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gistfile1.txt
.5/target/classes/ with scanner TypeAnnotationsScanner
16:51:56.900 [main] INFO org.reflections.Reflections - Reflections took 29 ms to scan 7 urls, producing 146 keys and 579 values
Tests run: 6, Failures: 3, Errors: 0, Skipped: 2, Time elapsed: 3.912 sec <<< FAILURE! - in org.nd4j.linalg.convolution.ConvolutionTestsC
testMoreIm2Col2[0: backend(org.nd4j.linalg.jcublas.JCublasBackend)={1}](org.nd4j.linalg.convolution.ConvolutionTestsC) Time elapsed: 3.647 sec <<< FAILURE!
java.lang.AssertionError:
expected:<[[[[[[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]
[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]]
[[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]
[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]]]
[[[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]
[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]]
[[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]
[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]]]]]]> but was:<[[[[[[1.00,1.00,1.00,1.00]
[1.00,1.00,1.00,1.00]
[2.00,2.00,2.00,2.00]
[2.00,2.00,2.00,2.00]]
[[3.00,3.00,3.00,3.00]
[3.00,3.00,3.00,3.00]
[4.00,4.00,4.00,4.00]
[4.00,4.00,4.00,4.00]]]
[[[1.00,1.00,1.00,1.00]
[1.00,1.00,1.00,1.00]
[2.00,2.00,2.00,2.00]
[2.00,2.00,2.00,2.00]]
[[3.00,3.00,3.00,3.00]
[3.00,3.00,3.00,3.00]
[4.00,4.00,4.00,4.00]
[4.00,4.00,4.00,4.00]]]]]]>
at org.nd4j.linalg.convolution.ConvolutionTestsC.testMoreIm2Col2(ConvolutionTestsC.java:114)
testIm2Col[0: backend(org.nd4j.linalg.jcublas.JCublasBackend)={1}](org.nd4j.linalg.convolution.ConvolutionTestsC) Time elapsed: 0.112 sec <<< FAILURE!
java.lang.AssertionError:
expected:<[[[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,1.00,2.00,0.00,0.00]
[0.00,0.00,3.00,4.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]
[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,5.00,6.00,0.00,0.00]
[0.00,0.00,7.00,8.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]]
[[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,9.00,10.00,0.00,0.00]
[0.00,0.00,11.00,12.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]
[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,13.00,14.00,0.00,0.00]
[0.00,0.00,15.00,16.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]]]> but was:<[[[[[[1.00,2.00,3.00,4.00,5.00,6.00]
[7.00,8.00,9.00,10.00,11.00,12.00]
[13.00,14.00,15.00,16.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]
[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]]
[[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]
[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]]]>
at org.nd4j.linalg.convolution.ConvolutionTestsC.testIm2Col(ConvolutionTestsC.java:60)
testIm2Col2[0: backend(org.nd4j.linalg.jcublas.JCublasBackend)={1}](org.nd4j.linalg.convolution.ConvolutionTestsC) Time elapsed: 0.09 sec <<< FAILURE!
java.lang.AssertionError:
expected:<[[[[[[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]
[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]]
[[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]
[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]]]
[[[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]
[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]]
[[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]
