org.deeplearning4j.arbiter.optimize.generator.RandomSearchGenerator Java Examples
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org.deeplearning4j.arbiter.optimize.generator.RandomSearchGenerator.
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Example #1
Source File: TestErrors.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Test(timeout = 20000L) public void testAllInvalidConfigCG() throws Exception { //Invalid config - basically check that this actually terminates File f = temp.newFolder(); ComputationGraphSpace mls = new ComputationGraphSpace.Builder() .addInputs("in") .layer("0", new DenseLayerSpace.Builder().nIn(4).nOut(new FixedValue<>(0)) //INVALID: nOut of 0 .activation(Activation.TANH) .build(), "in") .layer("1", new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "0") .setOutputs("1") .build(); CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3)) .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true)) .terminationConditions(new MaxCandidatesCondition(5)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration); runner.execute(); }
Example #2
Source File: TestJson.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Test public void testCandidateGeneratorJson() throws Exception { Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, new HashMap<>()); List<CandidateGenerator> l = new ArrayList<>(); l.add(new GridSearchCandidateGenerator(new DiscreteParameterSpace<>(0, 1, 2, 3, 4, 5), 10, GridSearchCandidateGenerator.Mode.Sequential, commands)); l.add(new GridSearchCandidateGenerator(new DiscreteParameterSpace<>(0, 1, 2, 3, 4, 5), 10, GridSearchCandidateGenerator.Mode.RandomOrder, commands)); l.add(new RandomSearchGenerator(new DiscreteParameterSpace<>(0, 1, 2, 3, 4, 5), commands)); for (CandidateGenerator cg : l) { String strJson = jsonMapper.writeValueAsString(cg); String strYaml = yamlMapper.writeValueAsString(cg); CandidateGenerator fromJson = jsonMapper.readValue(strJson, CandidateGenerator.class); CandidateGenerator fromYaml = yamlMapper.readValue(strYaml, CandidateGenerator.class); assertEquals(cg, fromJson); assertEquals(cg, fromYaml); } }
Example #3
Source File: TestRandomSearch.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Test public void test() throws Exception { Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, new HashMap<>()); //Define configuration: CandidateGenerator candidateGenerator = new RandomSearchGenerator(new BraninFunction.BraninSpace(), commands); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).scoreFunction(new BraninFunction.BraninScoreFunction()) .terminationConditions(new MaxCandidatesCondition(50)).build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new BraninFunction.BraninTaskCreator()); runner.addListeners(new LoggingStatusListener()); runner.execute(); // System.out.println("----- Complete -----"); }
Example #4
Source File: TestErrors.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Test(timeout = 20000L) public void testAllInvalidDataConfigMismatchCG() throws Exception { //Valid config - but mismatched with provided data File f = temp.newFolder(); ComputationGraphSpace mls = new ComputationGraphSpace.Builder() .addInputs("in") .layer("0", new DenseLayerSpace.Builder().nIn(4).nOut(10) .activation(Activation.TANH).build(), "in") .addLayer("1", new OutputLayerSpace.Builder().nIn(10).nOut(3).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "0") .setOutputs("1") .build(); CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3)) .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true)) .terminationConditions( new MaxCandidatesCondition(5)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator()); runner.execute(); }
Example #5
Source File: TestErrors.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Test(timeout = 20000L) public void testAllInvalidDataConfigMismatch() throws Exception { //Valid config - but mismatched with provided data File f = temp.newFolder(); MultiLayerSpace mls = new MultiLayerSpace.Builder() .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(10) //INVALID: nOut of 0 .activation(Activation.TANH) .build()) .addLayer(new OutputLayerSpace.Builder().nIn(10).nOut(3).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build()) .build(); CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3)) .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true)) .terminationConditions( new MaxCandidatesCondition(5)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration); runner.execute(); }
Example #6
Source File: TestErrors.java From deeplearning4j with Apache License 2.0 | 6 votes |
@Test(timeout = 20000L) public void testAllInvalidConfig() throws Exception { //Invalid config - basically check that this actually terminates File f = temp.newFolder(); MultiLayerSpace mls = new MultiLayerSpace.Builder() .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(new FixedValue<>(0)) //INVALID: nOut of 0 .activation(Activation.TANH) .build()) .addLayer(new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build()) .build(); CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3)) .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true)) .terminationConditions( new MaxCandidatesCondition(5)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration); runner.execute(); }
Example #7
Source File: TestMultiLayerSpace.java From deeplearning4j with Apache License 2.0 | 5 votes |
