Java 深度学习4J（DL4J）精确度低，召回率和F1
我将使用DL4J根据条件矩阵找到一个好的模型。我已经准备了类似CSV的数据集（如下所示的样本），在微调超参数并多次训练模型后，我仍然无法获得合理的精度、召回率和F1结果。请问我是否有错误的地方 样本数据集：Java 深度学习4J（DL4J）精确度低，召回率和F1,java,deep-learning,deeplearning4j,dl4j,Java,Deep Learning,Deeplearning4j,Dl4j,我将使用DL4J根据条件矩阵找到一个好的模型。我已经准备了类似CSV的数据集（如下所示的样本），在微调超参数并多次训练模型后，我仍然无法获得合理的精度、召回率和F1结果。请问我是否有错误的地方 样本数据集： int OUTPUT_NEURONS = 2; // Only 2 classes for output int CLASS_INDEX = 0; // First column is the label int FILE_SIZE = 0; // FILE_SIZE
int OUTPUT_NEURONS = 2; // Only 2 classes for output
int CLASS_INDEX = 0; // First column is the label
int FILE_SIZE = 0; // FILE_SIZE will be calculated while preparing the datavecRecords below
List<List<Writable>> datavecRecords = new ArrayList<>();
......
Prepare the datavecRecords using above csv data
......
CollectionRecordReader crr = new CollectionRecordReader(datavecRecords);
RecordReaderDataSetIterator iter = new RecordReaderDataSetIterator(crr, FILE_SIZE, CLASS_INDEX, OUTPUT_NEURONS);
allData = iter.next();
SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.6);
DataSet trainingData = testAndTrain.getTrain();
DataSet testData = testAndTrain.getTest();
DataNormalization normalizer = new NormalizerStandardize();
normalizer.fit(trainingData);
normalizer.transform(trainingData);
normalizer.transform(testData);
// For early escaping use
DataSetIterator trainSetIterator = new ListDataSetIterator(trainingData.asList());
DataSetIterator testSetIterator = new ListDataSetIterator(testData.asList());
// sortedKeys is the calculated number of input columns
INPUT_NEURONS = sortedKeys.size() - 1;
HIDDEN_NEURONS = FILE_SIZE / (2 * (INPUT_NEURONS + OUTPUT_NEURONS));
HIDDEN_NEURONS = HIDDEN_NEURONS <= 0 ? 1 : HIDDEN_NEURONS;
int n=0;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(12345)
.iterations(1)
.learningRate(0.001)
.weightInit(WeightInit.XAVIER)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.regularization(true).l2(1e-4)
.updater(new Nesterovs(0.001,0.9))
.list()
.layer(n++, new DenseLayer.Builder()
.nIn(INPUT_NEURONS)
.nOut(HIDDEN_NEURONS)
.activation(Activation.RELU)
.build())
.layer(n++, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
.nIn(HIDDEN_NEURONS)
.nOut(OUTPUT_NEURONS)
.activation(Activation.SOFTMAX)
.build())
.pretrain(false).backprop(true).build();
EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder()
.epochTerminationConditions(
new MaxEpochsTerminationCondition(10000),
new ScoreImprovementEpochTerminationCondition(50))
.iterationTerminationConditions(new MaxTimeIterationTerminationCondition(5, TimeUnit.MINUTES))
.scoreCalculator(new DataSetLossCalculator(testSetIterator, true))
.evaluateEveryNEpochs(1)
.modelSaver(saver)
.build();
Termination reason: EpochTerminationCondition
Termination details: ScoreImprovementEpochTerminationCondition(maxEpochsWithNoImprovement=50, minImprovement=0.0)
Total epochs: 55
Best epoch number: 4
Score at best epoch: 0.6579822991097982
Examples labeled as 0 classified by model as 0: 397 times
Examples labeled as 0 classified by model as 1: 58 times
Examples labeled as 1 classified by model as 0: 190 times
Examples labeled as 1 classified by model as 1: 55 times
==========================Scores========================================
# of classes: 2
Accuracy: 0.6457
Precision: 0.5815
Recall: 0.5485
F1 Score: 0.3073
========================================================================
Pattern1 : Accuracy: 0.6457142857142857 | Precision: 0.5815229681446081 | Recall: 0.54850863422292 | F1: 0.3072625698324022
int OUTPUT_NEURONS = 2; // Only 2 classes for output
int CLASS_INDEX = 0; // First column is the label
int FILE_SIZE = 0; // FILE_SIZE will be calculated while preparing the datavecRecords below
List<List<Writable>> datavecRecords = new ArrayList<>();
......
Prepare the datavecRecords using above csv data
......