[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]]]]]]> but was:<[[[[[[1.00,1.00,1.00,1.00]
[1.00,1.00,1.00,1.00]
[2.00,2.00,2.00,2.00]
[2.00,2.00,2.00,2.00]]
[[3.00,3.00,3.00,3.00]
[3.00,3.00,3.00,3.00]
[4.00,4.00,4.00,4.00]
[4.00,4.00,4.00,4.00]]]
[[[1.00,1.00,1.00,1.00]
[1.00,1.00,1.00,1.00]
[2.00,2.00,2.00,2.00]
[2.00,2.00,2.00,2.00]]
[[3.00,3.00,3.00,3.00]
[3.00,3.00,3.00,3.00]
[4.00,4.00,4.00,4.00]
[4.00,4.00,4.00,4.00]]]]]]>
at org.nd4j.linalg.convolution.ConvolutionTestsC.testIm2Col2(ConvolutionTestsC.java:91)
Results :
Failed tests:
NDArrayTestsFortran.testPutRow:526 expected: org.nd4j.linalg.jcublas.buffer.CudaFloatDataBuffer<[1.0,2.0,3.0,4.0]> but was: org.nd4j.linalg.jcublas.buffer.CudaFloatDataBuffer<[1.0,2.0,3.0,4.0]>
Nd4jTestsC.testArgMax:112 expected:<[[2.00,2.00]
[2.00,2.00]
[2.00,2.00]
[2.00,2.00]]> but was:<[[[1.00,2.00]
[3.00,4.00]
[5.00,6.00]]
[[7.00,8.00]
[9.00,10.00]
[11.00,12.00]]
[[13.00,14.00]
[15.00,16.00]
[17.00,18.00]]
[[19.00,20.00]
[21.00,22.00]
[23.00,24.00]]]>
Nd4jTestsC.testAssign:2574 expected:<[[1.00,2.00,3.00,4.00,5.00,6.00,7.00,8.00]
[9.00,10.00,11.00,12.00,13.00,14.00,15.00,16.00]
[17.00,18.00,19.00,20.00,21.00,22.00,23.00,24.00]
[25.00,26.00,27.00,28.00,29.00,30.00,31.00,32.00]
[33.00,34.00,35.00,36.00,37.00,38.00,39.00,40.00]
[41.00,42.00,43.00,44.00,45.00,46.00,47.00,48.00]
[49.00,50.00,51.00,52.00,53.00,54.00,55.00,56.00]
[57.00,58.00,59.00,60.00,61.00,62.00,63.00,64.00]
[65.00,66.00,67.00,68.00,69.00,70.00,71.00,72.00]
[73.00,74.00,75.00,76.00,77.00,78.00,79.00,80.00]
[81.00,82.00,83.00,84.00,85.00,86.00,87.00,88.00]
[89.00,90.00,91.00,92.00,93.00,94.00,95.00,96.00]]> but was:<[[1.00,5.00,3.00,7.00,2.00,6.00,4.00,8.00]
[33.00,37.00,35.00,39.00,34.00,38.00,36.00,40.00]
[65.00,69.00,67.00,71.00,66.00,70.00,68.00,72.00]
[17.00,21.00,19.00,23.00,18.00,22.00,20.00,24.00]
[49.00,53.00,51.00,55.00,50.00,54.00,52.00,56.00]
[81.00,85.00,83.00,87.00,82.00,86.00,84.00,88.00]
[9.00,13.00,11.00,15.00,10.00,14.00,12.00,16.00]
[41.00,45.00,43.00,47.00,42.00,46.00,44.00,48.00]
[73.00,77.00,75.00,79.00,74.00,78.00,76.00,80.00]
[25.00,29.00,27.00,31.00,26.00,30.00,28.00,32.00]
[57.00,61.00,59.00,63.00,58.00,62.00,60.00,64.00]
[89.00,93.00,91.00,95.00,90.00,94.00,92.00,96.00]]>
Nd4jTestsC.testBroadcast1d:732 expected:<[[[2.00,3.00]
[4.00,5.00]
[6.00,7.00]]
[[9.00,10.00]
[11.00,12.00]
[13.00,14.00]]
[[16.00,17.00]
[18.00,19.00]
[20.00,21.00]]
[[23.00,24.00]
[25.00,26.00]
[27.00,28.00]]]> but was:<[[[2.00,4.00]
[6.00,8.00]
[6.00,8.00]]
[[10.00,12.00]
[10.00,12.00]
[14.00,16.00]]
[[14.00,16.00]
[18.00,20.00]
[18.00,20.00]]
[[22.00,24.00]
[22.00,24.00]
[26.00,28.00]]]>
Nd4jTestsC.testMuliRowVector:1824 expected:<[[1.00,4.00]
[3.00,8.00]
[5.00,12.00]]> but was:<[[1.00,4.00]
[6.00,4.00]
[5.00,12.00]]>
Nd4jTestsC.testMultiSum:618 expected:<[ 10.00, 18.00]> but was:<[ 12.00, 16.00]>
Nd4jTestsC.testPutRow:1083 expected: org.nd4j.linalg.jcublas.buffer.CudaFloatDataBuffer<[1.0,2.0,3.0,4.0]> but was: org.nd4j.linalg.jcublas.buffer.CudaFloatDataBuffer<[1.0,2.0,3.0,4.0]>
Nd4jTestsC.testSignXZ:1888 expected:<[[1.00,-1.00,-1.00]
[-1.00,-1.00,-1.00]
[1.00,1.00,-1.00]
[1.00,-1.00,-1.00]]> but was:<[[1.00,-1.00,1.00]
[1.00,-1.00,-1.00]
[1.00,-1.00,-1.00]
[-1.00,-1.00,-1.00]]>
Nd4jTestsC.testStdev0:1847 expected:<[ 0.20, 0.25, 0.05]> but was:<[ 1.86, 1.76, 1.75]>
Nd4jTestsC.testSum2dv2:657 expected:<[ 14.00, 22.00]> but was:<[ 16.00, 20.00]>