@Test public void testSameRanges() { ParameterSpace<Double> l1Hyperparam = new ContinuousParameterSpace(0.001, 0.1); ParameterSpace<Double> l2Hyperparam = new ContinuousParameterSpace(0.001, 0.1); MultiLayerSpace hyperparameterSpace = new MultiLayerSpace.Builder().addLayer(new DenseLayerSpace.Builder().nIn(10).nOut(10).build()) .l1(l1Hyperparam).l2(l2Hyperparam).build(); CandidateGenerator c = new RandomSearchGenerator(hyperparameterSpace, null); Candidate candidate = c.getCandidate(); }
Example #8
Source File: TestDL4JLocalExecution.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test @Ignore public void testLocalExecutionEarlyStopping() throws Exception { EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>() .epochTerminationConditions(new MaxEpochsTerminationCondition(100)) .scoreCalculator(new DataSetLossCalculator(new IrisDataSetIterator(150, 150), true)) .modelSaver(new InMemoryModelSaver()).build(); Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName()); //Define: network config (hyperparameter space) MultiLayerSpace mls = new MultiLayerSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1))) .l2(new ContinuousParameterSpace(0.0001, 0.01)) .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(new IntegerParameterSpace(2, 10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), new IntegerParameterSpace(1, 2)) //1-2 identical layers (except nIn) .addLayer(new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build()) .earlyStoppingConfiguration(esConf).build(); //Define configuration: CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, commands); DataProvider dataProvider = new DataSetIteratorFactoryProvider(); String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest2\\").getAbsolutePath(); File f = new File(modelSavePath); if (f.exists()) f.delete(); f.mkdir(); f.deleteOnExit(); if (!f.exists()) throw new RuntimeException(); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataProvider(dataProvider) .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(new TestSetLossScoreFunction()) .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES), new MaxCandidatesCondition(100)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator(new ClassificationEvaluator())); runner.execute(); System.out.println("----- COMPLETE -----"); }
Example #9
Source File: HyperParameterTuning.java From Java-Deep-Learning-Cookbook with MIT License | 4 votes |
public static void main(String[] args) { ParameterSpace<Double> learningRateParam = new ContinuousParameterSpace(0.0001,0.01); ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(5,11); MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder() .updater(new AdamSpace(learningRateParam)) // .weightInit(WeightInit.DISTRIBUTION).dist(new LogNormalDistribution()) .addLayer(new DenseLayerSpace.Builder() .activation(Activation.RELU) .nIn(11) .nOut(layerSizeParam) .build()) .addLayer(new DenseLayerSpace.Builder() .activation(Activation.RELU) .nIn(layerSizeParam) .nOut(layerSizeParam) .build()) .addLayer(new OutputLayerSpace.Builder() .activation(Activation.SIGMOID) .lossFunction(LossFunctions.LossFunction.XENT) .nOut(1) .build()) .build(); Map<String,Object> dataParams = new HashMap<>(); dataParams.put("batchSize",new Integer(10)); Map<String,Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY,ExampleDataSource.class.getCanonicalName()); CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace,dataParams); Properties dataSourceProperties = new Properties(); dataSourceProperties.setProperty("minibatchSize", "64"); ResultSaver modelSaver = new FileModelSaver("resources/"); ScoreFunction scoreFunction = new EvaluationScoreFunction(org.deeplearning4j.eval.Evaluation.Metric.ACCURACY); TerminationCondition[] conditions = { new MaxTimeCondition(120, TimeUnit.MINUTES), new MaxCandidatesCondition(30) }; OptimizationConfiguration optimizationConfiguration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator) .dataSource(ExampleDataSource.class,dataSourceProperties) .modelSaver(modelSaver) .scoreFunction(scoreFunction) .terminationConditions(conditions) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(optimizationConfiguration,new MultiLayerNetworkTaskCreator()); //Uncomment this if you want to store the model. //StatsStorage ss = new FileStatsStorage(new File("HyperParamOptimizationStats.dl4j")); //runner.addListeners(new ArbiterStatusListener(ss)); //UIServer.getInstance().attach(ss); runner.addListeners(new LoggingStatusListener()); //new ArbiterStatusListener(ss) runner.execute(); //Print the best hyper params double bestScore = runner.bestScore(); int bestCandidateIndex = runner.bestScoreCandidateIndex(); int numberOfConfigsEvaluated = runner.numCandidatesCompleted(); String s = "Best score: " + bestScore + "\n" + "Index of model with best score: " + bestCandidateIndex + "\n" + "Number of configurations evaluated: " + numberOfConfigsEvaluated + "\n"; System.out.println(s); }
Example #10
Source File: HyperParameterTuningArbiterUiExample.java From Java-Deep-Learning-Cookbook with MIT License | 4 votes |
