From IPRE Wiki
train()
# shapes = 2
tolerance = 0.3
hidden = 10
Epoch # 10 | TSS Error: 0.4775 | Correct: 0.5000 | RMS Error: 0.3455
Epoch # 10, Layer = 'output' | Units: 0.5000 | Patterns: 0.5000
----------------------------------------------------
Final # 13 | TSS Error: 0.1674 | Correct: 1.0000 | RMS Error: 0.2046
Final # 13, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 0 X 1 0 X 0 X 1 0 X 0 X 1 0 X
>>>
>>> train()
# shapes = 2
tolerance = 0.3
hidden = 5
Epoch # 10 | TSS Error: 0.5948 | Correct: 0.5000 | RMS Error: 0.3856
Epoch # 10, Layer = 'output' | Units: 0.5000 | Patterns: 0.5000
Epoch # 20 | TSS Error: 1.0952 | Correct: 0.0000 | RMS Error: 0.5233
Epoch # 20, Layer = 'output' | Units: 0.0000 | Patterns: 0.0000
Epoch # 30 | TSS Error: 0.3115 | Correct: 0.5000 | RMS Error: 0.2791
Epoch # 30, Layer = 'output' | Units: 0.5000 | Patterns: 0.5000
Epoch # 40 | TSS Error: 0.2484 | Correct: 0.5000 | RMS Error: 0.2492
Epoch # 40, Layer = 'output' | Units: 0.5000 | Patterns: 0.5000
Epoch # 50 | TSS Error: 1.2115 | Correct: 0.0000 | RMS Error: 0.5503
Epoch # 50, Layer = 'output' | Units: 0.0000 | Patterns: 0.0000
Epoch # 60 | TSS Error: 1.1310 | Correct: 0.0000 | RMS Error: 0.5318
Epoch # 60, Layer = 'output' | Units: 0.0000 | Patterns: 0.0000
Epoch # 70 | TSS Error: 0.2057 | Correct: 0.5000 | RMS Error: 0.2268
Epoch # 70, Layer = 'output' | Units: 0.5000 | Patterns: 0.5000
----------------------------------------------------
Final # 71 | TSS Error: 0.1480 | Correct: 1.0000 | RMS Error: 0.1923
Final # 71, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 0 X 0 X 0 X 0 X 1 0 X 0 X 0 X 0 X
>>>
>>> train()
# shapes = 2
tolerance = 0.3
hidden = 9
----------------------------------------------------
Final # 9 | TSS Error: 0.1894 | Correct: 1.0000 | RMS Error: 0.2176
Final # 9, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 1 1 1 1 1 1 1 1 1
>>>
>>> train()
# shapes = 2
tolerance = 0.3
hidden = 9
----------------------------------------------------
Final # 9 | TSS Error: 0.1635 | Correct: 1.0000 | RMS Error: 0.2022
Final # 9, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 1 1 1 1 1 1 1 1 1
>>>
train()
# shapes = 4
tolerance = 0.3
hidden = 10
Epoch # 10 | TSS Error: 3.0776 | Correct: 0.4375 | RMS Error: 0.4386
Epoch # 10, Layer = 'output' | Units: 0.4375 | Patterns: 0.0000
Epoch # 20 | TSS Error: 1.3484 | Correct: 0.8750 | RMS Error: 0.2903
Epoch # 20, Layer = 'output' | Units: 0.8750 | Patterns: 0.7500
----------------------------------------------------
Final # 28 | TSS Error: 0.0903 | Correct: 1.0000 | RMS Error: 0.0751
Final # 28, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 2 X 3 X 2 X 2 X 1 2 X 2 X 3 X 2 X
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 3 1 X 3 3 3 3 3 1 X 3
# wrong: 18
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 10
Epoch # 10 | TSS Error: 2.8635 | Correct: 0.6250 | RMS Error: 0.4230
Epoch # 10, Layer = 'output' | Units: 0.6250 | Patterns: 0.0000
Epoch # 20 | TSS Error: 1.1922 | Correct: 0.7500 | RMS Error: 0.2730
Epoch # 20, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
----------------------------------------------------
Final # 25 | TSS Error: 0.1918 | Correct: 1.0000 | RMS Error: 0.1095
Final # 25, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 1 1 1 1 1 1 1 1 1
Shape 2 2 1 X 0 X 1 X 1 X 2 0 X 0 X 1 X 0 X
Shape 3 3 1 X 1 X 1 X 1 X 3 1 X 1 X 1 X 1 X
# wrong: 16
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 10
Epoch # 10 | TSS Error: 2.8000 | Correct: 0.5000 | RMS Error: 0.4183
Epoch # 10, Layer = 'output' | Units: 0.5000 | Patterns: 0.0000
Epoch # 20 | TSS Error: 1.1669 | Correct: 0.7500 | RMS Error: 0.2701
Epoch # 20, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 30 | TSS Error: 1.1883 | Correct: 0.7500 | RMS Error: 0.2725
