Java Code Examples for org.deeplearning4j.ui.api.UIServer#attach()
The following examples show how to use
org.deeplearning4j.ui.api.UIServer#attach() .
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Example 1
| Source File: TestVertxUI.java From deeplearning4j with Apache License 2.0 | 6 votes |
|
@Test
public void testUICompGraph() {
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")
.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);
}
}
Example 2
| Source File: Main.java From twse-captcha-solver-dl4j with MIT License | 5 votes |
|
public static void main(String[] args) throws Exception {
long startTime = System.currentTimeMillis();
logger.info("start up time: " + startTime);
File modelDir = new File(modelDirPath);
// create dir
boolean hasDir = modelDir.exists() || modelDir.mkdirs();
logger.info(modelPath);
// create model
ComputationGraph model = createModel();
// monitor the model score
UIServer uiServer = UIServer.getInstance();
StatsStorage statsStorage = new InMemoryStatsStorage();
uiServer.attach(statsStorage);
model.setListeners(new ScoreIterationListener(36), new StatsListener(statsStorage));
// construct the iterator
MultiDataSetIterator trainMulIterator = new CaptchaSetIterator(batchSize, "train");
MultiDataSetIterator testMulIterator = new CaptchaSetIterator(batchSize, "test");
MultiDataSetIterator validateMulIterator = new CaptchaSetIterator(batchSize, "validate");
// fit
for (int i = 0; i < epochs; i++) {
System.out.println("Epoch=====================" + i);
model.fit(trainMulIterator);
}
ModelSerializer.writeModel(model, modelPath, true);
long endTime = System.currentTimeMillis();
System.out.println("=============run time=====================" + (endTime - startTime));
System.out.println("=====eval model=====test==================");
modelPredict(model, testMulIterator);
System.out.println("=====eval model=====validate==================");
modelPredict(model, validateMulIterator);
}
Example 3
| Source File: TestVertxUI.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
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())
.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);
}
}
Example 4
| Source File: TestVertxUI.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
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())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setListeners(new StatsListener(ss, 1), new ScoreIterationListener(1));
DataSetIterator iter = new IrisDataSetIterator(150, 150);
for (int i = 0; i < 20; i++) {
net.fit(iter);
Thread.sleep(100);
}
}
}
Example 5
| Source File: TestVertxUI.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@Test
public void testUIAttachDetach() throws Exception {
StatsStorage ss = new InMemoryStatsStorage();
UIServer uiServer = UIServer.getInstance();
uiServer.attach(ss);
assertFalse(uiServer.getStatsStorageInstances().isEmpty());
uiServer.detach(ss);
assertTrue(uiServer.getStatsStorageInstances().isEmpty());
}
Example 6
| Source File: TestParallelEarlyStoppingUI.java From deeplearning4j with Apache License 2.0 | 5 votes |
|
@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())
.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());
}
Example 7
| Source File: CustomerRetentionPredictionExample.java From Java-Deep-Learning-Cookbook with MIT License | 4 votes |
|
public static void main(String[] args) throws IOException, InterruptedException {
final int labelIndex=11;
final int batchSize=8;
final int numClasses=2;
final INDArray weightsArray = Nd4j.create(new double[]{0.57, 0.75});
final RecordReader recordReader = generateReader(new ClassPathResource("Churn_Modelling.csv").getFile());
final DataSetIterator dataSetIterator = new RecordReaderDataSetIterator.Builder(recordReader,batchSize)
.classification(labelIndex,numClasses)
.build();
final DataNormalization dataNormalization = new NormalizerStandardize();
dataNormalization.fit(dataSetIterator);
dataSetIterator.setPreProcessor(dataNormalization);
final DataSetIteratorSplitter dataSetIteratorSplitter = new DataSetIteratorSplitter(dataSetIterator,1250,0.8);