public static void main(String[] args) { ParameterSpace<Double> learningRateParam = new ContinuousParameterSpace(0.0001,0.01); ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(5,11); MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder() .updater(new AdamSpace(learningRateParam)) // .weightInit(WeightInit.DISTRIBUTION).dist(new LogNormalDistribution()) .addLayer(new DenseLayerSpace.Builder() .activation(Activation.RELU) .nIn(11) .nOut(layerSizeParam) .build()) .addLayer(new DenseLayerSpace.Builder() .activation(Activation.RELU) .nIn(layerSizeParam) .nOut(layerSizeParam) .build()) .addLayer(new OutputLayerSpace.Builder() .activation(Activation.SIGMOID) .lossFunction(LossFunctions.LossFunction.XENT) .nOut(1) .build()) .build(); Map<String,Object> dataParams = new HashMap<>(); dataParams.put("batchSize",new Integer(10)); Map<String,Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, HyperParameterTuningArbiterUiExample.ExampleDataSource.class.getCanonicalName()); CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace,dataParams); Properties dataSourceProperties = new Properties(); dataSourceProperties.setProperty("minibatchSize", "64"); ResultSaver modelSaver = new FileModelSaver("resources/"); ScoreFunction scoreFunction = new EvaluationScoreFunction(org.deeplearning4j.eval.Evaluation.Metric.ACCURACY); TerminationCondition[] conditions = { new MaxTimeCondition(120, TimeUnit.MINUTES), new MaxCandidatesCondition(30) }; OptimizationConfiguration optimizationConfiguration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator) .dataSource(HyperParameterTuningArbiterUiExample.ExampleDataSource.class,dataSourceProperties) .modelSaver(modelSaver) .scoreFunction(scoreFunction) .terminationConditions(conditions) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(optimizationConfiguration,new MultiLayerNetworkTaskCreator()); //Uncomment this if you want to store the model. StatsStorage ss = new FileStatsStorage(new File("HyperParamOptimizationStats.dl4j")); runner.addListeners(new ArbiterStatusListener(ss)); UIServer.getInstance().attach(ss); //runner.addListeners(new LoggingStatusListener()); //new ArbiterStatusListener(ss) runner.execute(); //Print the best hyper params double bestScore = runner.bestScore(); int bestCandidateIndex = runner.bestScoreCandidateIndex(); int numberOfConfigsEvaluated = runner.numCandidatesCompleted(); String s = "Best score: " + bestScore + "\n" + "Index of model with best score: " + bestCandidateIndex + "\n" + "Number of configurations evaluated: " + numberOfConfigsEvaluated + "\n"; System.out.println(s); }
Example #11
Source File: ArbiterCLIRunnerTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testCliRunner() throws Exception { ArbiterCliRunner cliRunner = new ArbiterCliRunner(); //Define: network config (hyperparameter space) MultiLayerSpace mls = new MultiLayerSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1))) .l2(new ContinuousParameterSpace(0.0001, 0.01)) .addLayer(new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2,10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build()) .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build()) .numEpochs(3).build(); assertEquals(mls,MultiLayerSpace.fromJson(mls.toJson())); //Define configuration: Map<String,Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY,TestDataFactoryProviderMnist.class.getCanonicalName()); CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls,commands); DataProvider dataProvider = new DataSetIteratorFactoryProvider(); // String modelSavePath = FilenameUtils.concat(System.getProperty("java.io.tmpdir"),"ArbiterDL4JTest/"); String modelSavePath = new File(System.getProperty("java.io.tmpdir"),"ArbiterDL4JTest/").getAbsolutePath(); File dir = new File(modelSavePath); if(!dir.exists()) dir.mkdirs(); String configPath = System.getProperty("java.io.tmpdir") + File.separator + UUID.randomUUID().toString() + ".json"; OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator) .dataProvider(dataProvider) .modelSaver(new FileModelSaver(modelSavePath)) .scoreFunction(new TestSetLossScoreFunction()) .terminationConditions(new MaxTimeCondition(30, TimeUnit.SECONDS), new MaxCandidatesCondition(5)) .build(); assertEquals(configuration,OptimizationConfiguration.fromJson(configuration.toJson())); FileUtils.writeStringToFile(new File(configPath),configuration.toJson()); // System.out.println(configuration.toJson()); configuration.toJson(); log.info("Starting test"); cliRunner.runMain( "--dataSetIteratorClass", TestDataFactoryProviderMnist.class.getCanonicalName(), "--neuralNetType", ArbiterCliRunner.MULTI_LAYER_NETWORK, "--optimizationConfigPath", configPath ); }
Example #12
Source File: TestGraphLocalExecution.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testLocalExecutionEarlyStopping() throws Exception { EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>() .epochTerminationConditions(new MaxEpochsTerminationCondition(2)) .scoreCalculator(new ScoreProvider()) .modelSaver(new InMemoryModelSaver()).build(); Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName()); //Define: network config (hyperparameter space) ComputationGraphSpace cgs = new ComputationGraphSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new AdamSpace(new ContinuousParameterSpace(0.0001, 0.1))) .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in") .setInputTypes(InputType.feedForward(784)) .addLayer("first", new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2, 10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), "in") //1-2 identical layers (except nIn) .addLayer("out", new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "first") .setOutputs("out").earlyStoppingConfiguration(esConf).build(); //Define configuration: CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs, commands); DataProvider dataProvider = new DataSetIteratorFactoryProvider(); String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest2CG\\").getAbsolutePath(); File f = new File(modelSavePath); if (f.exists()) f.delete(); f.mkdir(); f.deleteOnExit(); if (!f.exists()) throw new RuntimeException(); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator) .dataProvider(dataProvider) .scoreFunction(ScoreFunctions.testSetF1()) .modelSaver(new FileModelSaver(modelSavePath)) .terminationConditions(new MaxTimeCondition(15, TimeUnit.SECONDS), new MaxCandidatesCondition(3)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new ComputationGraphTaskCreator()); runner.execute(); assertEquals(0, runner.numCandidatesFailed()); assertTrue(runner.numCandidatesCompleted() > 0); }
