@Test
public void testSameLabelsOutput() {
MultiLayerNetwork network = new MultiLayerNetwork(getNetworkConf(40));
network.init();
network.setListeners(new ScoreIterationListener(1));
network.fit(reshapeInput(data), data);
Evaluation ev = eval(network);
Assert.assertTrue(ev.f1() > 0.90);
}
/**
* 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
*/
public double f1Score(INDArray examples, INDArray labels) {
Evaluation eval = new Evaluation();
eval.eval(labels, labelProbabilities(examples));
return eval.f1();
}
