我一直在尝试运行 DL4J 为堆叠去噪自动编码器提供的示例代码。但是，我的结果非常糟糕：
Warning: class 0 was never predicted by the model. This class was excluded from the average precision
Warning: class 1 was never predicted by the model. This class was excluded from the average precision
Warning: class 2 was never predicted by the model. This class was excluded from the average precision
Warning: class 3 was never predicted by the model. This class was excluded from the average precision
Warning: class 4 was never predicted by the model. This class was excluded from the average precision
Warning: class 5 was never predicted by the model. This class was excluded from the average precision
Warning: class 6 was never predicted by the model. This class was excluded from the average precision
Warning: class 7 was never predicted by the model. This class was excluded from the average precision
Warning: class 9 was never predicted by the model. This class was excluded from the average precision
==========================Scores========================================
Accuracy: 0.0944
Precision: 0.0944
Recall: 0.1
F1 Score: 0.0971
我不确定是什么导致结果如此糟糕。我使用的是 MNIST 数据集，堆叠去噪自动编码器的代码由 DL4J 提供。我的代码如下：
/**
* Created by chris on 1/31/16.
* Import statements above
*/
public class stackedDenoisingAutoencoderExample {
private static Logger log = LoggerFactory.getLogger(stackedDenoisingAutoencoderExample.class);
public static void main(String[] args) throws Exception {
final int numRows = 28;
final int numColumns = 28;
int outputNum = 10;
int numSamples = 60000;
int batchSize = 100;
int iterations = 10;
int seed = 123;
int listenerFreq = batchSize / 5;
log.info("Load data....");
DataSetIterator iter = new MnistDataSetIterator(batchSize,numSamples,true);
log.info("Build model....");
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)
.gradientNormalizationThreshold(1.0)
.iterations(iterations)
.momentum(0.5)
.momentumAfter(Collections.singletonMap(3, 0.9))
.optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT)
.list(4)
.layer(0, new AutoEncoder.Builder().nIn(numRows * numColumns).nOut(500)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.corruptionLevel(0.3)
.build())
.layer(1, new AutoEncoder.Builder().nIn(500).nOut(250)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.corruptionLevel(0.3)
.build())
.layer(2, new AutoEncoder.Builder().nIn(250).nOut(200)
.weightInit(WeightInit.XAVIER).lossFunction(LossFunctions.LossFunction.RMSE_XENT)
.corruptionLevel(0.3)
.build())
.layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD).activation("softmax")
.nIn(200).nOut(outputNum).build())
.pretrain(true).backprop(false)
.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(Arrays.asList((IterationListener) new ScoreIterationListener(listenerFreq)));
log.info("Train model....");
model.fit(iter); // achieves end to end pre-training
log.info("Evaluate model....");
Evaluation eval = new Evaluation(outputNum);
DataSetIterator testIter = new MnistDataSetIterator(100,10000);
while(testIter.hasNext()) {
DataSet testMnist = testIter.next();
INDArray predict2 = model.output(testMnist.getFeatureMatrix());
eval.eval(testMnist.getLabels(), predict2);
}
log.info(eval.stats());
log.info("****************Example finished********************");
}
}
谢谢！