Nd4jTestsC.testSum3Of4_2222:680 expected:<[ 60.00, 76.00]> but was:<[ 64.00, 72.00]>
Nd4jTestsC.testSum3Of4_3322:754 expected:<[ 315.00, 351.00]> but was:<[ 324.00, 342.00]>
Nd4jTestsC.testSumDifferentOrdersSquareMatrix:2553 expected:<[ 4.00, 6.00]> but was:<[ 4.00, 5.00]>
Nd4jTestsC.testSumDifferentOrders:2587 expected:<[ 9.00, 12.00]> but was:<[ 9.00, 10.00]>
Nd4jTestsC.testTanhXZ:1922 expected:<[[-1.00,-1.00,-1.00]
[-0.99,-0.93,-0.50]
[0.50,0.93,0.99]
[1.00,1.00,1.00]]> but was:<[[-1.00,-0.99,0.50]
[1.00,-1.00,-0.93]
[0.93,1.00,-1.00]
[-0.50,0.99,1.00]]>
Nd4jTestsC.testTensorAlongDimension:151 I 0 failed expected:<1350.0> but was:<210.0>
Nd4jTestsC.testToFlattened2:469 expected:<[ 1.00, 2.00, 3.00, 4.00, 5.00, 6.00, 7.00, 8.00, 9.00, 10.00, 11.00, 12.00, 1.10, 2.10, 3.10, 4.10, 5.10, 6.10, 7.10, 8.10, 9.10, 10.10, 11.10, 12.10]> but was:<[ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]>
Nd4jTestsC.testToFlattenedOnViews:505 expected:<[ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 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218.30, 222.30, 195.30, 199.30, 203.30, 207.30, 211.30, 215.30, 219.30, 223.30, 196.30, 200.30, 204.30, 208.30, 212.30, 216.30, 220.30, 224.30, 225.30, 229.30, 233.30, 237.30, 241.30, 245.30, 249.30, 253.30, 226.30, 230.30, 234.30, 238.30, 242.30, 246.30, 250.30, 254.30, 227.30, 231.30, 235.30, 239.30, 243.30, 247.30, 251.30, 255.30, 228.30, 232.30, 236.30, 240.30, 244.30, 248.30, 252.30, 256.30]>
Nd4jTestsC.testToFlattenedOrder:416 expected:<[ 1.00, 2.00, 3.00, 4.00, 1.00, 2.00, 3.00, 4.00]> but was:<[ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]>
Nd4jTestsC.testToFlattenedWithOrder:2350 Values should be different. Actual: [ 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00]
Nd4jTestsComparisonFortran.testGemvApacheCommons:198 7,2 - getTensorAlongDimensionMatricesWithShape(4,1,12345).get(2).mmul(getSubMatricesWithShape(1,1,12345).get(0)) expected:<5.0> but was:<0.0>
ConvolutionTestsC.testIm2Col2:91 expected:<[[[[[[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]
[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]]
[[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]
[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]]]
[[[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]
[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]]
[[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]
[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]]]]]]> but was:<[[[[[[1.00,1.00,1.00,1.00]
[1.00,1.00,1.00,1.00]
[2.00,2.00,2.00,2.00]
[2.00,2.00,2.00,2.00]]
[[3.00,3.00,3.00,3.00]
[3.00,3.00,3.00,3.00]
[4.00,4.00,4.00,4.00]
[4.00,4.00,4.00,4.00]]]
[[[1.00,1.00,1.00,1.00]
[1.00,1.00,1.00,1.00]
[2.00,2.00,2.00,2.00]
[2.00,2.00,2.00,2.00]]
[[3.00,3.00,3.00,3.00]
[3.00,3.00,3.00,3.00]
[4.00,4.00,4.00,4.00]
[4.00,4.00,4.00,4.00]]]]]]>
ConvolutionTestsC.testIm2Col:60 expected:<[[[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,1.00,2.00,0.00,0.00]
[0.00,0.00,3.00,4.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]
[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,5.00,6.00,0.00,0.00]
[0.00,0.00,7.00,8.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]]
[[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,9.00,10.00,0.00,0.00]
[0.00,0.00,11.00,12.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]