Example #13
Source File: TestGraphLocalExecution.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testLocalExecutionMDS() throws Exception { //Define: network config (hyperparameter space) ComputationGraphSpace mls = new ComputationGraphSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1))) .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in") .setInputTypes(InputType.feedForward(784)) .addLayer("layer0", new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2, 10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), "in") .addLayer("out", new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "layer0") .setOutputs("out").numEpochs(3).build(); //Define configuration: CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, null); String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest\\").getAbsolutePath(); File f = new File(modelSavePath); if (f.exists()) f.delete(); f.mkdir(); f.deleteOnExit(); if (!f.exists()) throw new RuntimeException(); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator) .dataProvider(new TestMdsDataProvider(1, 32)) .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(ScoreFunctions.testSetLoss(true)) .terminationConditions(new MaxTimeCondition(30, TimeUnit.SECONDS), new MaxCandidatesCondition(3)) .scoreFunction(ScoreFunctions.testSetAccuracy()) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new ComputationGraphTaskCreator()); runner.execute(); assertEquals(0, runner.numCandidatesFailed()); assertTrue(runner.numCandidatesCompleted() > 0); }
Example #14
Source File: TestGraphLocalExecution.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testLocalExecution() throws Exception { Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName()); //Define: network config (hyperparameter space) ComputationGraphSpace mls = new ComputationGraphSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1))) .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in") .setInputTypes(InputType.feedForward(4)) .addLayer("layer0", new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2, 10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), "in") .addLayer("out", new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "layer0") .setOutputs("out").numEpochs(3).build(); //Define configuration: CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, commands); DataProvider dataProvider = new DataSetIteratorFactoryProvider(); String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest\\").getAbsolutePath(); File f = new File(modelSavePath); if (f.exists()) f.delete(); f.mkdir(); f.deleteOnExit(); if (!f.exists()) throw new RuntimeException(); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataProvider(dataProvider) .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(ScoreFunctions.testSetLoss(true)) .terminationConditions(new MaxTimeCondition(30, TimeUnit.SECONDS), new MaxCandidatesCondition(3)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new ComputationGraphTaskCreator(new ClassificationEvaluator())); runner.execute(); assertEquals(0, runner.numCandidatesFailed()); assertTrue(runner.numCandidatesCompleted() > 0); }
Example #15
Source File: TestJson.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testOptimizationFromJsonDataSource() { for(boolean withProperties : new boolean[]{false, true}) { //Define: network config (hyperparameter space) ComputationGraphSpace cgs = new ComputationGraphSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new AdaMaxSpace(new ContinuousParameterSpace(0.0001, 0.1))) .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in") .setInputTypes(InputType.feedForward(4)) .addLayer("first", new DenseLayerSpace.Builder().nIn(4).nOut(new IntegerParameterSpace(2, 10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), "in") //1-2 identical layers (except nIn) .addLayer("out", new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "first") .setOutputs("out").build(); //Define configuration: Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName()); CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs, commands); Properties p = new Properties(); p.setProperty("minibatch", "16"); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder().candidateGenerator(candidateGenerator) .dataSource(MnistDataSource.class, (withProperties ? p : null)) .scoreFunction(new TestSetLossScoreFunction()) .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES), new MaxCandidatesCondition(100)) .build(); String json = configuration.toJson(); OptimizationConfiguration loadConf = OptimizationConfiguration.fromJson(json); assertEquals(configuration, loadConf); assertNotNull(loadConf.getDataSource()); if(withProperties){ assertNotNull(loadConf.getDataSourceProperties()); } } }
Example #16
