org.deeplearning4j.arbiter.optimize.parameter.continuous.ContinuousParameterSpace Java Examples
The following examples show how to use
org.deeplearning4j.arbiter.optimize.parameter.continuous.ContinuousParameterSpace.
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Example #1
| Source File: TestJson.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testMultiLayerSpaceJson() {
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 DenseLayerSpace.Builder().nIn(1).nOut(new IntegerParameterSpace(5, 30))
.activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.SOFTPLUS,
Activation.LEAKYRELU))
.build(), new IntegerParameterSpace(1, 2), true) //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), true) //0 to 1 layers
.addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
.iLossFunction(LossFunctions.LossFunction.MCXENT.getILossFunction()).build())
.setInputType(InputType.convolutional(28, 28, 1)).build();
String asJson = mls.toJson();
// System.out.println(asJson);
MultiLayerSpace fromJson = MultiLayerSpace.fromJson(asJson);
assertEquals(mls, fromJson);
}
Example #2
| Source File: TestBasic.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
private static MultiLayerSpace getMultiLayerSpaceMnist() {
return new MultiLayerSpace.Builder()
.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())
.addLayer(new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(32, 128))
.activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
.build(), new IntegerParameterSpace(0, 1), true) //0 to 1 layers
.addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.setInputType(InputType.convolutionalFlat(28, 28, 1))
.build();
}
Example #3
| Source File: TestLayerSpace.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testBatchNorm() {
BatchNormalizationSpace sp = new BatchNormalizationSpace.Builder().gamma(1.5)
.beta(new ContinuousParameterSpace(2, 3)).lockGammaBeta(true).build();
//Set the parameter numbers...
List<ParameterSpace> list = sp.collectLeaves();
int k = 0;
for (int j = 0; j < list.size(); j++) {
if (list.get(j).numParameters() > 0) {
list.get(j).setIndices(k++);
}
}
BatchNormalization bn = sp.getValue(new double[]{0.6});
assertTrue(bn.isLockGammaBeta());
assertEquals(1.5, bn.getGamma(), 0.0);
assertEquals(0.6 * (3 - 2) + 2, bn.getBeta(), 1e-4);
}
Example #4
| Source File: TestMultiLayerSpace.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testDropout2(){
MultiLayerSpace mls = new MultiLayerSpace.Builder().updater(new Sgd(0.005)).seed(12345)
.addLayer(new ConvolutionLayerSpace.Builder().nOut(2)
.dropOut(new ContinuousParameterSpace(0.4,0.6))
.build())
.addLayer(new DropoutLayerSpace.Builder().dropOut(new ContinuousParameterSpace(0.4,0.6)).build())
.addLayer(new OutputLayerSpace.Builder().activation(Activation.SOFTMAX).nIn(10).nOut(5).build())
.setInputType(InputType.convolutional(28, 28, 1))
.build();
int nParams = mls.numParameters();
List<ParameterSpace> l = LeafUtils.getUniqueObjects(mls.collectLeaves());
int x=0;
for( ParameterSpace p : l){
int n = p.numParameters();
int[] arr = new int[n];
for(int i=0; i<arr.length; i++ ){
arr[i] = x++;
}
p.setIndices(arr);
}
MultiLayerConfiguration conf = mls.getValue(new double[nParams]).getMultiLayerConfiguration();
}
Example #5
| Source File: TestJson.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testParameterSpaceJson() throws Exception {
List<ParameterSpace<?>> l = new ArrayList<>();
l.add(new FixedValue<>(1.0));
l.add(new FixedValue<>(1));
l.add(new FixedValue<>("string"));
l.add(new ContinuousParameterSpace(-1, 1));
l.add(new ContinuousParameterSpace(new LogNormalDistribution(1, 1)));
l.add(new ContinuousParameterSpace(new NormalDistribution(2, 0.01)));
l.add(new DiscreteParameterSpace<>(1, 5, 7));
l.add(new DiscreteParameterSpace<>("first", "second", "third"));
l.add(new IntegerParameterSpace(0, 10));
l.add(new IntegerParameterSpace(new UniformIntegerDistribution(0, 50)));
l.add(new BooleanSpace());
for (ParameterSpace<?> ps : l) {
String strJson = jsonMapper.writeValueAsString(ps);
String strYaml = yamlMapper.writeValueAsString(ps);
ParameterSpace<?> fromJson = jsonMapper.readValue(strJson, ParameterSpace.class);
ParameterSpace<?> fromYaml = yamlMapper.readValue(strYaml, ParameterSpace.class);
assertEquals(ps, fromJson);
assertEquals(ps, fromYaml);
}
}
Example #6