log.info("Building Model------------------->>>>>>>>>");
final MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
.weightInit(WeightInit.RELU_UNIFORM)
.updater(new Adam(0.015D))
.list()
.layer(new DenseLayer.Builder().nIn(11).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
.layer(new DenseLayer.Builder().nIn(6).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
.layer(new DenseLayer.Builder().nIn(6).nOut(4).activation(Activation.RELU).dropOut(0.9).build())
.layer(new OutputLayer.Builder(new LossMCXENT(weightsArray)).nIn(4).nOut(2).activation(Activation.SOFTMAX).build())
.build();
final UIServer uiServer = UIServer.getInstance();
final StatsStorage statsStorage = new InMemoryStatsStorage();
final MultiLayerNetwork multiLayerNetwork = new MultiLayerNetwork(configuration);
multiLayerNetwork.init();
multiLayerNetwork.setListeners(new ScoreIterationListener(100),
new StatsListener(statsStorage));
uiServer.attach(statsStorage);
multiLayerNetwork.fit(dataSetIteratorSplitter.getTrainIterator(),100);
final Evaluation evaluation = multiLayerNetwork.evaluate(dataSetIteratorSplitter.getTestIterator(),Arrays.asList("0","1"));
System.out.println(evaluation.stats());
final File file = new File("model.zip");
ModelSerializer.writeModel(multiLayerNetwork,file,true);
ModelSerializer.addNormalizerToModel(file,dataNormalization);
}
Example 8
| Source File: CustomerRetentionPredictionExample.java From Java-Deep-Learning-Cookbook with MIT License | 4 votes |
|
public static void main(String[] args) throws IOException, InterruptedException {
final int labelIndex=11;
final int batchSize=8;
final int numClasses=2;
final INDArray weightsArray = Nd4j.create(new double[]{0.57, 0.75});
final RecordReader recordReader = generateReader(new ClassPathResource("Churn_Modelling.csv").getFile());
final DataSetIterator dataSetIterator = new RecordReaderDataSetIterator.Builder(recordReader,batchSize)
.classification(labelIndex,numClasses)
.build();
final DataNormalization dataNormalization = new NormalizerStandardize();
dataNormalization.fit(dataSetIterator);
dataSetIterator.setPreProcessor(dataNormalization);
final DataSetIteratorSplitter dataSetIteratorSplitter = new DataSetIteratorSplitter(dataSetIterator,1250,0.8);
log.info("Building Model------------------->>>>>>>>>");
final MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
.weightInit(WeightInit.RELU_UNIFORM)
.updater(new Adam(0.015D))
.list()
.layer(new DenseLayer.Builder().nIn(11).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
.layer(new DenseLayer.Builder().nIn(6).nOut(6).activation(Activation.RELU).dropOut(0.9).build())
.layer(new DenseLayer.Builder().nIn(6).nOut(4).activation(Activation.RELU).dropOut(0.9).build())
.layer(new OutputLayer.Builder(new LossMCXENT(weightsArray)).nIn(4).nOut(2).activation(Activation.SOFTMAX).build())
.build();
final UIServer uiServer = UIServer.getInstance();
final StatsStorage statsStorage = new InMemoryStatsStorage();
final MultiLayerNetwork multiLayerNetwork = new MultiLayerNetwork(configuration);
multiLayerNetwork.init();
multiLayerNetwork.setListeners(new ScoreIterationListener(100),
new StatsListener(statsStorage));
uiServer.attach(statsStorage);
multiLayerNetwork.fit(dataSetIteratorSplitter.getTrainIterator(),100);
final Evaluation evaluation = multiLayerNetwork.evaluate(dataSetIteratorSplitter.getTestIterator(),Arrays.asList("0","1"));
System.out.println(evaluation.stats());
final File file = new File("model.zip");
ModelSerializer.writeModel(multiLayerNetwork,file,true);
ModelSerializer.addNormalizerToModel(file,dataNormalization);
}
Example 9
| Source File: MLPMnistUIExample.java From dl4j-tutorials with MIT License | 4 votes |
|
public static void main(String[] args) throws IOException {
//number of rows and columns in the input pictures
final int numRows = 28;
final int numColumns = 28;
int outputNum = 10; // number of output classes
int batchSize = 128; // batch size for each epoch
int rngSeed = 123; // random number seed for reproducibility
