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September 9, 2016 05:06
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UCISequenceClassificationExample test throws no such element
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public static void main(String[] args) throws Exception { | |
downloadUCIData(); | |
// ----- Load the training data ----- | |
//Note that we have 450 training files for features: train/features/0.csv through train/features/449.csv | |
SequenceRecordReader trainFeatures = new CSVSequenceRecordReader(); | |
trainFeatures.initialize(new NumberedFileInputSplit(featuresDirTrain.getAbsolutePath() + "/%d.csv", 0, 449)); | |
SequenceRecordReader trainLabels = new CSVSequenceRecordReader(); | |
trainLabels.initialize(new NumberedFileInputSplit(labelsDirTrain.getAbsolutePath() + "/%d.csv", 0, 449)); | |
int miniBatchSize = 10; | |
int numLabelClasses = 6; | |
DataSetIterator trainData = new SequenceRecordReaderDataSetIterator(trainFeatures, trainLabels, miniBatchSize, numLabelClasses, | |
false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END); | |
//Normalize the training data | |
DataNormalization normalizer = new NormalizerStandardize(); | |
normalizer.fit(trainData); //Collect training data statistics | |
trainData.reset(); | |
//Use previously collected statistics to normalize on-the-fly. Each DataSet returned by 'trainData' iterator will be normalized | |
trainData.setPreProcessor(normalizer); | |
// ----- Load the test data ----- | |
//Same process as for the training data. | |
SequenceRecordReader testFeatures = new CSVSequenceRecordReader(); | |
testFeatures.initialize(new NumberedFileInputSplit(featuresDirTest.getAbsolutePath() + "/%d.csv", 0, 149)); | |
SequenceRecordReader testLabels = new CSVSequenceRecordReader(); | |
testLabels.initialize(new NumberedFileInputSplit(labelsDirTest.getAbsolutePath() + "/%d.csv", 0, 149)); | |
DataSetIterator testData = new SequenceRecordReaderDataSetIterator(testFeatures, testLabels, miniBatchSize, numLabelClasses, | |
false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END); | |
log.info(testData.next().toString()); | |
testData.setPreProcessor(normalizer); //Note that we are using the exact same normalization process as the training data | |
// ----- Configure the network ----- | |
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() | |
.seed(123) //Random number generator seed for improved repeatability. Optional. | |
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1) | |
.weightInit(WeightInit.XAVIER) | |
.updater(Updater.NESTEROVS).momentum(0.9) | |
.learningRate(0.005) | |
.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue) //Not always required, but helps with this data set | |
.gradientNormalizationThreshold(0.5) | |
.list() | |
.layer(0, new GravesLSTM.Builder().activation("tanh").nIn(1).nOut(10).build()) | |
.layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT) | |
.activation("softmax").nIn(10).nOut(numLabelClasses).build()) | |
.pretrain(false).backprop(true).build(); | |
MultiLayerNetwork net = new MultiLayerNetwork(conf); | |
net.init(); | |
net.setListeners(new ScoreIterationListener(20)); //Print the score (loss function value) every 20 iterations | |
// ----- Train the network, evaluating the test set performance at each epoch ----- | |
int nEpochs = 40; | |
String str = "Test set evaluation at epoch %d: Accuracy = %.2f, F1 = %.2f"; | |
for (int i = 0; i < nEpochs; i++) { | |
net.fit(trainData); | |
//Evaluate on the test set: | |
Evaluation evaluation = net.evaluate(testData); | |
log.info(String.format(str, i, evaluation.accuracy(), evaluation.f1())); | |
DataSet ds = testData.next(); | |
log.info(ds.toString()); | |
testData.reset(); | |
trainData.reset(); | |
} | |
log.info("----- Example Complete -----"); | |
} |
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