Epoch # 30, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 40 | TSS Error: 1.2583 | Correct: 0.6875 | RMS Error: 0.2804
Epoch # 40, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 50 | TSS Error: 1.2556 | Correct: 0.7500 | RMS Error: 0.2801
Epoch # 50, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
----------------------------------------------------
Final # 57 | TSS Error: 0.2212 | Correct: 1.0000 | RMS Error: 0.1176
Final # 57, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 3 X 3 X 3 X 3 X 1 3 X 3 X 3 X 3 X
Shape 2 2 2 0 X 0 X 2 2 0 X 2 2 0 X
Shape 3 3 3 1 X 1 X 3 3 3 1 X 1 X 3
# wrong: 16
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 15
Epoch # 10 | TSS Error: 2.1849 | Correct: 0.6250 | RMS Error: 0.3695
Epoch # 10, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 20 | TSS Error: 1.1271 | Correct: 0.7500 | RMS Error: 0.2654
Epoch # 20, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 30 | TSS Error: 1.2688 | Correct: 0.8125 | RMS Error: 0.2816
Epoch # 30, Layer = 'output' | Units: 0.8125 | Patterns: 0.5000
----------------------------------------------------
Final # 32 | TSS Error: 0.1072 | Correct: 1.0000 | RMS Error: 0.0819
Final # 32, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 1 1 1 1 1 1 1 1 1
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 1 X 1 X 1 X 1 X 3 1 X 1 X 1 X 1 X
# wrong: 16
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 15
Epoch # 10 | TSS Error: 3.0358 | Correct: 0.5000 | RMS Error: 0.4356
Epoch # 10, Layer = 'output' | Units: 0.5000 | Patterns: 0.0000
----------------------------------------------------
Final # 19 | TSS Error: 0.2462 | Correct: 1.0000 | RMS Error: 0.1240
Final # 19, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 1 1 1 1 1 1 1 1 1
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 1 X 1 X 1 X 1 X 3 1 X 1 X 1 X 1 X
# wrong: 16
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 15
Epoch # 10 | TSS Error: 2.8397 | Correct: 0.5000 | RMS Error: 0.4213
Epoch # 10, Layer = 'output' | Units: 0.5000 | Patterns: 0.0000
Epoch # 20 | TSS Error: 1.1159 | Correct: 0.8750 | RMS Error: 0.2641
Epoch # 20, Layer = 'output' | Units: 0.8750 | Patterns: 0.7500
----------------------------------------------------
Final # 21 | TSS Error: 0.1288 | Correct: 1.0000 | RMS Error: 0.0897
Final # 21, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 3 X 3 X 3 X 3 X 1 3 X 3 X 3 X 3 X
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 3 1 X 1 X 3 3 3 1 X 1 X 3
# wrong: 20
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 20
Epoch # 10 | TSS Error: 2.2688 | Correct: 0.5625 | RMS Error: 0.3766
Epoch # 10, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 20 | TSS Error: 1.3596 | Correct: 0.6875 | RMS Error: 0.2915
Epoch # 20, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
----------------------------------------------------
Final # 26 | TSS Error: 0.0314 | Correct: 1.0000 | RMS Error: 0.0443
Final # 26, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 2 X 2 X 2 X 2 X 1 2 X 2 X 2 X 2 X
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 2 X 1 X 1 X 2 X 3 2 X 1 X 1 X 2 X
# wrong: 24
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 20
Epoch # 10 | TSS Error: 1.8543 | Correct: 0.6875 | RMS Error: 0.3404
Epoch # 10, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
----------------------------------------------------
Final # 16 | TSS Error: 0.1933 | Correct: 1.0000 | RMS Error: 0.1099
Final # 16, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 2 X 2 X 2 X 2 X 1 2 X 2 X 2 X 2 X
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 2 X 3 2 X 2 X 3 2 X 2 X 3 2 X