Source File: TestJson.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testOptimizationFromJson() { EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>() .epochTerminationConditions(new MaxEpochsTerminationCondition(100)) .scoreCalculator(new DataSetLossCalculatorCG(new IrisDataSetIterator(150, 150), true)) .modelSaver(new InMemoryModelSaver<ComputationGraph>()).build(); //Define: network config (hyperparameter space) ComputationGraphSpace cgs = new ComputationGraphSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new AdaMaxSpace(new ContinuousParameterSpace(0.0001, 0.1))) .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in") .setInputTypes(InputType.feedForward(4)) .addLayer("first", new DenseLayerSpace.Builder().nIn(4).nOut(new IntegerParameterSpace(2, 10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), "in") //1-2 identical layers (except nIn) .addLayer("out", new OutputLayerSpace.Builder().nOut(3).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "first") .setOutputs("out").earlyStoppingConfiguration(esConf).build(); //Define configuration: Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName()); CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs, commands); DataProvider dataProvider = new DataSetIteratorFactoryProvider(); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder().candidateGenerator(candidateGenerator) .dataProvider(dataProvider).scoreFunction(new TestSetLossScoreFunction()) .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES), new MaxCandidatesCondition(100)) .build(); String json = configuration.toJson(); OptimizationConfiguration loadConf = OptimizationConfiguration.fromJson(json); assertEquals(configuration, loadConf); }
Example #17
Source File: TestDL4JLocalExecution.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testOcnn() { Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName()); //Define: network config (hyperparameter space) MultiLayerSpace mls = new MultiLayerSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1))) .l2(new ContinuousParameterSpace(0.0001, 0.01)) .addLayer( new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(250, 500)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), new IntegerParameterSpace(1, 2)) //1-2 identical layers (except nIn) .addLayer(new OCNNLayerSpace.Builder().nu(new ContinuousParameterSpace(0.0001, 0.1)) .numHidden(new DiscreteParameterSpace<Integer>(784 / 2,784 / 4)) .activation(Activation.HARDSIGMOID) .lossFunction(LossFunctions.LossFunction.MCXENT).build()) .setInputType(InputType.convolutionalFlat(28,28,1)) .build(); //Define configuration: CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, commands); DataProvider dataProvider = new DataSetIteratorFactoryProvider(); String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterDL4JTest3\\").getAbsolutePath(); File f = new File(modelSavePath); if (f.exists()) f.delete(); f.mkdir(); f.deleteOnExit(); if (!f.exists()) throw new RuntimeException(); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataProvider(dataProvider) .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(new TestSetLossScoreFunction()) .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES), new MaxCandidatesCondition(100)) .build(); //candidate generation: uncomment execute if you want to run IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator(new ClassificationEvaluator())); Candidate candidate = candidateGenerator.getCandidate(); // runner.execute(); System.out.println("----- COMPLETE -----"); }
Example #18
Source File: MNISTOptimizationTest.java From deeplearning4j with Apache License 2.0 | 4 votes |
public static void main(String[] args) throws Exception { EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>() .epochTerminationConditions(new MaxEpochsTerminationCondition(3)) .iterationTerminationConditions( new MaxTimeIterationTerminationCondition(5, TimeUnit.MINUTES), new MaxScoreIterationTerminationCondition(4.6) //Random score: -log_e(0.1) ~= 2.3 ).scoreCalculator(new DataSetLossCalculator(new MnistDataSetIterator(64, 2000, false, false, true, 123), true)).modelSaver(new InMemoryModelSaver()).build(); //Define: network config (hyperparameter space) MultiLayerSpace mls = new MultiLayerSpace.Builder() .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT) .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2))) .l2(new ContinuousParameterSpace(0.0001, 0.05)) .addLayer( new ConvolutionLayerSpace.Builder().nIn(1) .nOut(new IntegerParameterSpace(5, 30)) .kernelSize(new DiscreteParameterSpace<>(new int[] {3, 3}, new int[] {4, 4}, new int[] {5, 5})) .stride(new DiscreteParameterSpace<>(new int[] {1, 1}, new int[] {2, 2})) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.SOFTPLUS, Activation.LEAKYRELU)) .build(), new IntegerParameterSpace(1, 2)) //1-2 identical layers .addLayer(new DenseLayerSpace.Builder().nIn(4).nOut(new IntegerParameterSpace(2, 10)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), new IntegerParameterSpace(0, 1)) //0 to 1 layers .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build()) .earlyStoppingConfiguration(esConf).build(); Map<String, Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName()); //Define configuration: CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, commands); DataProvider dataProvider = new MnistDataSetProvider(); String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterMNISTSmall\\").getAbsolutePath(); File f = new File(modelSavePath); if (f.exists()) f.delete(); f.mkdir(); if (!f.exists()) throw new RuntimeException(); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator) .dataProvider(dataProvider) .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(new TestSetLossScoreFunction(true)) .terminationConditions(new MaxTimeCondition(120, TimeUnit.MINUTES), new MaxCandidatesCondition(100)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator()); // ArbiterUIServer server = ArbiterUIServer.getInstance(); // runner.addListeners(new UIOptimizationRunnerStatusListener(server)); runner.execute(); System.out.println("----- COMPLETE -----"); }
Example #19