[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,13.00,14.00,0.00,0.00]
[0.00,0.00,15.00,16.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]]]> but was:<[[[[[[1.00,2.00,3.00,4.00,5.00,6.00]
[7.00,8.00,9.00,10.00,11.00,12.00]
[13.00,14.00,15.00,16.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]
[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]]
[[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]
[[[[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]
[0.00,0.00,0.00,0.00,0.00,0.00]]]]]]>
ConvolutionTestsC.testMoreIm2Col2:114 expected:<[[[[[[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]
[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]]
[[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]
[1.00,1.00,1.00,1.00]
[3.00,3.00,3.00,3.00]]]
[[[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]
[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]]
[[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]
[2.00,2.00,2.00,2.00]
[4.00,4.00,4.00,4.00]]]]]]> but was:<[[[[[[1.00,1.00,1.00,1.00]
[1.00,1.00,1.00,1.00]
[2.00,2.00,2.00,2.00]
[2.00,2.00,2.00,2.00]]
[[3.00,3.00,3.00,3.00]
[3.00,3.00,3.00,3.00]
[4.00,4.00,4.00,4.00]
[4.00,4.00,4.00,4.00]]]
[[[1.00,1.00,1.00,1.00]
[1.00,1.00,1.00,1.00]
[2.00,2.00,2.00,2.00]
[2.00,2.00,2.00,2.00]]
[[3.00,3.00,3.00,3.00]
[3.00,3.00,3.00,3.00]
[4.00,4.00,4.00,4.00]
[4.00,4.00,4.00,4.00]]]]]]>
OpExecutionerTests.testAddBroadcast:381 expected:<[[2.00,5.00,8.00]
[3.00,6.00,9.00]]> but was:<[[2.00,6.00,7.00]
[4.00,5.00,9.00]]>
OpExecutionerTests.testClassificationSoftmax:370 expected:<[[0.30,0.28,0.42]
[0.31,0.29,0.40]
[0.31,0.28,0.41]
[0.31,0.29,0.40]
[0.30,0.28,0.42]
[0.31,0.28,0.41]
[0.31,0.28,0.41]
[0.31,0.28,0.41]
[0.31,0.30,0.39]
[0.31,0.29,0.39]
[0.30,0.28,0.42]
[0.31,0.29,0.40]
[0.31,0.29,0.39]
[0.31,0.29,0.40]
[0.30,0.27,0.43]
[0.31,0.28,0.41]
[0.30,0.27,0.43]
[0.30,0.28,0.42]
[0.31,0.28,0.42]
[0.31,0.28,0.41]
[0.31,0.29,0.41]
[0.30,0.28,0.42]
[0.30,0.27,0.43]
[0.31,0.28,0.41]
[0.32,0.30,0.39]
[0.31,0.29,0.39]
[0.31,0.28,0.41]
[0.31,0.28,0.41]
[0.30,0.28,0.42]
[0.31,0.29,0.40]
[0.31,0.29,0.39]
[0.30,0.28,0.42]
[0.31,0.28,0.41]
[0.30,0.28,0.42]
[0.31,0.29,0.39]
[0.30,0.28,0.42]
[0.30,0.27,0.43]
[0.31,0.29,0.39]
[0.31,0.29,0.40]
[0.31,0.28,0.41]
[0.30,0.28,0.42]
[0.32,0.30,0.38]
[0.31,0.29,0.40]
[0.30,0.28,0.42]
[0.31,0.29,0.40]
[0.31,0.29,0.40]
[0.31,0.28,0.41]
[0.31,0.29,0.40]
[0.30,0.28,0.42]
[0.31,0.28,0.41]
[0.34,0.35,0.30]
[0.34,0.35,0.31]
[0.34,0.36,0.30]
[0.33,0.35,0.32]
[0.34,0.36,0.31]
[0.34,0.36,0.29]
[0.34,0.36,0.30]
[0.33,0.33,0.34]
[0.34,0.36,0.30]
[0.34,0.34,0.32]
[0.33,0.35,0.32]
[0.34,0.35,0.32]
[0.33,0.35,0.32]
[0.34,0.36,0.29]
[0.33,0.33,0.34]
[0.34,0.35,0.32]
[0.34,0.36,0.30]
[0.34,0.35,0.31]
[0.34,0.35,0.31]
[0.33,0.35,0.32]
[0.34,0.36,0.30]
[0.33,0.34,0.33]
[0.34,0.37,0.29]
[0.34,0.37,0.29]
[0.34,0.35,0.31]
[0.34,0.35,0.31]
[0.34,0.36,0.30]
[0.34,0.36,0.29]
[0.34,0.36,0.30]
[0.33,0.33,0.34]
[0.33,0.34,0.32]
[0.33,0.34,0.32]
[0.33,0.34,0.33]
[0.35,0.37,0.28]
[0.35,0.36,0.29]
[0.34,0.35,0.31]
[0.34,0.36,0.30]
[0.33,0.35,0.31]
[0.34,0.35,0.31]
[0.33,0.35,0.32]
[0.34,0.36,0.30]
[0.34,0.36,0.30]
[0.33,0.35,0.32]
[0.33,0.34,0.33]
[0.34,0.35,0.31]
[0.34,0.35,0.31]
[0.34,0.35,0.31]
[0.34,0.35,0.31]
[0.32,0.32,0.36]
[0.34,0.35,0.31]
[0.34,0.36,0.29]
[0.35,0.37,0.28]
[0.35,0.37,0.28]
[0.35,0.38,0.27]