CollectionRecordReader crr = new CollectionRecordReader(datavecRecords);
RecordReaderDataSetIterator iter = new RecordReaderDataSetIterator(crr, FILE_SIZE, CLASS_INDEX, OUTPUT_NEURONS);
allData = iter.next();
SplitTestAndTrain testAndTrain = allData.splitTestAndTrain(0.6);
DataSet trainingData = testAndTrain.getTrain();
DataSet testData = testAndTrain.getTest();
DataNormalization normalizer = new NormalizerStandardize();
normalizer.fit(trainingData);
normalizer.transform(trainingData);
normalizer.transform(testData);
// For early escaping use
DataSetIterator trainSetIterator = new ListDataSetIterator(trainingData.asList());
DataSetIterator testSetIterator = new ListDataSetIterator(testData.asList());
// sortedKeys is the calculated number of input columns
INPUT_NEURONS = sortedKeys.size() - 1;
HIDDEN_NEURONS = FILE_SIZE / (2 * (INPUT_NEURONS + OUTPUT_NEURONS));
HIDDEN_NEURONS = HIDDEN_NEURONS <= 0 ? 1 : HIDDEN_NEURONS;
int n=0;
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(12345)
.iterations(1)
.learningRate(0.001)
.weightInit(WeightInit.XAVIER)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.regularization(true).l2(1e-4)
.updater(new Nesterovs(0.001,0.9))
.list()
.layer(n++, new DenseLayer.Builder()
.nIn(INPUT_NEURONS)
.nOut(HIDDEN_NEURONS)
.activation(Activation.RELU)
.build())
.layer(n++, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)
.nIn(HIDDEN_NEURONS)
.nOut(OUTPUT_NEURONS)
.activation(Activation.SOFTMAX)
.build())
.pretrain(false).backprop(true).build();
EarlyStoppingConfiguration esConf = new EarlyStoppingConfiguration.Builder()
.epochTerminationConditions(
new MaxEpochsTerminationCondition(10000),
new ScoreImprovementEpochTerminationCondition(50))
.iterationTerminationConditions(new MaxTimeIterationTerminationCondition(5, TimeUnit.MINUTES))
.scoreCalculator(new DataSetLossCalculator(testSetIterator, true))
.evaluateEveryNEpochs(1)
.modelSaver(saver)
.build();
Termination reason: EpochTerminationCondition
Termination details: ScoreImprovementEpochTerminationCondition(maxEpochsWithNoImprovement=50, minImprovement=0.0)
Total epochs: 55
Best epoch number: 4
Score at best epoch: 0.6579822991097982
Examples labeled as 0 classified by model as 0: 397 times
Examples labeled as 0 classified by model as 1: 58 times
Examples labeled as 1 classified by model as 0: 190 times
Examples labeled as 1 classified by model as 1: 55 times
==========================Scores========================================
# of classes: 2
Accuracy: 0.6457
Precision: 0.5815
Recall: 0.5485
F1 Score: 0.3073
========================================================================
Pattern1 : Accuracy: 0.6457142857142857 | Precision: 0.5815229681446081 | Recall: 0.54850863422292 | F1: 0.3072625698324022
StatsStorage statsStorage = new InMemoryStatsStorage();
MultiLayerNetwork networkModel = new MultiLayerNetwork(conf);
networkModel.setListeners(new StatsListener(statsStorage), new ScoreIterationListener(10));
IEarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, networkModel, trainSetIterator);
EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
// -------------------------- Evaluation trained model and print results --------------------------
System.out.println("Termination reason: " + result.getTerminationReason());
System.out.println("Termination details: " + result.getTerminationDetails());
System.out.println("Total epochs: " + result.getTotalEpochs());
System.out.println("Best epoch number: " + result.getBestModelEpoch());
System.out.println("Score at best epoch: " + result.getBestModelScore());
MultiLayerNetwork bestNetwork = result.getBestModel();
Evaluation eval1 = new Evaluation(OUTPUT_NEURONS);
testSetIterator.reset();
for (int i = 0; i < testData.numExamples(); i++) {
DataSet t = testData.get(i);
INDArray features = t.getFeatureMatrix();
INDArray labels = t.getLabels();
INDArray output = bestNetwork.output(features, false);
eval1.eval(labels, output);
}
M.messageln(eval1.stats());
非常感谢您可以在这里的用户社区中找到问题的答案：代码看起来不错。你没有做错什么。我想只需要调整一下 你的60/40测试/列车划分比我通常看到的70/30或80/20要高。你有什么理由想保留更多数据进行测试吗 当你说“合理”时，你是指与基线相比？如果不是，我会从一个基本上是逻辑回归的输出层开始，你可以用它作为基线
你试过添加更多层吗？再添加一个带有辍学的图层，可能会很有用。将尝试添加该图层并查看结果。顺便说一下，我正在研究仲裁者方法，以找到更好的超参数组合。但是我在版本0.9.1上没有运气；我遇到了Maven问题，当我将Maven属性更改为1.0.0-beta2时，我的所有代码都无效。