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我无法在 nd4j 中使用简单的卷积，并且有关此特定主题的文档很少。我正在尝试做的事情：

INDArray values = Nd4j.create(new double[]{1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
INDArray kernel = Nd4j.create(new double[]{0.5,0.5});

INDArray conv = Nd4j.getConvolution().convn(values, kernel, Convolution.Type.VALID);

无论值或卷积类型如何，我总是得到相同的异常（见下文）。当 nd4j 试图将值数组转换为复杂数组以执行我认为的傅立叶变换时，似乎会发生错误。

我尝试了几个版本的 nd4j（0.9.1 - 0.8.0 - 0.7.0），但无济于事。任何人都可以帮忙吗？

Exception in thread "main" java.lang.UnsupportedOperationException
at org.nd4j.linalg.api.complex.BaseComplexNDArray.putScalar(BaseComplexNDArray.java:1947)
at org.nd4j.linalg.api.complex.BaseComplexNDArray.putScalar(BaseComplexNDArray.java:1804)
at org.nd4j.linalg.api.complex.BaseComplexNDArray.copyFromReal(BaseComplexNDArray.java:545)
at org.nd4j.linalg.api.complex.BaseComplexNDArray.<init>(BaseComplexNDArray.java:159)
at org.nd4j.linalg.api.complex.BaseComplexNDArray.<init>(BaseComplexNDArray.java:167)
at org.nd4j.linalg.cpu.nativecpu.complex.ComplexNDArray.<init>(ComplexNDArray.java:104)
at org.nd4j.linalg.cpu.nativecpu.CpuNDArrayFactory.createComplex(CpuNDArrayFactory.java:166)
at org.nd4j.linalg.factory.Nd4j.createComplex(Nd4j.java:3345)
at org.nd4j.linalg.convolution.DefaultConvolutionInstance.convn(DefaultConvolutionInstance.java:116)
at org.nd4j.linalg.convolution.BaseConvolution.convn(BaseConvolution.java:66)
at com.example.demo.Main.testing(Main.java:41)
at com.example.demo.Main.main(Main.java:34)
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1 回答 1

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这有点棘手，因为 ND4j 目前不支持数学卷积。您必须制定自己的实现。

    double[] rawData = {12,10,15,12,10,11,15,12,12};
    INDArray data = Nd4j.create(rawData);
    double[] rawFilter = {1.0 / 2, 0, 1.0 / 2};
    INDArray filter = Nd4j.create(rawFilter);        
    Nd4jConv1d convolution = new Nd4jConv1d(1, 1, (int) filter.shape()[1], 1, 0);
    INDArray output = convolution.forward(data, filter);

如中所示：https ://github.com/deeplearning4j/deeplearning4j/blob/af7155d61dc810d3e7139f15f98810e0255b2e17/arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/MNISTOptimizationTest.java

注意：你需要一个额外的类 Nd4jConv1d。去 repo 获取它

于 2018-12-18T09:10:54.020 回答