[0.35,0.37,0.28]
[0.35,0.39,0.26]
[0.34,0.36,0.29]
[0.36,0.39,0.25]
[0.35,0.38,0.26]
[0.34,0.35,0.31]
[0.34,0.36,0.30]
[0.35,0.37,0.28]
[0.35,0.37,0.29]
[0.35,0.37,0.29]
[0.34,0.36,0.30]
[0.34,0.36,0.30]
[0.35,0.38,0.27]
[0.35,0.37,0.29]
[0.36,0.39,0.25]
[0.34,0.37,0.29]
[0.34,0.36,0.29]
[0.35,0.37,0.29]
[0.36,0.39,0.25]
[0.34,0.36,0.29]
[0.35,0.37,0.29]
[0.35,0.38,0.27]
[0.34,0.36,0.30]
[0.34,0.36,0.29]
[0.35,0.38,0.28]
[0.35,0.38,0.27]
[0.35,0.38,0.26]
[0.35,0.36,0.29]
[0.35,0.37,0.28]
[0.35,0.37,0.28]
[0.35,0.39,0.26]
[0.35,0.37,0.29]
[0.34,0.36,0.30]
[0.35,0.38,0.27]
[0.34,0.36,0.29]
[0.34,0.36,0.29]
[0.34,0.36,0.30]
[0.34,0.35,0.31]
[0.35,0.37,0.28]
[0.35,0.37,0.29]
[0.34,0.36,0.30]
[0.34,0.36,0.30]
[0.34,0.36,0.29]
[0.34,0.36,0.29]
[0.34,0.36,0.30]
[0.35,0.37,0.28]]> but was:<[[0.64,0.60,0.80]
[0.63,0.60,0.76]
[0.63,0.60,0.78]
[0.63,0.60,0.75]
[0.64,0.60,0.80]
[0.64,0.60,0.79]
[0.64,0.60,0.78]
[0.64,0.60,0.78]
[0.63,0.60,0.74]
[0.63,0.60,0.75]
[0.64,0.60,0.80]
[0.64,0.60,0.76]
[0.63,0.60,0.75]
[0.63,0.60,0.76]
[0.64,0.60,0.84]
[0.65,0.61,0.80]
[0.64,0.60,0.83]
[0.64,0.60,0.80]
[0.64,0.60,0.80]
[0.64,0.60,0.80]
[0.64,0.60,0.78]
[0.64,0.60,0.80]
[0.64,0.59,0.82]
[0.64,0.60,0.79]
[0.64,0.61,0.74]
[0.63,0.60,0.75]
[0.64,0.60,0.79]
[0.64,0.60,0.79]
[0.64,0.60,0.79]
[0.64,0.60,0.75]
[0.63,0.60,0.75]
[0.64,0.60,0.81]
[0.64,0.60,0.79]
[0.64,0.60,0.81]
[0.63,0.60,0.75]
[0.64,0.60,0.79]
[0.64,0.59,0.82]
[0.63,0.60,0.75]
[0.63,0.60,0.75]
[0.64,0.60,0.78]
[0.64,0.60,0.81]
[0.64,0.61,0.72]
[0.63,0.60,0.77]
[0.64,0.60,0.80]
[0.64,0.61,0.77]
[0.63,0.60,0.77]
[0.64,0.60,0.78]
[0.63,0.60,0.77]
[0.64,0.60,0.80]
[0.64,0.60,0.78]
[0.73,0.75,0.67]
[0.72,0.74,0.67]
[0.74,0.76,0.66]
[0.72,0.74,0.70]
[0.73,0.76,0.68]
[0.72,0.75,0.65]
[0.73,0.75,0.66]
[0.69,0.70,0.70]
[0.73,0.75,0.67]
[0.71,0.72,0.69]
[0.71,0.73,0.69]
[0.72,0.73,0.68]
[0.72,0.74,0.69]
[0.73,0.76,0.65]
[0.69,0.69,0.71]
[0.72,0.74,0.69]
[0.73,0.75,0.65]
[0.71,0.73,0.67]
[0.73,0.76,0.69]
[0.71,0.73,0.69]
[0.74,0.76,0.66]
[0.71,0.73,0.70]
[0.74,0.77,0.65]
[0.73,0.76,0.64]
[0.72,0.74,0.69]
[0.73,0.74,0.69]
[0.74,0.76,0.67]
[0.74,0.77,0.66]
[0.73,0.75,0.67]
[0.69,0.70,0.71]
[0.71,0.73,0.70]
[0.71,0.72,0.69]
[0.71,0.72,0.70]
[0.74,0.78,0.63]
[0.72,0.75,0.64]
[0.72,0.73,0.66]
[0.73,0.76,0.67]
[0.73,0.75,0.69]
[0.71,0.72,0.67]
[0.72,0.74,0.69]
[0.72,0.75,0.65]
[0.73,0.75,0.65]
[0.71,0.73,0.69]
[0.69,0.70,0.70]
[0.72,0.74,0.67]
[0.71,0.72,0.66]
[0.71,0.73,0.67]
[0.72,0.74,0.68]
[0.68,0.68,0.73]
[0.71,0.73,0.68]
[0.76,0.79,0.67]
[0.75,0.78,0.64]
[0.75,0.79,0.64]
[0.75,0.79,0.61]
[0.75,0.79,0.64]
[0.76,0.80,0.60]
[0.73,0.76,0.65]
[0.75,0.80,0.59]
[0.75,0.79,0.60]
[0.76,0.78,0.71]
[0.75,0.77,0.68]
[0.75,0.78,0.64]
[0.75,0.78,0.66]
[0.75,0.78,0.65]
[0.75,0.78,0.68]
[0.75,0.78,0.69]
[0.75,0.79,0.63]
[0.76,0.79,0.66]
[0.75,0.80,0.58]
[0.73,0.77,0.64]
[0.76,0.78,0.68]
[0.75,0.78,0.66]
[0.75,0.80,0.58]
[0.74,0.77,0.67]
[0.75,0.78,0.66]
[0.75,0.79,0.62]
[0.74,0.77,0.67]
[0.74,0.77,0.66]
[0.75,0.79,0.63]
[0.75,0.79,0.61]
[0.75,0.80,0.61]
[0.76,0.78,0.67]
[0.75,0.79,0.64]
[0.74,0.78,0.63]
[0.74,0.79,0.59]
[0.76,0.79,0.66]
[0.75,0.78,0.69]
[0.75,0.79,0.62]
[0.74,0.77,0.66]
[0.75,0.78,0.67]
[0.76,0.78,0.68]
[0.75,0.77,0.71]
[0.75,0.78,0.64]
[0.76,0.79,0.66]
[0.76,0.78,0.70]
[0.75,0.78,0.70]
[0.74,0.78,0.66]
[0.75,0.78,0.66]
[0.75,0.77,0.69]
[0.74,0.78,0.64]]>