Source File: TestScoreFunctions.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testROCScoreFunctions() throws Exception { for (boolean auc : new boolean[]{true, false}) { for (ROCScoreFunction.ROCType rocType : ROCScoreFunction.ROCType.values()) { String msg = (auc ? "AUC" : "AUPRC") + " - " + rocType; log.info("Starting: " + msg); ParameterSpace<Double> lr = new ContinuousParameterSpace(1e-5, 1e-3); int nOut = (rocType == ROCScoreFunction.ROCType.ROC ? 2 : 10); LossFunctions.LossFunction lf = (rocType == ROCScoreFunction.ROCType.BINARY ? LossFunctions.LossFunction.XENT : LossFunctions.LossFunction.MCXENT); Activation a = (rocType == ROCScoreFunction.ROCType.BINARY ? Activation.SIGMOID : Activation.SOFTMAX); MultiLayerSpace mls = new MultiLayerSpace.Builder() .trainingWorkspaceMode(WorkspaceMode.NONE) .inferenceWorkspaceMode(WorkspaceMode.NONE) .updater(new AdamSpace(lr)) .weightInit(WeightInit.XAVIER) .layer(new OutputLayerSpace.Builder().nIn(784).nOut(nOut) .activation(a) .lossFunction(lf).build()) .build(); CandidateGenerator cg = new RandomSearchGenerator(mls); ResultSaver rs = new InMemoryResultSaver(); ScoreFunction sf = new ROCScoreFunction(rocType, (auc ? ROCScoreFunction.Metric.AUC : ROCScoreFunction.Metric.AUPRC)); OptimizationConfiguration oc = new OptimizationConfiguration.Builder() .candidateGenerator(cg) .dataProvider(new DP(rocType)) .modelSaver(rs) .scoreFunction(sf) .terminationConditions(new MaxCandidatesCondition(3)) .rngSeed(12345) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(oc, new MultiLayerNetworkTaskCreator()); runner.execute(); List<ResultReference> list = runner.getResults(); for (ResultReference rr : list) { DataSetIterator testIter = new MnistDataSetIterator(4, 16, false, false, false, 12345); testIter.setPreProcessor(new PreProc(rocType)); OptimizationResult or = rr.getResult(); MultiLayerNetwork net = (MultiLayerNetwork) or.getResultReference().getResultModel(); double expScore; switch (rocType){ case ROC: if(auc){ expScore = net.doEvaluation(testIter, new ROC())[0].calculateAUC(); } else { expScore = net.doEvaluation(testIter, new ROC())[0].calculateAUCPR(); } break; case BINARY: if(auc){ expScore = net.doEvaluation(testIter, new ROCBinary())[0].calculateAverageAuc(); } else { expScore = net.doEvaluation(testIter, new ROCBinary())[0].calculateAverageAUCPR(); } break; case MULTICLASS: if(auc){ expScore = net.doEvaluation(testIter, new ROCMultiClass())[0].calculateAverageAUC(); } else { expScore = net.doEvaluation(testIter, new ROCMultiClass())[0].calculateAverageAUCPR(); } break; default: throw new RuntimeException(); } DataSetIterator iter = new MnistDataSetIterator(4, 16, false, false, false, 12345); iter.setPreProcessor(new PreProc(rocType)); assertEquals(msg, expScore, or.getScore(), 1e-4); } } } }
Example #20
Source File: HyperParameterTuning.java From Java-Deep-Learning-Cookbook with MIT License | 4 votes |
public static void main(String[] args) { ParameterSpace<Double> learningRateParam = new ContinuousParameterSpace(0.0001,0.01); ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(5,11); MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder() .updater(new AdamSpace(learningRateParam)) // .weightInit(WeightInit.DISTRIBUTION).dist(new LogNormalDistribution()) .addLayer(new DenseLayerSpace.Builder() .activation(Activation.RELU) .nIn(11) .nOut(layerSizeParam) .build()) .addLayer(new DenseLayerSpace.Builder() .activation(Activation.RELU) .nIn(layerSizeParam) .nOut(layerSizeParam) .build()) .addLayer(new OutputLayerSpace.Builder() .activation(Activation.SIGMOID) .lossFunction(LossFunctions.LossFunction.XENT) .nOut(1) .build()) .build(); Map<String,Object> dataParams = new HashMap<>(); dataParams.put("batchSize",new Integer(10)); Map<String,Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY,ExampleDataSource.class.getCanonicalName()); CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace,dataParams); Properties dataSourceProperties = new Properties(); dataSourceProperties.setProperty("minibatchSize", "64"); ResultSaver modelSaver = new FileModelSaver("resources/"); ScoreFunction scoreFunction = new EvaluationScoreFunction(org.deeplearning4j.eval.Evaluation.Metric.ACCURACY); TerminationCondition[] conditions = { new MaxTimeCondition(120, TimeUnit.MINUTES), new MaxCandidatesCondition(30) }; OptimizationConfiguration optimizationConfiguration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator) .dataSource(ExampleDataSource.class,dataSourceProperties) .modelSaver(modelSaver) .scoreFunction(scoreFunction) .terminationConditions(conditions) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(optimizationConfiguration,new MultiLayerNetworkTaskCreator()); //Uncomment this if you want to store the model. //StatsStorage ss = new FileStatsStorage(new File("HyperParamOptimizationStats.dl4j")); //runner.addListeners(new ArbiterStatusListener(ss)); //UIServer.getInstance().attach(ss); runner.addListeners(new LoggingStatusListener()); //new ArbiterStatusListener(ss) runner.execute(); //Print the best hyper params double bestScore = runner.bestScore(); int bestCandidateIndex = runner.bestScoreCandidateIndex(); int numberOfConfigsEvaluated = runner.numCandidatesCompleted(); String s = "Best score: " + bestScore + "\n" + "Index of model with best score: " + bestCandidateIndex + "\n" + "Number of configurations evaluated: " + numberOfConfigsEvaluated + "\n"; System.out.println(s); }
Example #21
Source File: HyperParameterTuningArbiterUiExample.java From Java-Deep-Learning-Cookbook with MIT License | 4 votes |