| Source File: TestMultiLayerSpace.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testDropout(){
MultiLayerSpace mls = new MultiLayerSpace.Builder().updater(new Sgd(0.005)).seed(12345)
.addLayer(new ConvolutionLayerSpace.Builder().nOut(2)
.dropOut(new ContinuousParameterSpace(0.4,0.6))
.build())
.addLayer(new GlobalPoolingLayerSpace.Builder().dropOut(new ContinuousParameterSpace(0.4,0.6)).build())
.addLayer(new OutputLayerSpace.Builder().activation(Activation.SOFTMAX).nIn(10).nOut(5).build())
.setInputType(InputType.convolutional(28, 28, 1))
.build();
int nParams = mls.numParameters();
List<ParameterSpace> l = LeafUtils.getUniqueObjects(mls.collectLeaves());
int x=0;
for( ParameterSpace p : l){
int n = p.numParameters();
int[] arr = new int[n];
for(int i=0; i<arr.length; i++ ){
arr[i] = x++;
}
p.setIndices(arr);
}
MultiLayerConfiguration conf = mls.getValue(new double[nParams]).getMultiLayerConfiguration();
}
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: 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 #11
| 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 #12
| 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 #13
| 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 #14
| 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 #15
| 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 #16
| 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 #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: 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 #19
| Source File: TestDL4JLocalExecution.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
@org.junit.Ignore
public void testLocalExecutionGridSearch() throws Exception {
//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, 0.01))
.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())
.numEpochs(3).build();
Map<String, Object> commands = new HashMap<>();
commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());
CandidateGenerator candidateGenerator = new GridSearchCandidateGenerator(mls, 5,
GridSearchCandidateGenerator.Mode.Sequential, 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(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 #20
| 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 #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: 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 #23
| 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 #24
| 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 #25
| Source File: TestMultiLayerSpace.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testBasic0() {
MultiLayerConfiguration expected =
new NeuralNetConfiguration.Builder()
.l1Bias(0.4)
.l2Bias(0.5)
.constrainBias(new NonNegativeConstraint())
.updater(new Sgd(0.005)).seed(12345).list()
.layer(0, new DenseLayer.Builder().l1Bias(0.6).nIn(10).nOut(10).build())
.layer(1, new DenseLayer.Builder().l2Bias(0.7).constrainBias(new UnitNormConstraint()).nIn(10).nOut(10).build()).layer(2,
new OutputLayer.Builder().lossFunction(LossFunction.MCXENT).activation(Activation.SOFTMAX)
.nIn(10).nOut(5).build())
.build();
MultiLayerSpace mls =
new MultiLayerSpace.Builder()
.l1Bias(0.4)
.l2Bias(0.5)
.constrainBias(new NonNegativeConstraint())
.updater(new Sgd(0.005)).seed(12345)
.addLayer(new DenseLayerSpace.Builder().l1Bias(new ContinuousParameterSpace(0,1)).nIn(10).nOut(10).build())
.addLayer(new DenseLayerSpace.Builder().l2Bias(0.7).constrainBias(new UnitNormConstraint()).nIn(10).nOut(10).build())
.addLayer(new OutputLayerSpace.Builder().lossFunction(LossFunction.MCXENT).activation(Activation.SOFTMAX)
.nIn(10).nOut(5).build())
.build();
int nParams = mls.numParameters();
assertEquals(1, nParams);
//Assign numbers to each leaf ParameterSpace object (normally done by candidate generator - manual here for testing)
List<ParameterSpace> noDuplicatesList = LeafUtils.getUniqueObjects(mls.collectLeaves());
//Second: assign each a number
int c = 0;
for (ParameterSpace ps : noDuplicatesList) {
int np = ps.numParameters();
if (np == 1) {
ps.setIndices(c++);
} else {
int[] values = new int[np];
for (int j = 0; j < np; j++)
values[c++] = j;
ps.setIndices(values);
}
}
MultiLayerConfiguration conf = mls.getValue(new double[] {0.6}).getMultiLayerConfiguration();
assertEquals(expected, conf);
}
Example #26
| 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 -----");
}
