org.deeplearning4j.api.storage.StatsStorage Java Examples
The following examples show how to use
org.deeplearning4j.api.storage.StatsStorage.
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Example #1
| 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 #2
| 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 #3
| 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 #4
| 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 #5
| 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 #6
| 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********************");
}
