本文整理汇总了Java中org.deeplearning4j.eval.Evaluation.f1方法的典型用法代码示例。如果您正苦于以下问题：Java Evaluation.f1方法的具体用法？Java Evaluation.f1怎么用？Java Evaluation.f1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.f1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: testMLPMultiLayerBackprop
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的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2: 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: testCNNMLNPretrain
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
@Test
public void testCNNMLNPretrain() throws Exception {
// Note CNN does not do pretrain
int numSamples = 10;
int batchSize = 10;
DataSetIterator mnistIter = new MnistDataSetIterator(batchSize, numSamples, true);
MultiLayerNetwork model = getCNNMLNConfig(false, true);
model.fit(mnistIter);
mnistIter.reset();
MultiLayerNetwork model2 = getCNNMLNConfig(false, true);
model2.fit(mnistIter);
mnistIter.reset();
DataSet test = mnistIter.next();
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5: testCNNMLNBackprop
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
@Test
public void testCNNMLNBackprop() throws Exception {
int numSamples = 10;
int batchSize = 10;
DataSetIterator mnistIter = new MnistDataSetIterator(batchSize, numSamples, true);
MultiLayerNetwork model = getCNNMLNConfig(true, false);
model.fit(mnistIter);
MultiLayerNetwork model2 = getCNNMLNConfig(true, false);
model2.fit(mnistIter);
DataSet test = mnistIter.next();
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6: testMLPMultiLayerPretrain
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
@Test
public void testMLPMultiLayerPretrain() {
// Note CNN does not do pretrain
MultiLayerNetwork model = getDenseMLNConfig(false, true);
model.fit(iter);
MultiLayerNetwork model2 = getDenseMLNConfig(false, true);
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7: f1Score
import org.deeplearning4j.eval.Evaluation; //导入方法依赖的package包/类
/**
* Returns the f1 score for the given examples.
* Think of this to be like a percentage right.
* The higher the number the more it got right.
* This is on a scale from 0 to 1.
*
* @param examples te the examples to classify (one example in each row)
* @param labels the true labels
* @return the scores for each ndarray
*/
@Override
public double f1Score(INDArray examples, INDArray labels) {
Evaluation eval = new Evaluation();
eval.eval(labels, labelProbabilities(examples));
return eval.f1();
}