本文整理汇总了Java中org.deeplearning4j.eval.Evaluation.evalTimeSeries方法的典型用法代码示例。如果您正苦于以下问题：Java Evaluation.evalTimeSeries方法的具体用法？Java Evaluation.evalTimeSeries怎么用？Java Evaluation.evalTimeSeries使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.deeplearning4j.eval.Evaluation的用法示例。
在下文中一共展示了Evaluation.evalTimeSeries方法的7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，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: main
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
/**
* args[0] input: word2vecファイル名
* args[1] input: sentimentモデル名
* args[2] input: test親フォルダ名
*
* @param args
* @throws Exception
*/
public static void main (final String[] args) throws Exception {
if (args[0]==null || args[1]==null || args[2]==null)
System.exit(1);
WordVectors wvec = WordVectorSerializer.loadTxtVectors(new File(args[0]));
MultiLayerNetwork model = ModelSerializer.restoreMultiLayerNetwork(args[1],false);
DataSetIterator test = new AsyncDataSetIterator(
new SentimentRecurrentIterator(args[2],wvec,100,300,false),1);
Evaluation evaluation = new Evaluation();
while(test.hasNext()) {
DataSet t = test.next();
INDArray features = t.getFeatures();
INDArray lables = t.getLabels();
INDArray inMask = t.getFeaturesMaskArray();
INDArray outMask = t.getLabelsMaskArray();
INDArray predicted = model.output(features,false,inMask,outMask);
evaluation.evalTimeSeries(lables,predicted,outMask);
}
System.out.println(evaluation.stats());
}
示例2: main
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
/**
* args[0] input: word2vecファイル名
* args[1] input: 学習モデル名
* args[2] input: train/test親フォルダ名
* args[3] output: 学習モデル名
*
* @param args
* @throws Exception
*/
public static void main (final String[] args) throws Exception {
if (args[0]==null || args[1]==null || args[2]==null || args[3]==null)
System.exit(1);
WordVectors wvec = WordVectorSerializer.loadTxtVectors(new File(args[0]));
MultiLayerNetwork model = ModelSerializer.restoreMultiLayerNetwork(args[1],true);
int batchSize = 16;//100;
int testBatch = 64;
int nEpochs = 1;
System.out.println("Starting online training");
DataSetIterator train = new AsyncDataSetIterator(
new SentimentRecurrentIterator(args[2],wvec,batchSize,300,true),2);
DataSetIterator test = new AsyncDataSetIterator(
new SentimentRecurrentIterator(args[2],wvec,testBatch,300,false),2);
for( int i=0; i<nEpochs; i++ ){
model.fit(train);
train.reset();
System.out.println("Epoch " + i + " complete. Starting evaluation:");
Evaluation evaluation = new Evaluation();
while(test.hasNext()) {
DataSet t = test.next();
INDArray features = t.getFeatures();
INDArray lables = t.getLabels();
INDArray inMask = t.getFeaturesMaskArray();
INDArray outMask = t.getLabelsMaskArray();
INDArray predicted = model.output(features,false,inMask,outMask);
evaluation.evalTimeSeries(lables,predicted,outMask);
}
test.reset();
System.out.println(evaluation.stats());
System.out.println("Save model");
ModelSerializer.writeModel(model, new FileOutputStream(args[3]), true);
}
}
示例3: f1Score
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
/**{@inheritDoc}
*/
@Override
public double f1Score(INDArray examples, INDArray labels) {
INDArray out = activate(examples, false);
Evaluation eval = new Evaluation();
eval.evalTimeSeries(labels, out, maskArray);
return eval.f1();
}
示例4: f1Score
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
/**
* {@inheritDoc}
*/
@Override
public double f1Score(INDArray examples, INDArray labels) {
INDArray out = activate(examples, false);
Evaluation eval = new Evaluation();
eval.evalTimeSeries(labels, out, maskArray);
return eval.f1();
}
示例5: main
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
public static void main(String[] args) throws Exception {
getModelData();
System.out.println("Total memory = " + Runtime.getRuntime().totalMemory());
int batchSize = 50;
int vectorSize = 300;
int nEpochs = 5;
int truncateReviewsToLength = 300;
MultiLayerConfiguration sentimentNN = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.updater(Updater.RMSPROP)
.regularization(true).l2(1e-5)
.weightInit(WeightInit.XAVIER)
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue).gradientNormalizationThreshold(1.0)
.learningRate(0.0018)
.list()
.layer(0, new GravesLSTM.Builder().nIn(vectorSize).nOut(200)
.activation("softsign").build())
.layer(1, new RnnOutputLayer.Builder().activation("softmax")
.lossFunction(LossFunctions.LossFunction.MCXENT).nIn(200).nOut(2).build())
.pretrain(false).backprop(true).build();
MultiLayerNetwork net = new MultiLayerNetwork(sentimentNN);
net.init();
net.setListeners(new ScoreIterationListener(1));
WordVectors wordVectors = WordVectorSerializer.loadGoogleModel(new File(GNEWS_VECTORS_PATH), true, false);
DataSetIterator trainData = new AsyncDataSetIterator(new SentimentExampleIterator(EXTRACT_DATA_PATH, wordVectors, batchSize, truncateReviewsToLength, true), 1);
