本文整理汇总了Java中org.deeplearning4j.ui.storage.InMemoryStatsStorage类的典型用法代码示例。如果您正苦于以下问题：Java InMemoryStatsStorage类的具体用法？Java InMemoryStatsStorage怎么用？Java InMemoryStatsStorage使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
InMemoryStatsStorage类属于org.deeplearning4j.ui.storage包，在下文中一共展示了InMemoryStatsStorage类的11个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Java代码示例。
示例1: testListenersViaModel
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
@Test
public void testListenersViaModel() {
TestListener.clearCounts();
MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().list().layer(0,
new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(10).nOut(10)
.activation(Activation.TANH).build());
MultiLayerConfiguration conf = builder.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
StatsStorage ss = new InMemoryStatsStorage();
model.setListeners(new TestListener(), new StatsListener(ss));
testListenersForModel(model, null);
assertEquals(1, ss.listSessionIDs().size());
assertEquals(2, ss.listWorkerIDsForSession(ss.listSessionIDs().get(0)).size());
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:21，代码来源:TestListeners.java
示例2: testListenersViaModelGraph
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
@Test
public void testListenersViaModelGraph() {
TestListener.clearCounts();
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder()
.addInputs("in").addLayer("0",
new OutputLayer.Builder(LossFunctions.LossFunction.MSE).nIn(10).nOut(10)
.activation(Activation.TANH).build(),
"in")
.setOutputs("0").build();
ComputationGraph model = new ComputationGraph(conf);
model.init();
StatsStorage ss = new InMemoryStatsStorage();
model.setListeners(new TestListener(), new StatsListener(ss));
testListenersForModel(model, null);
assertEquals(1, ss.listSessionIDs().size());
assertEquals(2, ss.listWorkerIDsForSession(ss.listSessionIDs().get(0)).size());
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:23，代码来源:TestListeners.java
示例3: enableRemoteListener
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
@Override
public void enableRemoteListener() {
if (remoteReceiverModule == null)
remoteReceiverModule = new RemoteReceiverModule();
if (remoteReceiverModule.isEnabled())
return;
enableRemoteListener(new InMemoryStatsStorage(), true);
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:9，代码来源:PlayUIServer.java
示例4: testUIMultipleSessions
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
@Test
@Ignore
public void testUIMultipleSessions() throws Exception {
for (int session = 0; session < 3; session++) {
StatsStorage ss = new InMemoryStatsStorage();
UIServer uiServer = UIServer.getInstance();
uiServer.attach(ss);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build())
.layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(4).nOut(3).build())
.pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setListeners(new StatsListener(ss), new ScoreIterationListener(1));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
for (int i = 0; i < 20; i++) {
net.fit(iter);
Thread.sleep(100);
}
}
Thread.sleep(1000000);
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:34，代码来源:TestPlayUI.java
示例5: testUICompGraph
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
@Test
@Ignore
public void testUICompGraph() throws Exception {
StatsStorage ss = new InMemoryStatsStorage();
UIServer uiServer = UIServer.getInstance();
uiServer.attach(ss);
ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in")
.addLayer("L0", new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build(),
"in")
.addLayer("L1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(4).nOut(3).build(), "L0")
.pretrain(false).backprop(true).setOutputs("L1").build();
ComputationGraph net = new ComputationGraph(conf);
net.init();
net.setListeners(new StatsListener(ss), new ScoreIterationListener(1));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
for (int i = 0; i < 100; i++) {
net.fit(iter);
Thread.sleep(100);
}
Thread.sleep(100000);
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:31，代码来源:TestPlayUI.java
示例6: testParallelStatsListenerCompatibility
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
@Test
@Ignore //To be run manually
public void testParallelStatsListenerCompatibility() throws Exception {
