org.deeplearning4j.arbiter.optimize.api.termination.TerminationCondition Java Examples
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org.deeplearning4j.arbiter.optimize.api.termination.TerminationCondition.
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Example #1
| Source File: ArbiterCliGenerator.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
private List<TerminationCondition> getConditions() {
List<TerminationCondition> ret = new ArrayList<>();
if(duration > 0) {
ret.add(new MaxTimeCondition(duration,TimeUnit.MINUTES));
}
if(numCandidates > 0)
ret.add(new MaxCandidatesCondition(numCandidates));
if(ret.isEmpty()) {
ret.add(new MaxCandidatesCondition(1));
}
return ret;
}
Example #2
| Source File: BaseOptimizationRunner.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
private boolean terminate() {
for (TerminationCondition c : config.getTerminationConditions()) {
if (c.terminate(this)) {
log.info("BaseOptimizationRunner global termination condition hit: {}", c);
return true;
}
}
return false;
}
Example #3
| 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 #4
| 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 #5
| 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 #6
| 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 #7
| 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 #8
| 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());
}
Example #9
| Source File: OptimizationConfiguration.java From deeplearning4j with Apache License 2.0 | 2 votes |
|
/**
* Termination conditions to use
* @param conditions
* @return
*/
public Builder terminationConditions(TerminationCondition... conditions) {
terminationConditions = Arrays.asList(conditions);
return this;
}