# wrong: 22
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 20
Epoch # 10 | TSS Error: 3.2988 | Correct: 0.4375 | RMS Error: 0.4541
Epoch # 10, Layer = 'output' | Units: 0.4375 | Patterns: 0.0000
----------------------------------------------------
Final # 19 | TSS Error: 0.1188 | Correct: 1.0000 | RMS Error: 0.0862
Final # 19, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 1 1 1 1 1 1 1 1 1
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 1 X 1 X 1 X 1 X 3 1 X 1 X 1 X 1 X
# wrong: 16
>>>
>>>
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 5
Epoch # 10 | TSS Error: 3.1204 | Correct: 0.7500 | RMS Error: 0.4416
Epoch # 10, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 20 | TSS Error: 2.7499 | Correct: 0.5625 | RMS Error: 0.4146
Epoch # 20, Layer = 'output' | Units: 0.5625 | Patterns: 0.0000
Epoch # 30 | TSS Error: 2.8959 | Correct: 0.5625 | RMS Error: 0.4254
Epoch # 30, Layer = 'output' | Units: 0.5625 | Patterns: 0.0000
Epoch # 40 | TSS Error: 1.2629 | Correct: 0.6875 | RMS Error: 0.2809
Epoch # 40, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 50 | TSS Error: 1.7556 | Correct: 0.6250 | RMS Error: 0.3312
Epoch # 50, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 60 | TSS Error: 1.1062 | Correct: 0.7500 | RMS Error: 0.2629
Epoch # 60, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 70 | TSS Error: 1.1261 | Correct: 0.7500 | RMS Error: 0.2653
Epoch # 70, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 80 | TSS Error: 0.6982 | Correct: 0.8125 | RMS Error: 0.2089
Epoch # 80, Layer = 'output' | Units: 0.8125 | Patterns: 0.5000
----------------------------------------------------
Final # 87 | TSS Error: 0.1057 | Correct: 1.0000 | RMS Error: 0.0813
Final # 87, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 1 1 3 X 1 1 1 3 X 1 1
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 3 3 1 X 3 3 3 1 X 3 3
# wrong: 12
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 5
Epoch # 10 | TSS Error: 3.1709 | Correct: 0.7500 | RMS Error: 0.4452
Epoch # 10, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 20 | TSS Error: 1.7544 | Correct: 0.6875 | RMS Error: 0.3311
Epoch # 20, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 30 | TSS Error: 1.5244 | Correct: 0.8125 | RMS Error: 0.3087
Epoch # 30, Layer = 'output' | Units: 0.8125 | Patterns: 0.5000
Epoch # 40 | TSS Error: 1.8296 | Correct: 0.8125 | RMS Error: 0.3382
Epoch # 40, Layer = 'output' | Units: 0.8125 | Patterns: 0.5000
Epoch # 50 | TSS Error: 1.8145 | Correct: 0.7500 | RMS Error: 0.3368
Epoch # 50, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
----------------------------------------------------
Final # 54 | TSS Error: 0.2419 | Correct: 1.0000 | RMS Error: 0.1230
Final # 54, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 3 X 2 X 3 X 2 X 2 X 3 X 2 X 2 X 3 X 2 X
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 3 3 3 3 3 3 3 3 3
# wrong: 18
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 5
Epoch # 10 | TSS Error: 3.1707 | Correct: 0.5625 | RMS Error: 0.4452
Epoch # 10, Layer = 'output' | Units: 0.5625 | Patterns: 0.0000
Epoch # 20 | TSS Error: 1.7764 | Correct: 0.6875 | RMS Error: 0.3332
Epoch # 20, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 30 | TSS Error: 0.8333 | Correct: 0.7500 | RMS Error: 0.2282
Epoch # 30, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
----------------------------------------------------
Final # 32 | TSS Error: 0.2763 | Correct: 1.0000 | RMS Error: 0.1314
Final # 32, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 1 1 1 1 1 1 1 1 1
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 1 X 1 X 1 X 1 X 3 1 X 1 X 1 X 1 X
# wrong: 16
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 1