UIServer uiServer = UIServer.getInstance();
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd()).weightInit(WeightInit.XAVIER).list()
.layer(0, new DenseLayer.Builder().nIn(4).nOut(3).build())
.layer(1, new OutputLayer.Builder().nIn(3).nOut(3)
.lossFunction(LossFunctions.LossFunction.MCXENT).build())
.pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
// it's important that the UI can report results from parallel training
// there's potential for StatsListener to fail if certain properties aren't set in the model
StatsStorage statsStorage = new InMemoryStatsStorage();
net.setListeners(new StatsListener(statsStorage));
uiServer.attach(statsStorage);
DataSetIterator irisIter = new IrisDataSetIterator(50, 500);
EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
EarlyStoppingConfiguration<MultiLayerNetwork> esConf =
new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>()
.epochTerminationConditions(new MaxEpochsTerminationCondition(500))
.scoreCalculator(new DataSetLossCalculator(irisIter, true))
.evaluateEveryNEpochs(2).modelSaver(saver).build();
IEarlyStoppingTrainer<MultiLayerNetwork> trainer =
new EarlyStoppingParallelTrainer<>(esConf, net, irisIter, null, 3, 6, 2);
EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
System.out.println(result);
assertEquals(EarlyStoppingResult.TerminationReason.EpochTerminationCondition, result.getTerminationReason());
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:37，代码来源:TestParallelEarlyStoppingUI.java
示例7: main
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
public static void main(String[] args) {
UIServer server = UIServer.getInstance();
StatsStorage statsStorage = new InMemoryStatsStorage();
server.attach(statsStorage);
server.enableRemoteListener();
}
开发者ID:buybrain，项目名称:docker-dl4j-ui，代码行数:7，代码来源:Server.java
示例8: LSTMTrainer
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
/**
* Constructor
* @param trainingSet Text file containing several ABC music files
* @throws IOException
*/
public LSTMTrainer(String trainingSet, int seed) throws IOException {
lstmLayerSize_ = 200; // original 200
batchSize_ = 32; // original 32
truncatedBackPropThroughTimeLength_ = 50;
nbEpochs_ = 100;
learningRate_ = 0.04; // 0.1 original // best 0.05 3epochs
generateSamplesEveryNMinibatches_ = 200;
generationInitialization_ = "X";
seed_ = seed;
random_ = new Random(seed);
output_ = null;
trainingSetIterator_ = new ABCIterator(trainingSet, Charset.forName("ASCII"), batchSize_, random_);
charToInt_ = trainingSetIterator_.getCharToInt();
intToChar_ = trainingSetIterator_.getIntToChar();
exampleLength_ = trainingSetIterator_.getExampleLength();
int nOut = trainingSetIterator_.totalOutcomes();
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.learningRate(learningRate_)
.rmsDecay(0.95) // 0.95 original
.seed(seed_)
.regularization(true) // true original
.l2(0.001)
.weightInit(WeightInit.XAVIER)
.updater(Updater.RMSPROP)
.list()
.layer(0, new GravesLSTM.Builder().nIn(trainingSetIterator_.inputColumns()).nOut(lstmLayerSize_)
.activation("tanh").build())
.layer(1, new GravesLSTM.Builder().nIn(lstmLayerSize_).nOut(lstmLayerSize_)
.activation("tanh").build())
.layer(2, new GravesLSTM.Builder().nIn(lstmLayerSize_).nOut(lstmLayerSize_)
.activation("tanh").build())
.layer(3, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT).activation("softmax")
.nIn(lstmLayerSize_).nOut(nOut).build())
.backpropType(BackpropType.TruncatedBPTT)
.tBPTTForwardLength(truncatedBackPropThroughTimeLength_)
.tBPTTBackwardLength(truncatedBackPropThroughTimeLength_)
.pretrain(false).backprop(true)
.build();
lstmNet_ = new MultiLayerNetwork(conf);
lstmNet_.init();
//lstmNet_.setListeners(new ScoreIterationListener(1));
//lstmNet_.setListeners(new HistogramIterationListener(1));
UIServer uiServer = UIServer.getInstance();
StatsStorage statsStorage = new InMemoryStatsStorage();
uiServer.attach(statsStorage);
lstmNet_.setListeners(new StatsListener(statsStorage));
if (ExecutionParameters.verbose) {
Layer[] layers = lstmNet_.getLayers();
int totalNumParams = 0;
for (int i = 0; i < layers.length; i++) {
int nParams = layers[i].numParams();
System.out.println("Number of parameters in layer " + i + ": " + nParams);
totalNumParams += nParams;
}
System.out.println("Total number of network parameters: " + totalNumParams);