public static void main(String[] args) { ParameterSpace<Double> learningRateParam = new ContinuousParameterSpace(0.0001,0.01); ParameterSpace<Integer> layerSizeParam = new IntegerParameterSpace(5,11); MultiLayerSpace hyperParamaterSpace = new MultiLayerSpace.Builder() .updater(new AdamSpace(learningRateParam)) // .weightInit(WeightInit.DISTRIBUTION).dist(new LogNormalDistribution()) .addLayer(new DenseLayerSpace.Builder() .activation(Activation.RELU) .nIn(11) .nOut(layerSizeParam) .build()) .addLayer(new DenseLayerSpace.Builder() .activation(Activation.RELU) .nIn(layerSizeParam) .nOut(layerSizeParam) .build()) .addLayer(new OutputLayerSpace.Builder() .activation(Activation.SIGMOID) .lossFunction(LossFunctions.LossFunction.XENT) .nOut(1) .build()) .build(); Map<String,Object> dataParams = new HashMap<>(); dataParams.put("batchSize",new Integer(10)); Map<String,Object> commands = new HashMap<>(); commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, HyperParameterTuningArbiterUiExample.ExampleDataSource.class.getCanonicalName()); CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperParamaterSpace,dataParams); Properties dataSourceProperties = new Properties(); dataSourceProperties.setProperty("minibatchSize", "64"); ResultSaver modelSaver = new FileModelSaver("resources/"); ScoreFunction scoreFunction = new EvaluationScoreFunction(org.deeplearning4j.eval.Evaluation.Metric.ACCURACY); TerminationCondition[] conditions = { new MaxTimeCondition(120, TimeUnit.MINUTES), new MaxCandidatesCondition(30) }; OptimizationConfiguration optimizationConfiguration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator) .dataSource(HyperParameterTuningArbiterUiExample.ExampleDataSource.class,dataSourceProperties) .modelSaver(modelSaver) .scoreFunction(scoreFunction) .terminationConditions(conditions) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(optimizationConfiguration,new MultiLayerNetworkTaskCreator()); //Uncomment this if you want to store the model. StatsStorage ss = new FileStatsStorage(new File("HyperParamOptimizationStats.dl4j")); runner.addListeners(new ArbiterStatusListener(ss)); UIServer.getInstance().attach(ss); //runner.addListeners(new LoggingStatusListener()); //new ArbiterStatusListener(ss) runner.execute(); //Print the best hyper params double bestScore = runner.bestScore(); int bestCandidateIndex = runner.bestScoreCandidateIndex(); int numberOfConfigsEvaluated = runner.numCandidatesCompleted(); String s = "Best score: " + bestScore + "\n" + "Index of model with best score: " + bestCandidateIndex + "\n" + "Number of configurations evaluated: " + numberOfConfigsEvaluated + "\n"; System.out.println(s); }
Example #22
Source File: TestBasic.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test @Ignore public void testBasicMnistDataSource() throws InterruptedException { ParameterSpace<Double> learningRateHyperparam = new ContinuousParameterSpace(0.0001, 0.1); ParameterSpace<Integer> layerSizeHyperparam = new IntegerParameterSpace(16, 256); MultiLayerSpace hyperparameterSpace = new MultiLayerSpace.Builder() .weightInit(WeightInit.XAVIER) .l2(0.0001) .updater(new SgdSpace(learningRateHyperparam)) .addLayer(new DenseLayerSpace.Builder() .nIn(784) .activation(Activation.LEAKYRELU) .nOut(layerSizeHyperparam) .build()) .addLayer(new OutputLayerSpace.Builder() .nOut(10) .activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT) .build()) .build(); CandidateGenerator candidateGenerator = new RandomSearchGenerator(hyperparameterSpace, null); ScoreFunction scoreFunction = new EvaluationScoreFunction(Evaluation.Metric.ACCURACY); TerminationCondition[] terminationConditions = { new MaxTimeCondition(5, TimeUnit.MINUTES), new MaxCandidatesCondition(2)}; String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterUiTestBasicMnist\\").getAbsolutePath(); File f = new File(modelSavePath); if (f.exists()) f.delete(); f.mkdir(); if (!f.exists()) throw new RuntimeException(); Class<? extends DataSource> ds = MnistDataSource.class; Properties dsp = new Properties(); dsp.setProperty("minibatch", "8"); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataSource(ds, dsp) .modelSaver(new FileModelSaver(modelSavePath)) .scoreFunction(scoreFunction) .terminationConditions(terminationConditions) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator()); StatsStorage ss = new InMemoryStatsStorage(); StatusListener sl = new ArbiterStatusListener(ss); runner.addListeners(sl); UIServer.getInstance().attach(ss); runner.execute(); Thread.sleep(90000); }
Example #23
Source File: TestBasic.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test @Ignore public void testBasicMnistCompGraph() throws Exception { ComputationGraphSpace cgs = new ComputationGraphSpace.Builder() .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2))) .l2(new ContinuousParameterSpace(0.0001, 0.05)) .addInputs("in") .addLayer("0", new ConvolutionLayerSpace.Builder().nIn(1) .nOut(new IntegerParameterSpace(5, 30)) .kernelSize(new DiscreteParameterSpace<>(new int[]{3, 3}, new int[]{4, 4}, new int[]{5, 5})) .stride(new DiscreteParameterSpace<>(new int[]{1, 1}, new int[]{2, 2})) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.SOFTPLUS, Activation.LEAKYRELU)) .build(), "in") .addLayer("1", new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(32, 128)) .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH)) .build(), "0") .addLayer("out", new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX) .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "1") .setOutputs("out") .setInputTypes(InputType.convolutionalFlat(28, 28, 1)) .build(); //Define configuration: CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs); DataProvider dataProvider = new MnistDataSetProvider(); String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterUiTestBasicMnistCG\\").getAbsolutePath(); File f = new File(modelSavePath); if (f.exists()) f.delete(); f.mkdir(); if (!f.exists()) throw new RuntimeException(); OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(candidateGenerator).dataProvider(dataProvider) .modelSaver(new FileModelSaver(modelSavePath)) .scoreFunction(new TestSetLossScoreFunction(true)) .terminationConditions(new MaxTimeCondition(120, TimeUnit.MINUTES), new MaxCandidatesCondition(100)) .build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new ComputationGraphTaskCreator()); StatsStorage ss = new InMemoryStatsStorage(); StatusListener sl = new ArbiterStatusListener(ss); runner.addListeners(sl); UIServer.getInstance().attach(ss); runner.execute(); Thread.sleep(100000); }