Epoch # 10 | TSS Error: 2.6055 | Correct: 0.6875 | RMS Error: 0.4035
Epoch # 10, Layer = 'output' | Units: 0.6875 | Patterns: 0.0000
Epoch # 20 | TSS Error: 2.2270 | Correct: 0.5000 | RMS Error: 0.3731
Epoch # 20, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 30 | TSS Error: 2.2014 | Correct: 0.5000 | RMS Error: 0.3709
Epoch # 30, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 40 | TSS Error: 2.1473 | Correct: 0.5625 | RMS Error: 0.3663
Epoch # 40, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 50 | TSS Error: 2.0775 | Correct: 0.5625 | RMS Error: 0.3603
Epoch # 50, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 60 | TSS Error: 2.1444 | Correct: 0.5625 | RMS Error: 0.3661
Epoch # 60, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 70 | TSS Error: 2.1644 | Correct: 0.4375 | RMS Error: 0.3678
Epoch # 70, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 80 | TSS Error: 2.1080 | Correct: 0.4375 | RMS Error: 0.3630
Epoch # 80, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 90 | TSS Error: 2.1771 | Correct: 0.4375 | RMS Error: 0.3689
Epoch # 90, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 100 | TSS Error: 2.1393 | Correct: 0.5000 | RMS Error: 0.3657
Epoch # 100, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 110 | TSS Error: 2.1204 | Correct: 0.4375 | RMS Error: 0.3640
Epoch # 110, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 120 | TSS Error: 2.1323 | Correct: 0.5625 | RMS Error: 0.3651
Epoch # 120, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 130 | TSS Error: 2.1696 | Correct: 0.5000 | RMS Error: 0.3682
Epoch # 130, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 140 | TSS Error: 2.1228 | Correct: 0.5625 | RMS Error: 0.3642
Epoch # 140, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 150 | TSS Error: 2.1342 | Correct: 0.4375 | RMS Error: 0.3652
Epoch # 150, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 160 | TSS Error: 2.1777 | Correct: 0.5000 | RMS Error: 0.3689
Epoch # 160, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 170 | TSS Error: 2.0614 | Correct: 0.4375 | RMS Error: 0.3589
Epoch # 170, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 180 | TSS Error: 2.0587 | Correct: 0.5625 | RMS Error: 0.3587
Epoch # 180, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 190 | TSS Error: 2.1245 | Correct: 0.5625 | RMS Error: 0.3644
Epoch # 190, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 200 | TSS Error: 2.1362 | Correct: 0.4375 | RMS Error: 0.3654
Epoch # 200, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 210 | TSS Error: 2.1456 | Correct: 0.5625 | RMS Error: 0.3662
Epoch # 210, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 220 | TSS Error: 2.1896 | Correct: 0.5000 | RMS Error: 0.3699
Epoch # 220, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 230 | TSS Error: 2.0707 | Correct: 0.5625 | RMS Error: 0.3598
Epoch # 230, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 240 | TSS Error: 2.1575 | Correct: 0.4375 | RMS Error: 0.3672
Epoch # 240, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 250 | TSS Error: 2.1129 | Correct: 0.6875 | RMS Error: 0.3634
Epoch # 250, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 260 | TSS Error: 2.1062 | Correct: 0.5625 | RMS Error: 0.3628
Epoch # 260, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 270 | TSS Error: 2.0709 | Correct: 0.5625 | RMS Error: 0.3598
Epoch # 270, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 280 | TSS Error: 2.1772 | Correct: 0.5000 | RMS Error: 0.3689
Epoch # 280, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 290 | TSS Error: 2.1041 | Correct: 0.5000 | RMS Error: 0.3626
Epoch # 290, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 300 | TSS Error: 2.1284 | Correct: 0.5625 | RMS Error: 0.3647
