本文整理汇总了Java中org.deeplearning4j.eval.Evaluation类的典型用法代码示例。如果您正苦于以下问题：Java Evaluation类的具体用法？Java Evaluation怎么用？Java Evaluation使用的例子？那么, 这里精选的类代码示例或许可以为您提供帮助。
Evaluation类属于org.deeplearning4j.eval包，在下文中一共展示了Evaluation类的15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: testEvaluation
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Test
public void testEvaluation(){
SparkDl4jMultiLayer sparkNet = getBasicNetwork();
MultiLayerNetwork netCopy = sparkNet.getNetwork().clone();
Evaluation evalExpected = new Evaluation();
INDArray outLocal = netCopy.output(input, Layer.TrainingMode.TEST);
evalExpected.eval(labels, outLocal);
Evaluation evalActual = sparkNet.evaluate(sparkData);
assertEquals(evalExpected.accuracy(), evalActual.accuracy(), 1e-3);
assertEquals(evalExpected.f1(), evalActual.f1(), 1e-3);
assertEquals(evalExpected.getNumRowCounter(), evalActual.getNumRowCounter(), 1e-3);
assertMapEquals(evalExpected.falseNegatives(),evalActual.falseNegatives());
assertMapEquals(evalExpected.falsePositives(), evalActual.falsePositives());
assertMapEquals(evalExpected.trueNegatives(), evalActual.trueNegatives());
assertMapEquals(evalExpected.truePositives(),evalActual.truePositives());
assertEquals(evalExpected.precision(), evalActual.precision(), 1e-3);
assertEquals(evalExpected.recall(), evalActual.recall(), 1e-3);
assertEquals(evalExpected.getConfusionMatrix(), evalActual.getConfusionMatrix());
}
开发者ID:PacktPublishing，项目名称:Deep-Learning-with-Hadoop，代码行数:24，代码来源:TestSparkMultiLayerParameterAveraging.java
示例2: evalMnistTestSet
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
private static void evalMnistTestSet(MultiLayerNetwork leNetModel) throws Exception {
log.info("Load test data....");
int batchSize = 64;
DataSetIterator mnistTest = new MnistDataSetIterator(batchSize,false,12345);
log.info("Evaluate model....");
int outputNum = 10;
Evaluation eval = new Evaluation(outputNum);
while(mnistTest.hasNext()){
DataSet dataSet = mnistTest.next();
INDArray output = leNetModel.output(dataSet.getFeatureMatrix(), false);
eval.eval(dataSet.getLabels(), output);
}
log.info(eval.stats());
}
示例3: evaluate
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@SuppressWarnings("rawtypes")
public DeepBeliefNetworkModel evaluate()
{
final DataSet testingData = ((IrisData) data).getTestingData();
final Evaluation evaluation = new Evaluation(parameters.getOutputSize());
for (int j = 0; j < 2; j++)
{
final INDArray output = model.output(testingData.getFeatureMatrix(), Layer.TrainingMode.TEST);
for (int i = 0; i < output.rows(); i++)
{
String actual = testingData.getLabels().getRow(i).toString().trim();
String predicted = output.getRow(i).toString().trim();
System.out.println("actual " + actual + " vs predicted " + predicted);
}
evaluation.eval(testingData.getLabels(), output);
System.out.println(evaluation.stats());
}
return this;
}
示例4: evaluate
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Override
@SuppressWarnings("rawtypes")
public Model evaluate()
{
final Evaluation evaluation = new Evaluation(parameters.getOutputSize());
try
{
final DataSetIterator iterator = new MnistDataSetIterator(100, 10000);
while (iterator.hasNext())
{
final DataSet testingData = iterator.next();
evaluation.eval(testingData.getLabels(), model.output(testingData.getFeatureMatrix()));
}
System.out.println(evaluation.stats());
}
catch (IOException e)
{
e.printStackTrace();
}
return this;
}
示例5: evaluate
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Override
@SuppressWarnings("rawtypes")
public Model evaluate()
{
final List<INDArray> testingFeatures = ((MnistData) data).getTestingFeatures();
final List<INDArray> testingLabels = ((MnistData) data).getTestingLabels();
final Evaluation evaluation = new Evaluation(parameters.getOutputSize());
for (int i = 0; i < testingFeatures.size(); i++)
{
evaluation.eval(testingLabels.get(i), model.output(testingFeatures.get(i)));
}
// evaluation.eval(testingLabels.get(0),
// model.output(testingFeatures.get(0)));
System.out.println(evaluation.stats());
return this;
}
示例6: main
import org.deeplearning4j.eval.Evaluation; //导入依赖的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7: testMLPMultiLayerBackprop
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Test
public void testMLPMultiLayerBackprop() {
MultiLayerNetwork model = getDenseMLNConfig(true, false);
model.fit(iter);
MultiLayerNetwork model2 = getDenseMLNConfig(true, false);
model2.fit(iter);
iter.reset();
DataSet test = iter.next();
assertEquals(model.params(), model2.params());
Evaluation eval = new Evaluation();
INDArray output = model.output(test.getFeatureMatrix());
eval.eval(test.getLabels(), output);