Example #24
Source File: TestMultiLayerSpace.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testDropoutSpace(){ ParameterSpace<Double> dropout = new DiscreteParameterSpace<>(0.0, 0.5); MultiLayerSpace mls = new MultiLayerSpace.Builder().updater(new Sgd(0.005)) .dropOut(dropout) .seed(12345) .layer(new DenseLayerSpace.Builder().nOut(10) .build()) .layer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX) .build()) .setInputType(InputType.feedForward(10)) .build(); int nParams = mls.numParameters(); assertEquals(1, nParams); new RandomSearchGenerator(mls, null); //Initializes the indices Random r = new Random(12345); int countNull = 0; int count05 = 0; for( int i=0; i<10; i++ ){ double[] d = new double[nParams]; for( int j=0; j<d.length; j++ ){ d[j] = r.nextDouble(); } MultiLayerConfiguration conf = mls.getValue(d).getMultiLayerConfiguration(); IDropout d0 = conf.getConf(0).getLayer().getIDropout(); IDropout d1 = conf.getConf(1).getLayer().getIDropout(); if(d0 == null){ assertNull(d1); countNull++; } else { Dropout do0 = (Dropout)d0; Dropout do1 = (Dropout)d1; assertEquals(0.5, do0.getP(), 0.0); assertEquals(0.5, do1.getP(), 0.0); count05++; } } assertTrue(countNull > 0); assertTrue(count05 > 0); }
Example #25
Source File: TestMultiLayerSpace.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testMathOps() { ParameterSpace<Integer> firstLayerSize = new IntegerParameterSpace(10,30); ParameterSpace<Integer> secondLayerSize = new MathOp<>(firstLayerSize, Op.MUL, 3); ParameterSpace<Double> firstLayerLR = new ContinuousParameterSpace(0.01, 0.1); ParameterSpace<Double> secondLayerLR = new MathOp<>(firstLayerLR, Op.ADD, 0.2); MultiLayerSpace mls = new MultiLayerSpace.Builder().updater(new Sgd(0.005)) .seed(12345) .layer(new DenseLayerSpace.Builder().nOut(firstLayerSize) .updater(new AdamSpace(firstLayerLR)) .build()) .layer(new OutputLayerSpace.Builder().nOut(secondLayerSize) .updater(new AdamSpace(secondLayerLR)) .activation(Activation.SOFTMAX) .build()) .setInputType(InputType.feedForward(10)) .build(); int nParams = mls.numParameters(); assertEquals(2, nParams); new RandomSearchGenerator(mls, null); //Initializes the indices Random r = new Random(12345); for( int i=0; i<10; i++ ){ double[] d = new double[nParams]; for( int j=0; j<d.length; j++ ){ d[j] = r.nextDouble(); } MultiLayerConfiguration conf = mls.getValue(d).getMultiLayerConfiguration(); long l0Size = ((FeedForwardLayer)conf.getConf(0).getLayer()).getNOut(); long l1Size = ((FeedForwardLayer)conf.getConf(1).getLayer()).getNOut(); assertEquals(3*l0Size, l1Size); double l0Lr = ((FeedForwardLayer)conf.getConf(0).getLayer()).getIUpdater().getLearningRate(0,0); double l1Lr = ((FeedForwardLayer)conf.getConf(1).getLayer()).getIUpdater().getLearningRate(0,0); assertEquals(l0Lr+0.2, l1Lr, 1e-6); } }
Example #26
Source File: TestMultiLayerSpace.java From deeplearning4j with Apache License 2.0 | 4 votes |
@Test public void testInputTypeBasic() throws Exception { ParameterSpace<Integer> layerSizeHyperparam = new IntegerParameterSpace(20, 60); MultiLayerSpace hyperparameterSpace = new MultiLayerSpace.Builder().l2(0.0001) .weightInit(WeightInit.XAVIER).updater(new Nesterovs()) .addLayer(new ConvolutionLayerSpace.Builder().kernelSize(5, 5).nIn(1).stride(1, 1) .nOut(layerSizeHyperparam).activation(Activation.IDENTITY).build()) .addLayer(new SubsamplingLayerSpace.Builder().poolingType(SubsamplingLayer.PoolingType.MAX) .kernelSize(2, 2).stride(2, 2).build()) .addLayer(new ConvolutionLayerSpace.Builder().kernelSize(5, 5) //Note that nIn need not be specified in later layers .stride(1, 1).nOut(50).activation(Activation.IDENTITY).build()) .addLayer(new SubsamplingLayerSpace.Builder().poolingType(SubsamplingLayer.PoolingType.MAX) .kernelSize(2, 2).stride(2, 2).build()) .addLayer(new DenseLayerSpace.Builder().activation(Activation.RELU).nOut(500).build()) .addLayer(new OutputLayerSpace.Builder() .lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).nOut(10) .activation(Activation.SOFTMAX).build()) .setInputType(InputType.convolutionalFlat(28, 28, 1)).build(); DataProvider dataProvider = new TestDataSetProvider(); File f = testDir.newFolder(); if (f.exists()) f.delete(); f.mkdir(); ResultSaver modelSaver = new FileModelSaver(f.getAbsolutePath()); ScoreFunction scoreFunction = new TestSetAccuracyScoreFunction(); int maxCandidates = 4; TerminationCondition[] terminationConditions; terminationConditions = new TerminationCondition[] {new MaxCandidatesCondition(maxCandidates)}; //Given these configuration options, let's put them all together: OptimizationConfiguration configuration = new OptimizationConfiguration.Builder() .candidateGenerator(new RandomSearchGenerator(hyperparameterSpace, null)) .dataProvider(dataProvider).modelSaver(modelSaver).scoreFunction(scoreFunction) .terminationConditions(terminationConditions).build(); IOptimizationRunner runner = new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator()); runner.execute(); assertEquals(maxCandidates, runner.getResults().size()); }