Epoch # 300, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 310 | TSS Error: 2.0898 | Correct: 0.5625 | RMS Error: 0.3614
Epoch # 310, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 320 | TSS Error: 2.1529 | Correct: 0.4375 | RMS Error: 0.3668
Epoch # 320, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 330 | TSS Error: 2.1224 | Correct: 0.5625 | RMS Error: 0.3642
Epoch # 330, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 340 | TSS Error: 2.1125 | Correct: 0.5000 | RMS Error: 0.3634
Epoch # 340, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 350 | TSS Error: 2.1174 | Correct: 0.4375 | RMS Error: 0.3638
Epoch # 350, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 360 | TSS Error: 2.0660 | Correct: 0.5625 | RMS Error: 0.3593
Epoch # 360, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 370 | TSS Error: 2.1496 | Correct: 0.5625 | RMS Error: 0.3665
Epoch # 370, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 380 | TSS Error: 2.0619 | Correct: 0.5625 | RMS Error: 0.3590
Epoch # 380, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 390 | TSS Error: 2.1247 | Correct: 0.5625 | RMS Error: 0.3644
Epoch # 390, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 400 | TSS Error: 2.1205 | Correct: 0.5000 | RMS Error: 0.3641
Epoch # 400, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 410 | TSS Error: 2.0739 | Correct: 0.4375 | RMS Error: 0.3600
Epoch # 410, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 420 | TSS Error: 2.0793 | Correct: 0.4375 | RMS Error: 0.3605
Epoch # 420, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 430 | TSS Error: 2.1748 | Correct: 0.4375 | RMS Error: 0.3687
Epoch # 430, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 440 | TSS Error: 2.0978 | Correct: 0.6250 | RMS Error: 0.3621
Epoch # 440, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 450 | TSS Error: 2.1423 | Correct: 0.5625 | RMS Error: 0.3659
Epoch # 450, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 460 | TSS Error: 2.2155 | Correct: 0.6250 | RMS Error: 0.3721
Epoch # 460, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 470 | TSS Error: 2.1354 | Correct: 0.5000 | RMS Error: 0.3653
Epoch # 470, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 480 | TSS Error: 2.1392 | Correct: 0.5625 | RMS Error: 0.3657
Epoch # 480, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 490 | TSS Error: 2.0603 | Correct: 0.4375 | RMS Error: 0.3588
Epoch # 490, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 500 | TSS Error: 2.0926 | Correct: 0.5625 | RMS Error: 0.3616
Epoch # 500, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 510 | TSS Error: 2.1532 | Correct: 0.5625 | RMS Error: 0.3668
Epoch # 510, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 520 | TSS Error: 2.1069 | Correct: 0.4375 | RMS Error: 0.3629
Epoch # 520, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 530 | TSS Error: 2.1020 | Correct: 0.5625 | RMS Error: 0.3625
Epoch # 530, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 540 | TSS Error: 2.1254 | Correct: 0.4375 | RMS Error: 0.3645
Epoch # 540, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 550 | TSS Error: 2.1220 | Correct: 0.4375 | RMS Error: 0.3642
Epoch # 550, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 560 | TSS Error: 2.0944 | Correct: 0.5625 | RMS Error: 0.3618
Epoch # 560, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 570 | TSS Error: 2.1226 | Correct: 0.5625 | RMS Error: 0.3642
Epoch # 570, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 580 | TSS Error: 2.1769 | Correct: 0.5625 | RMS Error: 0.3689
Epoch # 580, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 590 | TSS Error: 2.1075 | Correct: 0.5000 | RMS Error: 0.3629