OpExecutionerTests.testDescriptiveStatsDouble:219 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering f expected:<2.5> but was:<-1.7194769596666185E-24>
OpExecutionerTests.testDescriptiveStats:250 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering f expected:<2.5> but was:<-1.82003259257389056E17>
OpExecutionerTests.testDimensionMax:300 expected:<5.0> but was:<3.0>
OpExecutionerTests.testIamax2:233 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering f expected:<3> but was:<4>
OpExecutionerTests.testIamax:227 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering f expected:<3> but was:<4>
OpExecutionerTests.testOtherSoftmax:339 expected:<[[0.00,0.00,0.00,0.00,0.05,0.95]
[0.00,0.00,0.00,0.00,0.05,0.95]
[0.00,0.00,0.00,0.00,0.05,0.95]]> but was:<[[1.31,4.02,7.00,10.00,13.00,16.00]
[2.13,5.01,8.00,11.00,14.00,17.00]
[3.05,6.00,9.00,12.00,15.00,18.00]]>
OpExecutionerTests.testRowSoftmax:259 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering f expected:<1.0> but was:<21.5161190032959>
OpExecutionerTests.testSoftMax:409 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering f expected:<1.0> but was:<21.5161190032959>
OpExecutionerTests.testSoftmax:325 expected:<[[0.02,0.12,0.87]
[0.02,0.12,0.87]]> but was:<[[1.31,3.05,5.01]
[2.13,4.02,6.00]]>
OpExecutionerTests.testStdev:502 expected:<0.370035856962204> but was:<-1.44117524538064896E17>
OpExecutionerTests.testVariance:516 expected:<-1.44117524538064896E17> but was:<-1.687272502071876E-24>
OpExecutionerTestsC.testColumnStd:396 expected:<[ 173.78, 173.78, 173.78, 173.78]> but was:<[ 43.45, 43.45, 43.45, 43.45]>
OpExecutionerTestsC.testColumnVar:386 expected:<[ 30,200.00, 30,200.00, 30,200.00, 30,200.00]> but was:<[ 1,887.50, 1,887.50, 1,887.50, 1,887.50]>
OpExecutionerTestsC.testDescriptiveStatsDouble:228 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering c expected:<2.5> but was:<-1.44117524538064896E17>
OpExecutionerTestsC.testDescriptiveStats:245 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering c expected:<2.5> but was:<-2.21094196159709184E17>
OpExecutionerTestsC.testLogSoftmaxVector:580 expected:<[ -3.44, -2.44, -1.44, -0.44]> but was:<[ 1.31, 2.13, 3.05, 4.02]>
OpExecutionerTestsC.testLogSoftmax:606 expected:<[[-1.09,-1.10,-1.10]
[-1.11,-1.10,-1.09]
[-1.10,-1.10,-1.10]
[-1.10,-1.09,-1.10]
[-1.10,-1.11,-1.09]
[-1.08,-1.10,-1.11]
[-1.10,-1.10,-1.11]
[-1.09,-1.10,-1.10]
[-1.10,-1.10,-1.09]
[-1.10,-1.10,-1.10]
[-1.11,-1.10,-1.09]
[-1.08,-1.11,-1.10]
[-1.09,-1.10,-1.10]
[-1.10,-1.10,-1.10]
[-1.11,-1.10,-1.09]
[-1.10,-1.09,-1.10]
[-1.16,-1.17,-0.98]
[-1.10,-1.08,-1.11]
[-1.09,-1.11,-1.10]
[-1.14,-1.06,-1.10]
[-1.11,-1.09,-1.09]
[-1.07,-1.14,-1.08]
[-1.09,-1.12,-1.08]
[-1.12,-1.07,-1.11]
[-1.08,-1.10,-1.11]
[-1.12,-1.10,-1.08]
[-1.06,-1.13,-1.10]
[-1.12,-1.12,-1.06]
[-1.10,-1.11,-1.08]
[-1.08,-1.12,-1.10]
[-1.10,-1.09,-1.11]
[-1.12,-1.08,-1.10]
[-1.08,-1.07,-1.15]
[-1.15,-1.06,-1.09]
[-1.10,-1.11,-1.09]
[-1.08,-1.12,-1.09]
[-1.11,-1.09,-1.10]
[-1.10,-1.09,-1.11]
[-1.10,-1.10,-1.10]
[-1.08,-1.09,-1.13]
[-1.09,-1.11,-1.10]
[-1.11,-1.09,-1.09]
[-1.11,-1.10,-1.09]
[-1.10,-1.10,-1.09]
[-1.08,-1.11,-1.11]
[-1.09,-1.10,-1.11]
[-1.12,-1.09,-1.09]
[-1.10,-1.11,-1.09]
[-1.09,-1.09,-1.11]
[-1.10,-1.09,-1.11]
[-1.10,-1.09,-1.10]
[-1.10,-1.11,-1.09]
[-1.10,-1.10,-1.09]
[-1.10,-1.10,-1.10]
[-1.09,-1.10,-1.11]
[-1.08,-1.11,-1.11]
