org.deeplearning4j.arbiter.layers.DenseLayerSpace Java Examples
The following examples show how to use
org.deeplearning4j.arbiter.layers.DenseLayerSpace.
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
| 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 #2
| Source File: TestJson.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testComputationGraphSpaceJson() {
ParameterSpace<Integer> p = new IntegerParameterSpace(10, 100);
ComputationGraphSpace cgs =
new ComputationGraphSpace.Builder()
.updater(new AdamSpace(new DiscreteParameterSpace<>(0.1, 0.5, 1.0)))
.seed(12345).addInputs("in")
.addLayer("0", new DenseLayerSpace.Builder()
.nIn(new IntegerParameterSpace(1, 100)).nOut(p).build(), "in")
.addLayer("1", new DenseLayerSpace.Builder().nIn(p).nOut(10).build(), "0")
.addLayer("2", new OutputLayerSpace.Builder().iLossFunction(
LossFunctions.LossFunction.MCXENT.getILossFunction()).nIn(10)
.nOut(5).build(), "1")
.setOutputs("2").build();
String asJson = cgs.toJson();
ComputationGraphSpace fromJson = ComputationGraphSpace.fromJson(asJson);
assertEquals(cgs, fromJson);
}
Example #3
| 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 #4
| 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 #5
| 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 #6
| 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 #7
| 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 #8
| Source File: TestComputationGraphSpace.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testBasic() {
ComputationGraphConfiguration expected = new NeuralNetConfiguration.Builder()
.updater(new Sgd(0.005))
.seed(12345)
.graphBuilder().addInputs("in")
.addLayer("0", new DenseLayer.Builder().nIn(10).nOut(10).build(), "in")
.addLayer("1", new DenseLayer.Builder().nIn(10).nOut(10).build(), "0").addLayer("2",
new OutputLayer.Builder().lossFunction(LossFunction.MCXENT)
.activation(Activation.SOFTMAX)
.nIn(10).nOut(5)
.build(),
"1")
.setOutputs("2").build();
ComputationGraphSpace cgs = new ComputationGraphSpace.Builder()
.updater(new Sgd(0.005))
.seed(12345).addInputs("in")
.addLayer("0", new DenseLayerSpace.Builder().nIn(10).nOut(10).build(), "in")
.addLayer("1", new DenseLayerSpace.Builder().nIn(10).nOut(10).build(), "0")
.addLayer("2", new OutputLayerSpace.Builder().lossFunction(LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(10).nOut(5)
.build(), "1")
.setOutputs("2").setInputTypes(InputType.feedForward(10))
.build();
int nParams = cgs.numParameters();
assertEquals(0, nParams);
ComputationGraphConfiguration conf = cgs.getValue(new double[0]).getConfiguration();
assertEquals(expected, conf);
}
Example #9
| 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 #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: 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 #17
| 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 #18
| 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 #19
| 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 #20
| 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 #21
| 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 #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: 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 #24
| 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);
}