Epoch # 590, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 600 | TSS Error: 2.2116 | Correct: 0.5625 | RMS Error: 0.3718
Epoch # 600, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 610 | TSS Error: 2.1026 | Correct: 0.5625 | RMS Error: 0.3625
Epoch # 610, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 620 | TSS Error: 2.0935 | Correct: 0.6250 | RMS Error: 0.3617
Epoch # 620, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 630 | TSS Error: 2.0608 | Correct: 0.5000 | RMS Error: 0.3589
Epoch # 630, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 640 | TSS Error: 2.1556 | Correct: 0.4375 | RMS Error: 0.3671
Epoch # 640, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 650 | TSS Error: 2.1527 | Correct: 0.5000 | RMS Error: 0.3668
Epoch # 650, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 660 | TSS Error: 2.1065 | Correct: 0.4375 | RMS Error: 0.3628
Epoch # 660, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 670 | TSS Error: 2.0916 | Correct: 0.5000 | RMS Error: 0.3616
Epoch # 670, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 680 | TSS Error: 2.1399 | Correct: 0.4375 | RMS Error: 0.3657
Epoch # 680, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 690 | TSS Error: 2.1149 | Correct: 0.5625 | RMS Error: 0.3636
Epoch # 690, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 700 | TSS Error: 2.0972 | Correct: 0.5625 | RMS Error: 0.3620
Epoch # 700, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 710 | TSS Error: 2.1196 | Correct: 0.5625 | RMS Error: 0.3640
Epoch # 710, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 720 | TSS Error: 2.1403 | Correct: 0.5000 | RMS Error: 0.3657
Epoch # 720, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 730 | TSS Error: 2.1203 | Correct: 0.5625 | RMS Error: 0.3640
Epoch # 730, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 740 | TSS Error: 2.1276 | Correct: 0.5625 | RMS Error: 0.3647
Epoch # 740, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 750 | TSS Error: 2.0867 | Correct: 0.5000 | RMS Error: 0.3611
Epoch # 750, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 760 | TSS Error: 2.0434 | Correct: 0.6875 | RMS Error: 0.3574
Epoch # 760, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 770 | TSS Error: 2.1146 | Correct: 0.4375 | RMS Error: 0.3635
Epoch # 770, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 780 | TSS Error: 2.0776 | Correct: 0.4375 | RMS Error: 0.3603
Epoch # 780, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 790 | TSS Error: 2.1378 | Correct: 0.5625 | RMS Error: 0.3655
Epoch # 790, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 800 | TSS Error: 2.1576 | Correct: 0.5625 | RMS Error: 0.3672
Epoch # 800, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 810 | TSS Error: 2.2089 | Correct: 0.5000 | RMS Error: 0.3716
Epoch # 810, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 820 | TSS Error: 2.0943 | Correct: 0.5625 | RMS Error: 0.3618
Epoch # 820, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 830 | TSS Error: 2.1363 | Correct: 0.6250 | RMS Error: 0.3654
Epoch # 830, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 840 | TSS Error: 2.1000 | Correct: 0.5625 | RMS Error: 0.3623
Epoch # 840, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 850 | TSS Error: 2.0716 | Correct: 0.5625 | RMS Error: 0.3598
Epoch # 850, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 860 | TSS Error: 2.2292 | Correct: 0.5625 | RMS Error: 0.3733
Epoch # 860, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 870 | TSS Error: 2.0821 | Correct: 0.5625 | RMS Error: 0.3607
Epoch # 870, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 880 | TSS Error: 2.0612 | Correct: 0.5625 | RMS Error: 0.3589
