Execution Time0.15s

Test: TMVA-DNN-RNN-FullRNN-Cpu (Passed)
Build: master-aarch64-centos7-gcc48 (techlab-arm64-moonshot-xgene-004) on 2019-11-14 00:49:23
Repository revision: 32b17abcda23e44b64218a42d0ca69cb30cda7e0

Test Timing: Passed
Processors1

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Test output
Training RNN to identity firstCopying output into input
Copying output into input
loss: 1.90151
loss: 0.971274
loss: 0.528827
loss: 0.302542
loss: 0.181431
loss: 0.114754
loss: 0.0773582
loss: 0.0560941
loss: 0.0438504
loss: 0.0366995
loss: 0.0324427
loss: 0.0298391
loss: 0.0281836
loss: 0.0270744
loss: 0.0262816
loss: 0.0256734
loss: 0.0251738
loss: 0.0247393
loss: 0.0243444
loss: 0.0239747
loss: 0.0236215
loss: 0.02328
loss: 0.0229471
loss: 0.0226212
loss: 0.0223012
loss: 0.0219864
loss: 0.0216765
loss: 0.0213712
loss: 0.0210703
loss: 0.0207736
loss: 0.0204811
loss: 0.0201926
loss: 0.0199081
loss: 0.0196275
loss: 0.0193507
loss: 0.0190777
loss: 0.0188084
loss: 0.0185427
loss: 0.0182806
loss: 0.018022
loss: 0.0177669
loss: 0.0175152
loss: 0.0172668
loss: 0.0170218
loss: 0.01678
loss: 0.0165414
loss: 0.016306
loss: 0.0160738
loss: 0.0158446
loss: 0.0156184
Training RNN to simple time dependent data iter = 1 loss: 1.12894
iter = 2 loss: 0.710358
iter = 3 loss: 0.539514
iter = 4 loss: 0.47768
iter = 5 loss: 0.41731
iter = 6 loss: 0.359664
iter = 7 loss: 0.31797
iter = 8 loss: 0.28546
iter = 9 loss: 0.258269
iter = 10 loss: 0.235274
iter = 11 loss: 0.215716
iter = 12 loss: 0.198952
iter = 13 loss: 0.184457
iter = 14 loss: 0.171804
iter = 15 loss: 0.160656
iter = 16 loss: 0.150744
iter = 17 loss: 0.141858
iter = 18 loss: 0.133829
iter = 19 loss: 0.126529
iter = 20 loss: 0.119856
iter = 21 loss: 0.113739
iter = 22 loss: 0.108129
iter = 23 loss: 0.102986
iter = 24 loss: 0.0982762
iter = 25 loss: 0.0939586
iter = 26 loss: 0.0899905
iter = 27 loss: 0.0863295
iter = 28 loss: 0.0829381
iter = 29 loss: 0.0797847
iter = 30 loss: 0.0768433
iter = 31 loss: 0.0740922
iter = 32 loss: 0.0715135
iter = 33 loss: 0.0690919
iter = 34 loss: 0.066814
iter = 35 loss: 0.0646682
iter = 36 loss: 0.0626439
iter = 37 loss: 0.0607318
iter = 38 loss: 0.0589235
iter = 39 loss: 0.0572112
iter = 40 loss: 0.055588
iter = 41 loss: 0.0540475
iter = 42 loss: 0.0525839
iter = 43 loss: 0.051192
iter = 44 loss: 0.0498667
iter = 45 loss: 0.0486038
iter = 46 loss: 0.047399
iter = 47 loss: 0.0462487
iter = 48 loss: 0.0451494
iter = 49 loss: 0.0440979
iter = 50 loss: 0.0430913

2x64 matrix is as follows

     |       0    |       1    |       2    |       3    |       4    |
----------------------------------------------------------------------
   0 |          1           1           0           1           0 
   1 |     0.9934      0.9487     0.03093      0.9872      0.0858 


     |       5    |       6    |       7    |       8    |       9    |
----------------------------------------------------------------------
   0 |          1           0           1           1           1 
   1 |     0.9872     0.06578      0.9885      0.9923      0.9728 


     |      10    |      11    |      12    |      13    |      14    |
----------------------------------------------------------------------
   0 |          1           1           0           1           1 
   1 |     0.9856      0.9872     0.04032      0.9783      0.9875 


     |      15    |      16    |      17    |      18    |      19    |
----------------------------------------------------------------------
   0 |          1           0           1           0           0 
   1 |     0.9943     0.09216      0.9845     0.09763     0.08265 


     |      20    |      21    |      22    |      23    |      24    |
----------------------------------------------------------------------
   0 |          1           0           0           0           1 
   1 |     0.9244     0.02305     0.03871     0.03758      0.9919 


     |      25    |      26    |      27    |      28    |      29    |
----------------------------------------------------------------------
   0 |          1           1           0           0           1 
   1 |     0.9484       0.986     0.05478     0.04935      0.9903 


     |      30    |      31    |      32    |      33    |      34    |
----------------------------------------------------------------------
   0 |          1           0           0           0           0 
   1 |     0.9701     0.02949     0.04083     0.04965      0.0519 


     |      35    |      36    |      37    |      38    |      39    |
----------------------------------------------------------------------
   0 |          1           0           0           1           0 
   1 |     0.9787     0.06211     0.05277      0.9935     0.06156 


     |      40    |      41    |      42    |      43    |      44    |
----------------------------------------------------------------------
   0 |          0           1           1           1           1 
   1 |    0.06732      0.8976      0.9645      0.9932      0.9773 


     |      45    |      46    |      47    |      48    |      49    |
----------------------------------------------------------------------
   0 |          1           0           0           1           0 
   1 |     0.9577      0.1002     0.03596       0.967     0.08147 


     |      50    |      51    |      52    |      53    |      54    |
----------------------------------------------------------------------
   0 |          0           0           1           1           0 
   1 |    0.03827     0.05933      0.8566      0.9901     0.04056 


     |      55    |      56    |      57    |      58    |      59    |
----------------------------------------------------------------------
   0 |          1           1           0           1           0 
   1 |     0.9632      0.9874     0.04161      0.9808     0.06174 


     |      60    |      61    |      62    |      63    |
----------------------------------------------------------------------
   0 |          0           1           1           1 
   1 |    0.06187      0.9494      0.9798      0.9887 

ROC integral is 0.453247
Test full RNN passed : Efficiencies are 0 and 1