[-1.10,-1.10,-1.10]
[-1.10,-1.11,-1.09]
[-1.09,-1.10,-1.11]
[-1.10,-1.10,-1.09]
[-1.09,-1.11,-1.09]
[-1.09,-1.09,-1.11]
[-1.11,-1.09,-1.09]
[-1.11,-1.11,-1.08]
[-1.10,-1.10,-1.09]
[-1.10,-1.10,-1.10]
[-1.21,-1.21,-0.90]
[-1.12,-1.07,-1.11]
[-1.09,-1.10,-1.11]
[-1.15,-1.05,-1.10]
[-1.11,-1.11,-1.08]
[-1.04,-1.17,-1.08]
[-1.09,-1.13,-1.07]
[-1.12,-1.06,-1.12]
[-1.07,-1.09,-1.13]
[-1.13,-1.09,-1.08]
[-1.05,-1.15,-1.10]
[-1.14,-1.14,-1.03]
[-1.09,-1.13,-1.08]
[-1.07,-1.13,-1.10]
[-1.09,-1.08,-1.12]
[-1.13,-1.07,-1.10]
[-1.07,-1.05,-1.18]
[-1.17,-1.06,-1.07]
[-1.10,-1.10,-1.10]
[-1.08,-1.14,-1.08]
[-1.08,-1.10,-1.12]
[-1.10,-1.10,-1.10]
[-1.10,-1.11,-1.09]
[-1.10,-1.07,-1.13]
[-1.11,-1.12,-1.07]
[-1.12,-1.10,-1.08]
[-1.11,-1.11,-1.07]
[-1.10,-1.09,-1.11]
[-1.09,-1.11,-1.09]
[-1.09,-1.11,-1.10]
[-1.12,-1.09,-1.09]
[-1.11,-1.10,-1.09]
[-1.09,-1.10,-1.10]
[-1.09,-1.11,-1.10]
[-1.07,-1.13,-1.10]
[-1.15,-1.07,-1.08]
[-1.07,-1.08,-1.15]
[-1.14,-1.05,-1.11]
[-1.16,-1.15,-1.00]
[-1.12,-1.07,-1.11]
[-1.08,-1.09,-1.13]
[-1.10,-1.07,-1.13]
[-1.14,-1.12,-1.04]
[-1.08,-1.07,-1.15]
[-1.16,-1.05,-1.09]
[-1.06,-1.16,-1.08]
[-1.02,-1.14,-1.13]
[-1.08,-1.03,-1.20]
[-1.12,-1.06,-1.12]
[-1.10,-1.08,-1.11]
[-1.02,-1.04,-1.25]
[-1.11,-1.13,-1.06]
[-1.07,-1.13,-1.10]
[-1.07,-1.13,-1.09]
[-1.10,-1.11,-1.09]
[-1.17,-1.04,-1.09]
[-1.14,-1.10,-1.06]
[-1.09,-1.15,-1.06]
[-1.11,-1.14,-1.05]
[-1.07,-1.11,-1.12]
[-1.15,-1.06,-1.09]
[-1.06,-1.05,-1.19]
[-1.14,-1.09,-1.07]
[-1.08,-1.13,-1.08]
[-1.13,-1.13,-1.04]
[-1.05,-1.11,-1.14]
[-1.15,-1.13,-1.02]
[-1.06,-1.08,-1.15]
[-1.08,-1.14,-1.08]
[-1.13,-1.02,-1.16]
[-1.23,-1.01,-1.07]
[-1.12,-1.08,-1.10]
[-1.07,-1.05,-1.18]
[-1.03,-1.21,-1.07]
[-1.02,-1.06,-1.23]
[-1.06,-1.08,-1.16]
[-1.06,-1.09,-1.14]
[-1.14,-1.14,-1.02]
[-1.06,-1.08,-1.17]
[-1.10,-1.03,-1.17]
[-1.12,-1.10,-1.08]
[-1.01,-1.17,-1.12]
[-1.07,-1.08,-1.15]
[-1.10,-1.05,-1.16]]> but was:<[[0.64,0.63,0.63]
[0.63,0.64,0.64]
[0.64,0.64,0.63]
[0.63,0.64,0.64]
[0.63,0.63,0.64]
[0.65,0.64,0.64]
[0.64,0.64,0.64]
[0.64,0.64,0.64]
[0.64,0.63,0.64]
[0.64,0.64,0.64]
[0.63,0.64,0.64]
[0.64,0.63,0.64]
[0.64,0.63,0.63]
[0.64,0.64,0.64]
[0.63,0.64,0.64]
[0.63,0.64,0.63]
[0.64,0.64,0.73]
[0.72,0.74,0.72]
[0.73,0.72,0.73]
[0.69,0.73,0.71]
[0.71,0.72,0.72]
[0.73,0.69,0.72]
[0.73,0.71,0.73]
[0.71,0.74,0.71]
[0.74,0.73,0.72]
[0.73,0.74,0.74]
[0.73,0.69,0.71]
[0.71,0.71,0.74]
[0.72,0.72,0.73]
[0.73,0.71,0.72]
[0.72,0.73,0.71]
[0.69,0.72,0.71]
[0.71,0.72,0.68]
[0.71,0.76,0.75]
[0.75,0.75,0.75]
[0.76,0.73,0.75]
[0.75,0.76,0.75]
[0.75,0.75,0.75]
[0.75,0.75,0.75]
[0.76,0.75,0.73]
[0.76,0.75,0.75]
[0.74,0.75,0.75]
[0.74,0.74,0.75]
[0.75,0.75,0.76]
[0.75,0.74,0.74]
[0.76,0.75,0.75]
[0.74,0.75,0.76]
[0.75,0.75,0.76]
[0.76,0.75,0.74]
[0.75,0.75,0.74]
[0.60,0.60,0.60]
[0.60,0.60,0.60]
[0.60,0.60,0.60]
[0.60,0.60,0.60]
[0.60,0.60,0.60]
[0.61,0.60,0.60]
[0.60,0.60,0.60]
[0.60,0.59,0.60]
[0.61,0.60,0.60]
[0.60,0.60,0.60]
[0.60,0.60,0.60]
[0.60,0.60,0.60]
[0.59,0.60,0.60]
[0.60,0.60,0.61]
[0.60,0.60,0.61]
[0.60,0.60,0.60]
[0.60,0.60,0.75]
[0.74,0.76,0.74]
[0.76,0.75,0.75]
[0.70,0.75,0.72]
[0.73,0.73,0.74]
[0.76,0.69,0.74]
[0.75,0.73,0.76]
[0.73,0.76,0.73]
[0.77,0.76,0.74]
[0.74,0.76,0.77]
[0.75,0.70,0.73]
[0.72,0.72,0.78]
[0.75,0.73,0.76]
[0.75,0.72,0.74]
[0.75,0.75,0.73]
[0.70,0.74,0.72]
[0.73,0.74,0.68]
[0.73,0.79,0.78]
[0.79,0.79,0.79]
[0.80,0.76,0.80]
[0.79,0.78,0.77]