}
}
开发者ID:paveyry，项目名称:LyreLand，代码行数:69，代码来源:LSTMTrainer.java
示例9: train
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
private static void train(CommandLine c) {
int nEpochs = Integer.parseInt(c.getOptionValue("e"));
String modelName = c.getOptionValue("o");
DataIterator<NormalizerStandardize> it = DataIterator.irisCsv(c.getOptionValue("i"));
RecordReaderDataSetIterator trainData = it.getIterator();
NormalizerStandardize normalizer = it.getNormalizer();
log.info("Data Loaded");
MultiLayerConfiguration conf = net(4, 3);
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
UIServer uiServer = UIServer.getInstance();
StatsStorage statsStorage = new InMemoryStatsStorage();
uiServer.attach(statsStorage);
model.setListeners(Arrays.asList(new ScoreIterationListener(1), new StatsListener(statsStorage)));
for (int i = 0; i < nEpochs; i++) {
log.info("Starting epoch {} of {}", i, nEpochs);
while (trainData.hasNext()) {
model.fit(trainData.next());
}
log.info("Finished epoch {}", i);
trainData.reset();
}
try {
ModelSerializer.writeModel(model, modelName, true);
normalizer.save(
new File(modelName + ".norm1"),
new File(modelName + ".norm2"),
new File(modelName + ".norm3"),
new File(modelName + ".norm4")
);
} catch (IOException e) {
e.printStackTrace();
}
log.info("Model saved to: {}", modelName);
}
开发者ID:wmeddie，项目名称:dl4j-trainer-archetype，代码行数:47，代码来源:Train.java
示例10: testUI
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
@Test
@Ignore
public void testUI() throws Exception {
StatsStorage ss = new InMemoryStatsStorage();
PlayUIServer uiServer = (PlayUIServer) UIServer.getInstance();
assertEquals(9000, uiServer.getPort());
uiServer.stop();
PlayUIServer playUIServer = new PlayUIServer();
playUIServer.runMain(new String[] {"--uiPort", "9100", "-r", "true"});
assertEquals(9100, playUIServer.getPort());
playUIServer.stop();
// uiServer.attach(ss);
//
// MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
// .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
// .list()
// .layer(0, new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(4).build())
// .layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).nIn(4).nOut(3).build())
// .pretrain(false).backprop(true).build();
//
// MultiLayerNetwork net = new MultiLayerNetwork(conf);
// net.init();
// net.setListeners(new StatsListener(ss, 3), new ScoreIterationListener(1));
//
// DataSetIterator iter = new IrisDataSetIterator(150, 150);
//
// for (int i = 0; i < 500; i++) {
// net.fit(iter);
//// Thread.sleep(100);
// Thread.sleep(100);
// }
//
//// uiServer.stop();
Thread.sleep(100000);
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:42，代码来源:TestPlayUI.java
示例11: testUI_VAE
import org.deeplearning4j.ui.storage.InMemoryStatsStorage; //导入依赖的package包/类
@Test
@Ignore
public void testUI_VAE() throws Exception {
//Variational autoencoder - for unsupervised layerwise pretraining
StatsStorage ss = new InMemoryStatsStorage();
UIServer uiServer = UIServer.getInstance();
uiServer.attach(ss);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(new Sgd(1e-5))
.list().layer(0,
new VariationalAutoencoder.Builder().nIn(4).nOut(3).encoderLayerSizes(10, 11)
.decoderLayerSizes(12, 13).weightInit(WeightInit.XAVIER)
.pzxActivationFunction(Activation.IDENTITY)
.reconstructionDistribution(
new GaussianReconstructionDistribution())
.activation(Activation.LEAKYRELU).build())
.layer(1, new VariationalAutoencoder.Builder().nIn(3).nOut(3).encoderLayerSizes(7)
.decoderLayerSizes(8).weightInit(WeightInit.XAVIER)
.pzxActivationFunction(Activation.IDENTITY)
.reconstructionDistribution(new GaussianReconstructionDistribution())
.activation(Activation.LEAKYRELU).build())
.layer(2, new OutputLayer.Builder().nIn(3).nOut(3).build()).pretrain(true).backprop(true)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setListeners(new StatsListener(ss), new ScoreIterationListener(1));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
for (int i = 0; i < 50; i++) {
net.fit(iter);
Thread.sleep(100);
}
Thread.sleep(100000);
}
开发者ID:deeplearning4j，项目名称:deeplearning4j，代码行数:43，代码来源:TestPlayUI.java
注：本文中的org.deeplearning4j.ui.storage.InMemoryStatsStorage类示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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