Epoch # 880, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 890 | TSS Error: 2.1509 | Correct: 0.5000 | RMS Error: 0.3666
Epoch # 890, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 900 | TSS Error: 2.1065 | Correct: 0.5625 | RMS Error: 0.3628
Epoch # 900, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 910 | TSS Error: 2.1552 | Correct: 0.5000 | RMS Error: 0.3670
Epoch # 910, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 920 | TSS Error: 2.1301 | Correct: 0.5625 | RMS Error: 0.3649
Epoch # 920, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 930 | TSS Error: 2.0713 | Correct: 0.5625 | RMS Error: 0.3598
Epoch # 930, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 940 | TSS Error: 2.1078 | Correct: 0.5625 | RMS Error: 0.3630
Epoch # 940, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 950 | TSS Error: 2.0938 | Correct: 0.5625 | RMS Error: 0.3617
Epoch # 950, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 960 | TSS Error: 2.1362 | Correct: 0.6250 | RMS Error: 0.3654
Epoch # 960, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 970 | TSS Error: 2.1005 | Correct: 0.4375 | RMS Error: 0.3623
Epoch # 970, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 980 | TSS Error: 2.1105 | Correct: 0.4375 | RMS Error: 0.3632
Epoch # 980, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 990 | TSS Error: 2.0965 | Correct: 0.5625 | RMS Error: 0.3620
Epoch # 990, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1000 | TSS Error: 2.1046 | Correct: 0.5625 | RMS Error: 0.3627
Epoch # 1000, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1010 | TSS Error: 2.1953 | Correct: 0.4375 | RMS Error: 0.3704
Epoch # 1010, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1020 | TSS Error: 2.1481 | Correct: 0.6875 | RMS Error: 0.3664
Epoch # 1020, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 1030 | TSS Error: 2.1131 | Correct: 0.5000 | RMS Error: 0.3634
Epoch # 1030, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1040 | TSS Error: 2.1124 | Correct: 0.5625 | RMS Error: 0.3633
Epoch # 1040, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1050 | TSS Error: 2.1471 | Correct: 0.5000 | RMS Error: 0.3663
Epoch # 1050, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1060 | TSS Error: 2.0756 | Correct: 0.4375 | RMS Error: 0.3602
Epoch # 1060, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1070 | TSS Error: 2.1931 | Correct: 0.5000 | RMS Error: 0.3702
Epoch # 1070, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1080 | TSS Error: 2.1931 | Correct: 0.5625 | RMS Error: 0.3702
Epoch # 1080, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1090 | TSS Error: 2.1228 | Correct: 0.5625 | RMS Error: 0.3642
Epoch # 1090, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1100 | TSS Error: 2.1346 | Correct: 0.5625 | RMS Error: 0.3653
Epoch # 1100, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1110 | TSS Error: 2.0749 | Correct: 0.5625 | RMS Error: 0.3601
Epoch # 1110, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1120 | TSS Error: 2.1737 | Correct: 0.4375 | RMS Error: 0.3686
Epoch # 1120, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1130 | TSS Error: 2.1376 | Correct: 0.5625 | RMS Error: 0.3655
Epoch # 1130, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1140 | TSS Error: 2.1561 | Correct: 0.5625 | RMS Error: 0.3671
Epoch # 1140, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1150 | TSS Error: 2.0635 | Correct: 0.4375 | RMS Error: 0.3591
Epoch # 1150, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1160 | TSS Error: 2.2351 | Correct: 0.5625 | RMS Error: 0.3738
Epoch # 1160, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1170 | TSS Error: 2.1905 | Correct: 0.4375 | RMS Error: 0.3700