double f1Score = eval.f1();
Evaluation eval2 = new Evaluation();
INDArray output2 = model2.output(test.getFeatureMatrix());
eval2.eval(test.getLabels(), output2);
double f1Score2 = eval2.f1();
assertEquals(f1Score, f1Score2, 1e-4);
}
示例8: testCGEvaluation
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Test
public void testCGEvaluation() {
Nd4j.getRandom().setSeed(12345);
ComputationGraphConfiguration configuration = getIrisGraphConfiguration();
ComputationGraph graph = new ComputationGraph(configuration);
graph.init();
Nd4j.getRandom().setSeed(12345);
MultiLayerConfiguration mlnConfig = getIrisMLNConfiguration();
MultiLayerNetwork net = new MultiLayerNetwork(mlnConfig);
net.init();
DataSetIterator iris = new IrisDataSetIterator(75, 150);
net.fit(iris);
iris.reset();
graph.fit(iris);
iris.reset();
Evaluation evalExpected = net.evaluate(iris);
iris.reset();
Evaluation evalActual = graph.evaluate(iris);
assertEquals(evalExpected.accuracy(), evalActual.accuracy(), 0e-4);
}
示例9: testEvaluation
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Test
public void testEvaluation() {
SparkDl4jMultiLayer sparkNet = getBasicNetwork();
MultiLayerNetwork netCopy = sparkNet.getNetwork().clone();
Evaluation evalExpected = new Evaluation();
INDArray outLocal = netCopy.output(input, Layer.TrainingMode.TEST);
evalExpected.eval(labels, outLocal);
Evaluation evalActual = sparkNet.evaluate(sparkData);
assertEquals(evalExpected.accuracy(), evalActual.accuracy(), 1e-3);
assertEquals(evalExpected.f1(), evalActual.f1(), 1e-3);
assertEquals(evalExpected.getNumRowCounter(), evalActual.getNumRowCounter(), 1e-3);
assertMapEquals(evalExpected.falseNegatives(), evalActual.falseNegatives());
assertMapEquals(evalExpected.falsePositives(), evalActual.falsePositives());
assertMapEquals(evalExpected.trueNegatives(), evalActual.trueNegatives());
assertMapEquals(evalExpected.truePositives(), evalActual.truePositives());
assertEquals(evalExpected.precision(), evalActual.precision(), 1e-3);
assertEquals(evalExpected.recall(), evalActual.recall(), 1e-3);
assertEquals(evalExpected.getConfusionMatrix(), evalActual.getConfusionMatrix());
}
示例10: evaluate
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Override
public String evaluate(FederatedDataSet federatedDataSet) {
//evaluate the model on the test set
DataSet testData = (DataSet) federatedDataSet.getNativeDataSet();
double score = model.score(testData);
Evaluation eval = new Evaluation(numClasses);
INDArray output = model.output(testData.getFeatureMatrix());
eval.eval(testData.getLabels(), output);
return "Score: " + score;
}
示例11: evaluate
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Override
public String evaluate(FederatedDataSet federatedDataSet) {
DataSet testData = (DataSet) federatedDataSet.getNativeDataSet();
List<DataSet> listDs = testData.asList();
DataSetIterator iterator = new ListDataSetIterator(listDs, BATCH_SIZE);
Evaluation eval = new Evaluation(OUTPUT_NUM); //create an evaluation object with 10 possible classes
while (iterator.hasNext()) {
DataSet next = iterator.next();
INDArray output = model.output(next.getFeatureMatrix()); //get the networks prediction
eval.eval(next.getLabels(), output); //check the prediction against the true class
}
return eval.stats();
}
示例12: evaluate
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
public void evaluate() {
log.info("Evaluate model....");
Evaluation eval = new Evaluation(ConfigurationFactory.NUM_OUTPUTS);
while (m_testSet.hasNext()) {
DataSet ds = m_testSet.next();
INDArray output = m_model.output(ds.getFeatureMatrix(), false);
eval.eval(ds.getLabels(), output);
}
log.info(eval.stats());
m_testSet.reset();
}
示例13: toNetworkStatisticsResource
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
private NetworkStatisticsResource toNetworkStatisticsResource(Evaluation evaluation) {
NetworkStatisticsResource resource = new NetworkStatisticsResource();
resource.setAccuracy(evaluation.accuracy());
resource.setF1(evaluation.f1());
resource.setPrecision(evaluation.precision());
resource.setRecall(evaluation.recall());
return resource;
}
示例14: evaluate
import org.deeplearning4j.eval.Evaluation; //导入依赖的package包/类
@Override
@SuppressWarnings("rawtypes")
public Model evaluate()
{
final DataSet testingData = ((IrisData) data).getTestingData();
final Evaluation evaluation = new Evaluation(parameters.getOutputSize());
evaluation.eval(testingData.getLabels(), model.output(testingData.getFeatureMatrix()));
System.out.println(evaluation.stats());
return this;
}
示例15: main
import org.deeplearning4j.eval.Evaluation; //导入依赖的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);
}
}