我尝试为java＆＃34; main＆＃34;运行deeplearning java.lang.UnsatisfiedLinkError：java.library.path中没有jniopenblas
Class Test{
protected static final String[] allowedExtensions =
BaseImageLoader.ALLOWED_FORMATS;
protected static int height = 20;
protected static int width = 20;
protected static int channels = 1;
protected static int outputNum = 2;
protected static final long seed = 123;
protected static double rate = 0.006;
protected static int epochs = 10;
public static final Random randNumGen = new Random();
private static Logger log = LoggerFactory.getLogger(Text2Saved.class);
public static void main(String[] args) {
File parentDir = new File("./data/text2_test");
String modelfile = "./data/text2-goodmodel.model";
System.out.println(modelfile);
ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();
BalancedPathFilter pathFilter = new BalancedPathFilter(randNumGen, allowedExtensions, labelMaker);
FileSplit filesInDir = new FileSplit(parentDir, allowedExtensions, randNumGen);
// Split the image files into train and test.
InputSplit[] filesInDirSplit = filesInDir.sample(pathFilter);
InputSplit testData = filesInDirSplit[0];
ImageRecordReader testReader = new ImageRecordReader(height, width, channels, labelMaker);
System.out.println("Number of records in Test: " + testData.length());
try {
testReader.initialize(testData);
MultiLayerNetwork model = ModelSerializer.restoreMultiLayerNetwork(new File(modelfile));
DataSetIterator testIter = new RecordReaderDataSetIterator(testReader, 20, 1, outputNum);
Evaluation eval = new Evaluation(outputNum);
while (testIter.hasNext()) {
DataSet next = testIter.next();
INDArray output = model.output(next.getFeatureMatrix(), false);
eval.eval(next.getLabels(), output);
}
System.out.println(eval.stats());
log.info(eval.stats());
} catch (IOException e) {
e.printStackTrace();
}
}
异常详情
线程中的异常＆＃34; main＆＃34; java.lang.UnsatisfiedLinkError：java.library.path中没有jniopenblas
at java.lang.ClassLoader.loadLibrary(ClassLoader.java:1867)
at java.lang.Runtime.loadLibrary0(Runtime.java:870)
at java.lang.System.loadLibrary(System.java:1122)
at org.bytedeco.javacpp.Loader.loadLibrary(Loader.java:945)
at org.bytedeco.javacpp.Loader.load(Loader.java:750)
at org.bytedeco.javacpp.Loader.load(Loader.java:657)
at org.bytedeco.javacpp.openblas.<clinit>(openblas.java:10)
at org.nd4j.linalg.cpu.nativecpu.blas.CpuBlas.setMaxThreads(CpuBlas.java:87)
at org.nd4j.nativeblas.Nd4jBlas.<init>(Nd4jBlas.java:36)
at org.nd4j.linalg.cpu.nativecpu.blas.CpuBlas.<init>(CpuBlas.java:11)
at org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory.createBlas(CpuNDArrayFactory.java:79)
at org.nd4j.linalg.factory.BaseNDArrayFactory.blas(BaseNDArrayFactory.java:71)
at org.nd4j.linalg.cpu.nativecpu.blas.CpuLevel3.<init>(CpuLevel3.java:26)
at org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory.createLevel3(CpuNDArrayFactory.java:94)
at org.nd4j.linalg.factory.BaseNDArrayFactory.level3(BaseNDArrayFactory.java:92)
at org.nd4j.linalg.factory.BaseBlasWrapper.level3(BaseBlasWrapper.java:42)
at org.nd4j.linalg.api.ndarray.BaseNDArray.mmuli(BaseNDArray.java:2849)
at org.nd4j.linalg.api.ndarray.BaseNDArray.mmul(BaseNDArray.java:2643)
at org.deeplearning4j.nn.layers.BaseLayer.preOutput(BaseLayer.java:373)
at org.deeplearning4j.nn.layers.BaseLayer.activate(BaseLayer.java:384)
at org.deeplearning4j.nn.layers.BaseLayer.activate(BaseLayer.java:405)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.activationFromPrevLayer(MultiLayerNetwork.java:590)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.feedForwardToLayer(MultiLayerNetwork.java:713)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.feedForward(MultiLayerNetwork.java:667)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.feedForward(MultiLayerNetwork.java:658)
at org.deeplearning4j.nn.multilayer.MultiLayerNetwork.output(MultiLayerNetwork.java:1541)
at org.woolfel.robottag.Text2Saved.main(Text2Saved.java:60)