The Lund Neural Network Program - JETNET version 3.1
              ******  Latest date of change: Feb. 2, 1994   ******


                      Initialized for a 3 layered net with
                        1 nodes in layer number 2 (output layer)
                       10 nodes in layer number 1
                        6 nodes in layer number 0 (input layer)

                      Standard Back-Propagation updating.

                      Weights and thresholds set randomly
Training using 2 event classes over    30 epochs with  50 bins
UNSETR called
Minimum decay length significance: 3.000
Minimum vertex multuplicity      :     3
Initialisation complete - total of   8273 good patterns
Fraction in class 1: .3046
Fraction in class 2: .6954
Training using   6204 events, testing using   2069
Event cache filled with   8273 events (space for  12000)
After   1 epochs, trainFoM= .1258+- .0101 
                  test FoM= .2522+- .0172 
After   2 epochs, trainFoM= .2381+- .0097 
                  test FoM= .3032+- .0166 
After   3 epochs, trainFoM= .2769+- .0094 
                  test FoM= .3140+- .0165 
After   4 epochs, trainFoM= .2876+- .0093 
                  test FoM= .3215+- .0165 
After   5 epochs, trainFoM= .2973+- .0093 
                  test FoM= .3304+- .0163 
After   6 epochs, trainFoM= .3055+- .0091 
                  test FoM= .3424+- .0160 
After   7 epochs, trainFoM= .3123+- .0091 
                  test FoM= .3357+- .0159 
After   8 epochs, trainFoM= .3176+- .0090 
                  test FoM= .3445+- .0157 
After   9 epochs, trainFoM= .3199+- .0089 
                  test FoM= .3463+- .0156 
After  10 epochs, trainFoM= .3257+- .0089 
                  test FoM= .3516+- .0152 
After  11 epochs, trainFoM= .3286+- .0088 
                  test FoM= .3533+- .0155 
After  12 epochs, trainFoM= .3336+- .0088 
                  test FoM= .3601+- .0153 
After  13 epochs, trainFoM= .3356+- .0088 
                  test FoM= .3567+- .0153 
After  14 epochs, trainFoM= .3364+- .0088 
                  test FoM= .3595+- .0153 
After  15 epochs, trainFoM= .3398+- .0088 
                  test FoM= .3604+- .0154 
After  16 epochs, trainFoM= .3397+- .0087 
                  test FoM= .3586+- .0154 
After  17 epochs, trainFoM= .3426+- .0087 
                  test FoM= .3591+- .0153 
After  18 epochs, trainFoM= .3424+- .0087 
                  test FoM= .3610+- .0153 
After  19 epochs, trainFoM= .3436+- .0087 
                  test FoM= .3626+- .0153 
After  20 epochs, trainFoM= .3461+- .0087 
                  test FoM= .3636+- .0153 
After  21 epochs, trainFoM= .3452+- .0087 
                  test FoM= .3655+- .0152 
After  22 epochs, trainFoM= .3476+- .0087 
                  test FoM= .3652+- .0152 
After  23 epochs, trainFoM= .3478+- .0087 
                  test FoM= .3646+- .0152 
After  24 epochs, trainFoM= .3471+- .0087 
                  test FoM= .3668+- .0153 
After  25 epochs, trainFoM= .3494+- .0087 
                  test FoM= .3680+- .0152 
After  26 epochs, trainFoM= .3471+- .0087 
                  test FoM= .3642+- .0152 
After  27 epochs, trainFoM= .3485+- .0087 
                  test FoM= .3680+- .0152 
After  28 epochs, trainFoM= .3483+- .0087 
                  test FoM= .3661+- .0152 
After  29 epochs, trainFoM= .3488+- .0087 
                  test FoM= .3671+- .0152 
After  30 epochs, trainFoM= .3505+- .0087 
                  test FoM= .3712+- .0152 
NETTRA finished - summary
Nodename            FoM +- error
Input  1 class 1   .0792+- .0085
Input  1 class 2   .0792+- .0085
Input  2 class 1   .1013+- .0090
Input  2 class 2   .1013+- .0090
Input  3 class 1   .1252+- .0081
Input  3 class 2   .1252+- .0081
Input  4 class 1   .0346+- .0089
Input  4 class 2   .0346+- .0089
Input  5 class 1   .0861+- .0086
Input  5 class 2   .0861+- .0086
Input  6 class 1   .0437+- .0090
Input  6 class 2   .0437+- .0090
       trainFoM +- error testFoM +-error
Output 1   .3505+- .0087   .3712+- .0152
UNFIN: Input ntuple closed