int numEpochs = 15; // number of epochs to perform
int listenerFrequency = 1;
//Get the DataSetIterators:
DataSetIterator mnistTrain = new MnistDataSetIterator(batchSize, true, rngSeed);
DataSetIterator mnistTest = new MnistDataSetIterator(batchSize, false, rngSeed);
log.info("Build model....");
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(rngSeed) //include a random seed for reproducibility
// use stochastic gradient descent as an optimization algorithm
.updater(new Nesterovs(0.006, 0.9))
.l2(1e-4)
.list()
.layer(0, new DenseLayer.Builder() //create the first, input layer with xavier initialization
// batchSize, features
.nIn(numRows * numColumns)
.nOut(1000)
.activation(Activation.RELU)
.weightInit(WeightInit.XAVIER)
.build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) //create hidden layer
.nIn(1000)
.nOut(outputNum)
.activation(Activation.SOFTMAX)
.weightInit(WeightInit.XAVIER)
.build())
.pretrain(false).backprop(true) //use backpropagation to adjust weights
.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
//Initialize the user interface backend
// 获取一个UI实例
UIServer uiServer = UIServer.getInstance();
//Configure where the network information (gradients, activations, score vs. time etc) is to be stored
//Then add the StatsListener to collect this information from the network, as it trains
// 训练的存储位置
StatsStorage statsStorage = new InMemoryStatsStorage(); //Alternative: new FileStatsStorage(File) - see UIStorageExample
//Attach the StatsStorage instance to the UI: this allows the contents of the StatsStorage to be visualized
uiServer.attach(statsStorage);
model.init();
//print the score with every 1 iteration
model.setListeners(new StatsListener(statsStorage, listenerFrequency)
,new ScoreIterationListener(1)
);
log.info("Train model....");
for( int i=0; i<numEpochs; i++ ){
model.fit(mnistTrain);
}
log.info("Evaluate model....");
Evaluation eval = new Evaluation(outputNum); //create an evaluation object with 10 possible classes
while(mnistTest.hasNext()){
DataSet next = mnistTest.next();
INDArray output = model.output(next.getFeatures(), false); //get the networks prediction
eval.eval(next.getLabels(), output); //check the prediction against the true class
}
log.info(eval.stats());
log.info("****************Example finished********************");
}
Example 10
| Source File: TestVertxUIMultiSession.java From deeplearning4j with Apache License 2.0 | 4 votes |
|
@Test
public void testUIAutoAttach() throws Exception {
HashMap<String, StatsStorage> statsStorageForSession = new HashMap<>();
Function<String, StatsStorage> statsStorageProvider = statsStorageForSession::get;
UIServer uIServer = UIServer.getInstance(true, statsStorageProvider);
for (int session = 0; session < 3; session++) {
int layerSize = session + 4;
InMemoryStatsStorage ss = new InMemoryStatsStorage();
String sessionId = Integer.toString(session);
statsStorageForSession.put(sessionId, ss);
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).list()
.layer(0, new DenseLayer.Builder().activation(Activation.TANH).nIn(4).nOut(layerSize).build())
.layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).nIn(layerSize).nOut(3).build())
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
StatsListener statsListener = new StatsListener(ss, 1);
statsListener.setSessionID(sessionId);
net.setListeners(statsListener, new ScoreIterationListener(1));
uIServer.attach(ss);
DataSetIterator iter = new IrisDataSetIterator(150, 150);
for (int i = 0; i < 20; i++) {
net.fit(iter);
}
assertTrue(uIServer.isAttached(statsStorageForSession.get(sessionId)));
uIServer.detach(ss);
assertFalse(uIServer.isAttached(statsStorageForSession.get(sessionId)));
/*
* Visiting /train/:sessionId to auto-attach StatsStorage
*/
String sessionUrl = trainingSessionUrl(uIServer.getAddress(), sessionId);
HttpURLConnection conn = (HttpURLConnection) new URL(sessionUrl).openConnection();
conn.connect();
assertEquals(HttpResponseStatus.OK.code(), conn.getResponseCode());
assertTrue(uIServer.isAttached(statsStorageForSession.get(sessionId)));
}
}
