Execution Time0.06s

Test: TMVA-DNN-RNN-FullRNN-Cpu (Passed)
Build: v6-18-00-patches-x86_64-ubuntu16-gcc54-opt (sft-ubuntu-1604-4) on 2019-11-14 00:46:47
Repository revision: 869553a4dd0f00a0fc618d6e9d1fbdd66c820707

Test Timing: Passed
Processors1

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Test output
Training RNN to identity firstCopying output into input
Copying output into input
loss: 0.974803
loss: 0.164434
loss: 0.147423
loss: 0.133544
loss: 0.121845
loss: 0.11174
loss: 0.102852
loss: 0.0949316
loss: 0.0878081
loss: 0.081361
loss: 0.0755004
loss: 0.0701564
loss: 0.0652723
loss: 0.0608005
loss: 0.0567003
loss: 0.052936
loss: 0.0494759
loss: 0.0462921
loss: 0.0433593
loss: 0.040655
loss: 0.0381586
loss: 0.0358518
loss: 0.033718
loss: 0.0317422
loss: 0.0299108
loss: 0.0282115
loss: 0.0266332
loss: 0.0251657
loss: 0.0238
loss: 0.0225277
loss: 0.0213413
loss: 0.020234
loss: 0.0191994
loss: 0.0182318
loss: 0.0173262
loss: 0.0164778
loss: 0.0156823
loss: 0.0149357
loss: 0.0142345
loss: 0.0135753
loss: 0.0129552
loss: 0.0123712
loss: 0.011821
loss: 0.0113021
loss: 0.0108125
loss: 0.01035
loss: 0.009913
loss: 0.00949971
loss: 0.00910857
loss: 0.00873818
Training RNN to simple time dependent data iter = 1 loss: 0.824161
iter = 2 loss: 0.728135
iter = 3 loss: 0.708405
iter = 4 loss: 0.696328
iter = 5 loss: 0.687034
iter = 6 loss: 0.678625
iter = 7 loss: 0.670745
iter = 8 loss: 0.663607
iter = 9 loss: 0.657442
iter = 10 loss: 0.652125
iter = 11 loss: 0.647347
iter = 12 loss: 0.64284
iter = 13 loss: 0.638421
iter = 14 loss: 0.633974
iter = 15 loss: 0.629419
iter = 16 loss: 0.624707
iter = 17 loss: 0.619801
iter = 18 loss: 0.614675
iter = 19 loss: 0.60931
iter = 20 loss: 0.603695
iter = 21 loss: 0.597822
iter = 22 loss: 0.591686
iter = 23 loss: 0.585267
iter = 24 loss: 0.578518
iter = 25 loss: 0.571348
iter = 26 loss: 0.5636
iter = 27 loss: 0.555068
iter = 28 loss: 0.545517
iter = 29 loss: 0.534805
iter = 30 loss: 0.523264
iter = 31 loss: 0.511624
iter = 32 loss: 0.500189
iter = 33 loss: 0.489004
iter = 34 loss: 0.478086
iter = 35 loss: 0.467426
iter = 36 loss: 0.457003
iter = 37 loss: 0.446801
iter = 38 loss: 0.43681
iter = 39 loss: 0.427024
iter = 40 loss: 0.417439
iter = 41 loss: 0.408057
iter = 42 loss: 0.398879
iter = 43 loss: 0.389907
iter = 44 loss: 0.38115
iter = 45 loss: 0.372633
iter = 46 loss: 0.364411
iter = 47 loss: 0.356627
iter = 48 loss: 0.34963
iter = 49 loss: 0.344198
iter = 50 loss: 0.341925

2x64 matrix is as follows

     |       0    |       1    |       2    |       3    |       4    |
----------------------------------------------------------------------
   0 |          1           1           0           1           0 
   1 |     0.9211      0.9013      0.2959      0.8773      0.4777 


     |       5    |       6    |       7    |       8    |       9    |
----------------------------------------------------------------------
   0 |          1           0           1           1           1 
   1 |     0.8137      0.7187      0.7658      0.7842      0.8713 


     |      10    |      11    |      12    |      13    |      14    |
----------------------------------------------------------------------
   0 |          1           1           0           1           1 
   1 |      0.841      0.7709      0.2852      0.7365      0.8884 


     |      15    |      16    |      17    |      18    |      19    |
----------------------------------------------------------------------
   0 |          1           0           1           0           0 
   1 |     0.6865      0.4137      0.8616       0.241      0.2029 


     |      20    |      21    |      22    |      23    |      24    |
----------------------------------------------------------------------
   0 |          1           0           0           0           1 
   1 |     0.7469       0.131      0.4439      0.5556      0.8818 


     |      25    |      26    |      27    |      28    |      29    |
----------------------------------------------------------------------
   0 |          1           1           0           0           1 
   1 |     0.8421      0.8826      0.3504       0.327      0.7444 


     |      30    |      31    |      32    |      33    |      34    |
----------------------------------------------------------------------
   0 |          1           0           0           0           0 
   1 |     0.8563      0.2654      0.1988      0.4596      0.2147 


     |      35    |      36    |      37    |      38    |      39    |
----------------------------------------------------------------------
   0 |          1           0           0           1           0 
   1 |     0.8959      0.4929      0.6454      0.8599      0.2721 


     |      40    |      41    |      42    |      43    |      44    |
----------------------------------------------------------------------
   0 |          0           1           1           1           1 
   1 |     0.2058      0.4149      0.8659        0.87      0.7582 


     |      45    |      46    |      47    |      48    |      49    |
----------------------------------------------------------------------
   0 |          1           0           0           1           0 
   1 |     0.8677       0.664      0.2145      0.5903      0.2233 


     |      50    |      51    |      52    |      53    |      54    |
----------------------------------------------------------------------
   0 |          0           0           1           1           0 
   1 |     0.4318      0.2921      0.6455      0.8751      0.3943 


     |      55    |      56    |      57    |      58    |      59    |
----------------------------------------------------------------------
   0 |          1           1           0           1           0 
   1 |     0.6988       0.636       0.351      0.8718      0.3524 


     |      60    |      61    |      62    |      63    |
----------------------------------------------------------------------
   0 |          0           1           1           1 
   1 |     0.2032      0.7041       0.826      0.8625 

ROC integral is 0.453247
ERROR : Test full RNN failed : Efficiencies are 0.172414 and 0.971429