org.deeplearning4j.arbiter.ui.listener.ArbiterStatusListener Java Examples
The following examples show how to use
org.deeplearning4j.arbiter.ui.listener.ArbiterStatusListener.
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Example #1
| 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 #2
| 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 #3
| 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 #4
| 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);
}