DataSetIterator testData = new AsyncDataSetIterator(new SentimentExampleIterator(EXTRACT_DATA_PATH, wordVectors, 100, truncateReviewsToLength, false), 1);
for (int i = 0; i < nEpochs; i++) {
net.fit(trainData);
trainData.reset();
Evaluation evaluation = new Evaluation();
while (testData.hasNext()) {
DataSet t = testData.next();
INDArray dataFeatures = t.getFeatureMatrix();
INDArray dataLabels = t.getLabels();
INDArray inMask = t.getFeaturesMaskArray();
INDArray outMask = t.getLabelsMaskArray();
INDArray predicted = net.output(dataFeatures, false, inMask, outMask);
evaluation.evalTimeSeries(dataLabels, predicted, outMask);
}
testData.reset();
System.out.println(evaluation.stats());
}
}
开发者ID:PacktPublishing，项目名称:Machine-Learning-End-to-Endguide-for-Java-developers，代码行数:54，代码来源:DL4JSentimentAnalysisExample.java
示例6: main
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
public static void main(String[] args) throws Exception {
downloadData();
int batchSize = 50;
int vectorSize = 300;
int nEpochs = 5;
int truncateReviewsToLength = 300;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1)
.updater(Updater.RMSPROP)
.regularization(true).l2(1e-5)
.weightInit(WeightInit.XAVIER)
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue).gradientNormalizationThreshold(1.0)
.learningRate(0.0018)
.list()
.layer(0, new GravesLSTM.Builder().nIn(vectorSize).nOut(200)
.activation("softsign").build())
.layer(1, new RnnOutputLayer.Builder().activation("softmax")
.lossFunction(LossFunctions.LossFunction.MCXENT).nIn(200).nOut(2).build())
.pretrain(false)
.backprop(true)
.build();
MultiLayerNetwork net = new MultiLayerNetwork(conf);
net.init();
net.setListeners(new ScoreIterationListener(1));
WordVectors wordVectors = WordVectorSerializer.loadGoogleModel(new File(WORD_VECTORS_PATH), true, false);
DataSetIterator train = new AsyncDataSetIterator(new SentimentExampleIterator(DATA_PATH,wordVectors,batchSize,truncateReviewsToLength,true),1);
DataSetIterator test = new AsyncDataSetIterator(new SentimentExampleIterator(DATA_PATH,wordVectors,100,truncateReviewsToLength,false),1);
System.out.println("Starting training");
for( int i=0; i<nEpochs; i++ ){
net.fit(train);
train.reset();
System.out.println("Epoch " + i + " complete. Starting evaluation:");
Evaluation evaluation = new Evaluation();
while(test.hasNext()){
DataSet t = test.next();
INDArray features = t.getFeatureMatrix();
INDArray lables = t.getLabels();
INDArray inMask = t.getFeaturesMaskArray();
INDArray outMask = t.getLabelsMaskArray();
INDArray predicted = net.output(features,false,inMask,outMask);
evaluation.evalTimeSeries(lables,predicted,outMask);
}
test.reset();
System.out.println(evaluation.stats());
}
}
示例7: main
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
/**
* args[0] input: word2vecファイル名
* args[1] input: train/test親フォルダ名
* args[2] output: 学習モデル名
*
* @param args
* @throws Exception
*/
public static void main (final String[] args) throws Exception {
if (args[0]==null || args[1]==null || args[2]==null)
System.exit(1);
WordVectors wvec = WordVectorSerializer.loadTxtVectors(new File(args[0]));
int numInputs = wvec.lookupTable().layerSize();
int numOutputs = 2; // FIXME positive or negative
int batchSize = 16;//100;
int testBatch = 64;
int nEpochs = 5000;
int listenfreq = 10;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(7485)
.updater(Updater.RMSPROP) //ADADELTA
.learningRate(0.001) //RMSPROP
.rmsDecay(0.90) //RMSPROP
//.rho(0.95) //ADADELTA
.epsilon(1e-8) //ALL
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.weightInit(WeightInit.XAVIER)
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0)
//.regularization(true)
//.l2(1e-5)
.list()
.layer(0, new GravesLSTM.Builder()
.nIn(numInputs).nOut(numInputs)
.activation("softsign")
.build())
.layer(1, new RnnOutputLayer.Builder()
.lossFunction(LossFunctions.LossFunction.MCXENT)
.activation("softmax")
.nIn(numInputs).nOut(numOutputs)
.build())
.pretrain(false).backprop(true).build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(new ScoreIterationListener(listenfreq));
LOG.info("Starting training");
DataSetIterator train = new AsyncDataSetIterator(
new SentimentRecurrentIterator(args[1],wvec,batchSize,300,true),2);
DataSetIterator test = new AsyncDataSetIterator(
new SentimentRecurrentIterator(args[1],wvec,testBatch,300,false),2);
for( int i=0; i<nEpochs; i++ ){
model.fit(train);
train.reset();
LOG.info("Epoch " + i + " complete. Starting evaluation:");
Evaluation evaluation = new Evaluation();
while(test.hasNext()) {
DataSet t = test.next();
INDArray features = t.getFeatures();
INDArray lables = t.getLabels();
INDArray inMask = t.getFeaturesMaskArray();
INDArray outMask = t.getLabelsMaskArray();
INDArray predicted = model.output(features,false,inMask,outMask);
evaluation.evalTimeSeries(lables,predicted,outMask);
}
test.reset();
LOG.info(evaluation.stats());
LOG.info("Save model");
ModelSerializer.writeModel(model, new FileOutputStream(args[2]), true);
}
}