Examples with Model org.deeplearning4j.nn.api.Model used on opensource projects

Example 1 with Model

use of org.deeplearning4j.nn.api.Model 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 Model

use of org.deeplearning4j.nn.api.Model in project deeplearning4j by deeplearning4j.

the class GravesBidirectionalLSTMTest method testConvergence.

@Test
@Ignore
public void testConvergence() {
    Nd4j.getRandom().setSeed(12345);
    final int state1Len = 100;
    final int state2Len = 30;
    //segment by signal mean
    //Data: has shape [miniBatchSize,nIn,timeSeriesLength];
    final INDArray sig1 = Nd4j.randn(new int[] { 1, 2, state1Len }).mul(0.1);
    final INDArray sig2 = Nd4j.randn(new int[] { 1, 2, state2Len }).mul(0.1).add(Nd4j.ones(new int[] { 1, 2, state2Len }).mul(1.0));
    INDArray sig = Nd4j.concat(2, sig1, sig2);
    INDArray labels = Nd4j.zeros(new int[] { 1, 2, state1Len + state2Len });
    for (int t = 0; t < state1Len; t++) {
        labels.putScalar(new int[] { 0, 0, t }, 1.0);
    }
    for (int t = state1Len; t < state1Len + state2Len; t++) {
        labels.putScalar(new int[] { 0, 1, t }, 1.0);
    }
    for (int i = 0; i < 3; i++) {
        sig = Nd4j.concat(2, sig, sig);
        labels = Nd4j.concat(2, labels, labels);
    }
    final DataSet ds = new DataSet(sig, labels);
    final MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(5).learningRate(0.1).rmsDecay(0.95).regularization(true).l2(0.001).updater(Updater.ADAGRAD).seed(12345).list().pretrain(false).layer(0, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(-0.05, 0.05)).build()).layer(1, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(-0.05, 0.05)).build()).layer(2, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(2).activation(Activation.TANH).build()).backprop(true).build();
    final MultiLayerNetwork net = new MultiLayerNetwork(conf);
    final IterationListener scoreSaver = new IterationListener() {

        @Override
        public boolean invoked() {
            return false;
        }

        @Override
        public void invoke() {
        }

        @Override
        public void iterationDone(Model model, int iteration) {
            score = model.score();
        }
    };
    net.setListeners(scoreSaver, new ScoreIterationListener(1));
    double oldScore = Double.POSITIVE_INFINITY;
    net.init();
    for (int iEpoch = 0; iEpoch < 3; iEpoch++) {
        net.fit(ds);
        System.out.print(String.format("score is %f%n", score));
        assertTrue(!Double.isNaN(score));
        assertTrue(score < 0.9 * oldScore);
        oldScore = score;
        final INDArray output = net.output(ds.getFeatureMatrix());
        Evaluation evaluation = new Evaluation();
        evaluation.evalTimeSeries(ds.getLabels(), output);
        System.out.print(evaluation.stats() + "\n");
    }
}

Example 3 with Model

use of org.deeplearning4j.nn.api.Model in project deeplearning4j by deeplearning4j.

the class TestOptimizers method testRastriginFnMultipleStepsHelper.

private static void testRastriginFnMultipleStepsHelper(OptimizationAlgorithm oa, int nOptIter, int maxNumLineSearchIter) {
    double[] scores = new double[nOptIter + 1];
    for (int i = 0; i <= nOptIter; i++) {
        NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().maxNumLineSearchIterations(maxNumLineSearchIter).iterations(i).miniBatch(false).learningRate(1e-2).layer(new DenseLayer.Builder().nIn(1).nOut(1).updater(Updater.ADAGRAD).build()).build();
        //Normally done by ParamInitializers, but obviously that isn't done here
        conf.addVariable("W");
        Model m = new RastriginFunctionModel(100, conf);
        int nParams = m.numParams();
        if (i == 0) {
            m.computeGradientAndScore();
            //Before optimization
            scores[0] = m.score();
        } else {
            ConvexOptimizer opt = getOptimizer(oa, conf, m);
            opt.getUpdater().setStateViewArray((Layer) m, Nd4j.createUninitialized(new int[] { 1, nParams }, 'c'), true);
            opt.optimize();
            m.computeGradientAndScore();
            scores[i] = m.score();
            assertTrue(!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]));
        }
    }
    if (PRINT_OPT_RESULTS) {
        System.out.println("Rastrigin: Multiple optimization iterations (" + nOptIter + " opt. iter.) score vs iteration, maxNumLineSearchIter=" + maxNumLineSearchIter + ": " + oa);
        System.out.println(Arrays.toString(scores));
    }
    for (int i = 1; i < scores.length; i++) {
        if (i == 1) {
            //Require at least one step of improvement
            assertTrue(scores[i] <= scores[i - 1]);
        } else {
            assertTrue(scores[i] <= scores[i - 1]);
        }
    }
}

