
}

Example 3 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration in project deeplearning4j by deeplearning4j.

the class TestKryoWarning method doTestCG.

private static void doTestCG(SparkConf sparkConf) {
    JavaSparkContext sc = new JavaSparkContext(sparkConf);
    try {
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in").addLayer("0", new OutputLayer.Builder().nIn(10).nOut(10).build(), "in").setOutputs("0").pretrain(false).backprop(true).build();
        TrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(1).build();
        SparkListenable scg = new SparkComputationGraph(sc, conf, tm);
    } finally {
        sc.stop();
    }
}

Example 4 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration in project deeplearning4j by deeplearning4j.

the class GradientCheckTestsComputationGraph method testBasicTwoOutputs.

@Test
public void testBasicTwoOutputs() {
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).activation(Activation.TANH).updater(Updater.NONE).learningRate(1.0).graphBuilder().addInputs("in1", "in2").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in1").addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "in2").addLayer("out1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2).nOut(2).activation(Activation.IDENTITY).build(), "d0").addLayer("out2", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.L2).nIn(2).nOut(2).activation(Activation.IDENTITY).build(), "d1").setOutputs("out1", "out2").pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    System.out.println("Num layers: " + graph.getNumLayers());
    System.out.println("Num params: " + graph.numParams());
    Nd4j.getRandom().setSeed(12345);
    int nParams = graph.numParams();
    INDArray newParams = Nd4j.rand(1, nParams);
    graph.setParams(newParams);
    int[] mbSizes = new int[] { 1, 3, 10 };
    for (int minibatch : mbSizes) {
        INDArray in1 = Nd4j.rand(minibatch, 2);
        INDArray in2 = Nd4j.rand(minibatch, 2);
        INDArray labels1 = Nd4j.rand(minibatch, 2);
        INDArray labels2 = Nd4j.rand(minibatch, 2);
        String testName = "testBasicStackUnstack() - minibatch = " + minibatch;
        if (PRINT_RESULTS) {
            System.out.println(testName);
            for (int j = 0; j < graph.getNumLayers(); j++) System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
        }
        boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] { in1, in2 }, new INDArray[] { labels1, labels2 });
        assertTrue(testName, gradOK);
    }
}

Example 5 with ComputationGraphConfiguration

use of org.deeplearning4j.nn.conf.ComputationGraphConfiguration in project deeplearning4j by deeplearning4j.

the class GradientCheckTestsComputationGraph method testMultipleOutputsLayer.

@Test
public void testMultipleOutputsLayer() {
    Nd4j.getRandom().setSeed(12345);
    ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 1)).updater(Updater.NONE).learningRate(1.0).activation(Activation.TANH).graphBuilder().addInputs("i0").addLayer("d0", new DenseLayer.Builder().nIn(2).nOut(2).build(), "i0").addLayer("d1", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0").addLayer("d2", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0").addLayer("d3", new DenseLayer.Builder().nIn(2).nOut(2).build(), "d0").addLayer("out", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(6).nOut(2).build(), "d1", "d2", "d3").setOutputs("out").pretrain(false).backprop(true).build();
    ComputationGraph graph = new ComputationGraph(conf);
    graph.init();
    int[] minibatchSizes = { 1, 3 };
    for (int mb : minibatchSizes) {
        INDArray input = Nd4j.rand(mb, 2);
        INDArray out = Nd4j.rand(mb, 2);
        String msg = "testMultipleOutputsLayer() - minibatchSize = " + mb;
        if (PRINT_RESULTS) {
            System.out.println(msg);
            for (int j = 0; j < graph.getNumLayers(); j++) System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
        }
        boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[] { input }, new INDArray[] { out });
        assertTrue(msg, gradOK);
    }
}