Execution Time0.57s

Test: TMVA-DNN-RNN-FullRNN-Cpu (Passed)
Build: PR-4279-x86_64-fedora29-gcc8-opt (root-fedora29-3.cern.ch) on 2019-11-14 21:03:34

Test Timing: Passed
Processors1

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Test output
Training RNN to identity firstCopying output into input
Copying output into input
loss: 0.979692
loss: 0.508284
loss: 0.284437
loss: 0.168324
loss: 0.10531
loss: 0.070067
loss: 0.0498567
loss: 0.0379797
loss: 0.0308005
loss: 0.0263042
loss: 0.0233577
loss: 0.0213173
loss: 0.0198144
loss: 0.0186369
loss: 0.0176617
loss: 0.0168174
loss: 0.0160616
loss: 0.0153696
loss: 0.014726
loss: 0.0141216
loss: 0.0135503
loss: 0.0130083
loss: 0.0124925
loss: 0.012001
loss: 0.0115321
loss: 0.0110843
loss: 0.0106564
loss: 0.0102474
loss: 0.00985629
loss: 0.00948215
loss: 0.00912415
loss: 0.0087815
loss: 0.00845344
loss: 0.00813928
loss: 0.00783837
loss: 0.00755006
loss: 0.00727379
loss: 0.00700896
loss: 0.00675507
loss: 0.00651161
loss: 0.0062781
loss: 0.00605409
loss: 0.00583915
loss: 0.00563286
loss: 0.00543485
loss: 0.00524474
loss: 0.0050622
loss: 0.00488687
loss: 0.00471846
loss: 0.00455665
Training RNN to simple time dependent data iter = 1 loss: 0.739237
iter = 2 loss: 0.712661
iter = 3 loss: 0.704398
iter = 4 loss: 0.70013
iter = 5 loss: 0.697513
iter = 6 loss: 0.695753
iter = 7 loss: 0.69449
iter = 8 loss: 0.693534
iter = 9 loss: 0.692783
iter = 10 loss: 0.692172
iter = 11 loss: 0.691661
iter = 12 loss: 0.691223
iter = 13 loss: 0.690841
iter = 14 loss: 0.690499
iter = 15 loss: 0.690189
iter = 16 loss: 0.689902
iter = 17 loss: 0.689632
iter = 18 loss: 0.689373
iter = 19 loss: 0.689121
iter = 20 loss: 0.688872
iter = 21 loss: 0.68862
iter = 22 loss: 0.688362
iter = 23 loss: 0.688092
iter = 24 loss: 0.687803
iter = 25 loss: 0.687488
iter = 26 loss: 0.687138
iter = 27 loss: 0.686737
iter = 28 loss: 0.686266
iter = 29 loss: 0.685697
iter = 30 loss: 0.684989
iter = 31 loss: 0.684079
iter = 32 loss: 0.682882
iter = 33 loss: 0.681296
iter = 34 loss: 0.679253
iter = 35 loss: 0.676813
iter = 36 loss: 0.674167
iter = 37 loss: 0.671397
iter = 38 loss: 0.668366
iter = 39 loss: 0.664895
iter = 40 loss: 0.660969
iter = 41 loss: 0.656854
iter = 42 loss: 0.652784
iter = 43 loss: 0.64867
iter = 44 loss: 0.644376
iter = 45 loss: 0.63985
iter = 46 loss: 0.635061
iter = 47 loss: 0.629984
iter = 48 loss: 0.62459
iter = 49 loss: 0.618849
iter = 50 loss: 0.612728

2x64 matrix is as follows

     |       0    |       1    |       2    |       3    |       4    |
----------------------------------------------------------------------
   0 |          1           1           0           1           0 
   1 |     0.5869      0.6017      0.5318      0.5842      0.5151 


     |       5    |       6    |       7    |       8    |       9    |
----------------------------------------------------------------------
   0 |          1           0           1           1           1 
   1 |     0.5787      0.5419      0.5163      0.5266      0.5613 


     |      10    |      11    |      12    |      13    |      14    |
----------------------------------------------------------------------
   0 |          1           1           0           1           1 
   1 |     0.6142      0.5849      0.4272      0.5394      0.5886 


     |      15    |      16    |      17    |      18    |      19    |
----------------------------------------------------------------------
   0 |          1           0           1           0           0 
   1 |      0.546      0.3759       0.559      0.5468      0.4887 


     |      20    |      21    |      22    |      23    |      24    |
----------------------------------------------------------------------
   0 |          1           0           0           0           1 
   1 |     0.6036      0.4768      0.6189      0.4274      0.5819 


     |      25    |      26    |      27    |      28    |      29    |
----------------------------------------------------------------------
   0 |          1           1           0           0           1 
   1 |     0.6179      0.5148      0.5061      0.3494      0.5953 


     |      30    |      31    |      32    |      33    |      34    |
----------------------------------------------------------------------
   0 |          1           0           0           0           0 
   1 |     0.6122      0.4982      0.4533      0.4383      0.3556 


     |      35    |      36    |      37    |      38    |      39    |
----------------------------------------------------------------------
   0 |          1           0           0           1           0 
   1 |      0.403      0.5946      0.5598      0.4136      0.5243 


     |      40    |      41    |      42    |      43    |      44    |
----------------------------------------------------------------------
   0 |          0           1           1           1           1 
   1 |     0.4636      0.5223       0.574      0.6384      0.6051 


     |      45    |      46    |      47    |      48    |      49    |
----------------------------------------------------------------------
   0 |          1           0           0           1           0 
   1 |     0.6608      0.5157        0.49      0.5953      0.5499 


     |      50    |      51    |      52    |      53    |      54    |
----------------------------------------------------------------------
   0 |          0           0           1           1           0 
   1 |     0.5011      0.4442      0.5208      0.6156      0.5373 


     |      55    |      56    |      57    |      58    |      59    |
----------------------------------------------------------------------
   0 |          1           1           0           1           0 
   1 |     0.6118      0.6033      0.4309      0.6527         0.4 


     |      60    |      61    |      62    |      63    |
----------------------------------------------------------------------
   0 |          0           1           1           1 
   1 |     0.4369       0.637      0.6201      0.6036 

ROC integral is 0.453247
ERROR : Test full RNN failed : Efficiencies are 0.551724 and 0.942857