Example 4 with Model

use of org.deeplearning4j.nn.api.Model in project deeplearning4j by deeplearning4j.

the class TestOptimizers method testSphereFnOptHelper.

public void testSphereFnOptHelper(OptimizationAlgorithm oa, int numLineSearchIter, int nDimensions) {
    if (PRINT_OPT_RESULTS)
        System.out.println("---------\n Alg= " + oa + ", nIter= " + numLineSearchIter + ", nDimensions= " + nDimensions);
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().maxNumLineSearchIterations(numLineSearchIter).iterations(100).learningRate(1e-2).layer(new RBM.Builder().nIn(1).nOut(1).updater(Updater.SGD).build()).build();
    //Normally done by ParamInitializers, but obviously that isn't done here
    conf.addVariable("W");
    Random rng = new DefaultRandom(12345L);
    org.nd4j.linalg.api.rng.distribution.Distribution dist = new org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution(rng, -10, 10);
    Model m = new SphereFunctionModel(nDimensions, dist, conf);
    m.computeGradientAndScore();
    double scoreBefore = m.score();
    assertTrue(!Double.isNaN(scoreBefore) && !Double.isInfinite(scoreBefore));
    if (PRINT_OPT_RESULTS) {
        System.out.println("Before:");
        System.out.println(scoreBefore);
        System.out.println(m.params());
    }
    ConvexOptimizer opt = getOptimizer(oa, conf, m);
    opt.setupSearchState(m.gradientAndScore());
    opt.optimize();
    m.computeGradientAndScore();
    double scoreAfter = m.score();
    assertTrue(!Double.isNaN(scoreAfter) && !Double.isInfinite(scoreAfter));
    if (PRINT_OPT_RESULTS) {
        System.out.println("After:");
        System.out.println(scoreAfter);
        System.out.println(m.params());
    }
    //Expected behaviour after optimization:
    //(a) score is better (lower) after optimization.
    //(b) Parameters are closer to minimum after optimization (TODO)
    assertTrue("Score did not improve after optimization (b= " + scoreBefore + " ,a= " + scoreAfter + ")", scoreAfter < scoreBefore);
}

Example 5 with Model

use of org.deeplearning4j.nn.api.Model in project deeplearning4j by deeplearning4j.

the class TestOptimizers method testSphereFnMultipleStepsHelper.

private static void testSphereFnMultipleStepsHelper(OptimizationAlgorithm oa, int nOptIter, int maxNumLineSearchIter) {
    double[] scores = new double[nOptIter + 1];
    for (int i = 0; i <= nOptIter; i++) {
        Random rng = new DefaultRandom(12345L);
        org.nd4j.linalg.api.rng.distribution.Distribution dist = new org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution(rng, -10, 10);
        NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().maxNumLineSearchIterations(maxNumLineSearchIter).iterations(i).learningRate(0.1).layer(new DenseLayer.Builder().nIn(1).nOut(1).updater(Updater.SGD).build()).build();
        //Normally done by ParamInitializers, but obviously that isn't done here
        conf.addVariable("W");
        Model m = new SphereFunctionModel(100, dist, conf);
        if (i == 0) {
            m.computeGradientAndScore();
            //Before optimization
            scores[0] = m.score();
        } else {
            ConvexOptimizer opt = getOptimizer(oa, conf, m);
            opt.optimize();
            m.computeGradientAndScore();
            scores[i] = m.score();
            assertTrue(!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]));
        }
    }
    if (PRINT_OPT_RESULTS) {
        System.out.println("Multiple optimization iterations (" + nOptIter + " opt. iter.) score vs iteration, maxNumLineSearchIter=" + maxNumLineSearchIter + ": " + oa);
        System.out.println(Arrays.toString(scores));
    }
    for (int i = 1; i < scores.length; i++) {
        assertTrue(scores[i] <= scores[i - 1]);
    }
    //Very easy function, expect score ~= 0 with any reasonable number of steps/numLineSearchIter
    assertTrue(scores[scores.length - 1] < 1.0);
}