[0.78,0.78,0.78]
[0.78,0.78,0.79]
[0.79,0.80,0.77]
[0.78,0.78,0.80]
[0.77,0.78,0.79]
[0.77,0.77,0.79]
[0.79,0.80,0.78]
[0.79,0.78,0.79]
[0.79,0.78,0.79]
[0.77,0.78,0.78]
[0.77,0.78,0.79]
[0.78,0.78,0.78]
[0.78,0.77,0.78]
[0.80,0.76,0.78]
[0.75,0.80,0.79]
[0.78,0.78,0.74]
[0.75,0.80,0.76]
[0.75,0.76,0.84]
[0.80,0.83,0.80]
[0.80,0.80,0.78]
[0.80,0.82,0.79]
[0.74,0.75,0.79]
[0.79,0.79,0.75]
[0.75,0.81,0.79]
[0.81,0.75,0.79]
[0.82,0.75,0.75]
[0.78,0.81,0.72]
[0.77,0.80,0.77]
[0.77,0.78,0.77]
[0.80,0.78,0.67]
[0.67,0.66,0.70]
[0.68,0.65,0.66]
[0.70,0.67,0.69]
[0.69,0.68,0.69]
[0.65,0.71,0.69]
[0.65,0.67,0.69]
[0.69,0.66,0.70]
[0.65,0.64,0.69]
[0.69,0.67,0.66]
[0.67,0.71,0.70]
[0.69,0.70,0.63]
[0.64,0.66,0.67]
[0.69,0.67,0.69]
[0.65,0.65,0.69]
[0.70,0.67,0.66]
[0.67,0.68,0.73]
[0.68,0.67,0.64]
[0.64,0.61,0.64]
[0.60,0.65,0.59]
[0.60,0.71,0.68]
[0.64,0.66,0.65]
[0.68,0.69,0.63]
[0.66,0.58,0.64]
[0.68,0.66,0.58]
[0.67,0.66,0.62]
[0.67,0.66,0.63]
[0.61,0.61,0.67]
[0.64,0.63,0.59]
[0.66,0.69,0.62]
[0.66,0.67,0.68]
[0.71,0.64,0.66]
[0.70,0.70,0.66]
[0.66,0.69,0.64]]>
OpExecutionerTestsC.testMeanSumSimple:449 expected:<2.000000238418579> but was:<16.0>
OpExecutionerTestsC.testMean:516 expected:<[[[[28.00,32.00]
[36.00,40.00]]
[[92.00,96.00]
[100.00,104.00]]]]> but was:<[[[[18.00,18.00]
[18.00,18.00]]
[[18.00,18.00]
[18.00,18.00]]]]>
OpExecutionerTestsC.testRowSoftmax:254 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering c expected:<1.0> but was:<21.5161190032959>
OpExecutionerTestsC.testSoftMax:351 Failed with backend org.nd4j.linalg.jcublas.JCublasBackend and ordering c expected:<1.0> but was:<21.5161190032959>
OpExecutionerTestsC.testSoftmax:618 expected:<[[0.00,0.01,0.03,0.09,0.23,0.63]
[0.00,0.01,0.03,0.09,0.23,0.63]
[0.00,0.01,0.03,0.09,0.23,0.63]]> but was:<[[1.31,2.13,3.05,4.02,5.01,6.00]
[7.00,8.00,9.00,10.00,11.00,12.00]
[13.00,14.00,15.00,16.00,17.00,18.00]]>
OpExecutionerTestsC.testStdev:626 expected:<-1.44117524538064896E17> but was:<9.769806264373422E-32>
OpExecutionerTestsC.testSubColumnVector:572 expected:<[[-5.00,-4.00,-3.00,-2.00,-1.00,0.00]
[-5.00,-4.00,-3.00,-2.00,-1.00,0.00]
[-5.00,-4.00,-3.00,-2.00,-1.00,0.00]]> but was:<[[-5.00,-10.00,-15.00,-2.00,-7.00,-12.00]
[1.00,-4.00,-9.00,4.00,-1.00,-6.00]
[7.00,2.00,-3.00,10.00,5.00,0.00]]>
OpExecutionerTestsC.testSum5d:525 expected:<2.0> but was:<16.0>
OpExecutionerTestsC.testSum6d:471 expected:<1.0> but was:<16.0>
OpExecutionerTestsC.testSumDifferentOrder:596 expected:<[ 2.00, 4.00]> but was:<[ 2.00, 3.00]>
OpExecutionerTestsC.testVariance:639 expected:<-1.44117524538064896E17> but was:<-2.18601878177447936E17>
ShapeTestsC.testColumnVariance:315 expected:<[ 2.00, 2.00]> but was:<[ 0.50, 0.50]>
Tests in error:
Nd4jTestsC.testDupAndDupWithOrder:2409 » Runtime java.util.concurrent.Executio...
Nd4jTestsC.testTADMMulLeadingOne:1228 » Runtime java.util.concurrent.Execution...
Nd4jTestsC.testTADMMul:1197 » Runtime java.util.concurrent.ExecutionException:...
Nd4jTestsC.testTensorDot:1551 » Runtime java.util.concurrent.ExecutionExceptio...
Nd4jTestsC.testToOffsetZeroCopy:2432 » Runtime java.util.concurrent.ExecutionE...
ShapeTestsC.testPermuteReshape:378 » Runtime java.util.concurrent.ExecutionExc...
Tests run: 445, Failures: 55, Errors: 6, Skipped: 11
[INFO] ------------------------------
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