Epoch # 1170, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1180 | TSS Error: 2.0961 | Correct: 0.5000 | RMS Error: 0.3619
Epoch # 1180, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1190 | TSS Error: 2.1395 | Correct: 0.5000 | RMS Error: 0.3657
Epoch # 1190, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1200 | TSS Error: 2.1498 | Correct: 0.6250 | RMS Error: 0.3666
Epoch # 1200, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 1210 | TSS Error: 2.1440 | Correct: 0.5625 | RMS Error: 0.3661
Epoch # 1210, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1220 | TSS Error: 2.0922 | Correct: 0.5000 | RMS Error: 0.3616
Epoch # 1220, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1230 | TSS Error: 2.0797 | Correct: 0.4375 | RMS Error: 0.3605
Epoch # 1230, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1240 | TSS Error: 2.1617 | Correct: 0.5625 | RMS Error: 0.3676
Epoch # 1240, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1250 | TSS Error: 2.1187 | Correct: 0.4375 | RMS Error: 0.3639
Epoch # 1250, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1260 | TSS Error: 2.0437 | Correct: 0.4375 | RMS Error: 0.3574
Epoch # 1260, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1270 | TSS Error: 2.1072 | Correct: 0.5000 | RMS Error: 0.3629
Epoch # 1270, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1280 | TSS Error: 2.1557 | Correct: 0.4375 | RMS Error: 0.3671
Epoch # 1280, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1290 | TSS Error: 2.2130 | Correct: 0.5000 | RMS Error: 0.3719
Epoch # 1290, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1300 | TSS Error: 2.1628 | Correct: 0.4375 | RMS Error: 0.3677
Epoch # 1300, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1310 | TSS Error: 2.1535 | Correct: 0.5625 | RMS Error: 0.3669
Epoch # 1310, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1320 | TSS Error: 2.1147 | Correct: 0.5000 | RMS Error: 0.3635
Epoch # 1320, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1330 | TSS Error: 2.1411 | Correct: 0.5625 | RMS Error: 0.3658
Epoch # 1330, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1340 | TSS Error: 2.1186 | Correct: 0.5625 | RMS Error: 0.3639
Epoch # 1340, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1350 | TSS Error: 2.0908 | Correct: 0.5625 | RMS Error: 0.3615
Epoch # 1350, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1360 | TSS Error: 2.1233 | Correct: 0.5625 | RMS Error: 0.3643
Epoch # 1360, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1370 | TSS Error: 2.1268 | Correct: 0.5625 | RMS Error: 0.3646
Epoch # 1370, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1380 | TSS Error: 2.1744 | Correct: 0.6250 | RMS Error: 0.3686
Epoch # 1380, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 1390 | TSS Error: 2.0716 | Correct: 0.5625 | RMS Error: 0.3598
Epoch # 1390, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1400 | TSS Error: 2.0856 | Correct: 0.5625 | RMS Error: 0.3610
Epoch # 1400, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1410 | TSS Error: 2.0642 | Correct: 0.5625 | RMS Error: 0.3592
Epoch # 1410, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1420 | TSS Error: 2.1105 | Correct: 0.5625 | RMS Error: 0.3632
Epoch # 1420, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1430 | TSS Error: 2.1550 | Correct: 0.4375 | RMS Error: 0.3670
Epoch # 1430, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1440 | TSS Error: 2.1421 | Correct: 0.5625 | RMS Error: 0.3659
Epoch # 1440, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1450 | TSS Error: 2.1590 | Correct: 0.4375 | RMS Error: 0.3673
Epoch # 1450, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1460 | TSS Error: 2.1403 | Correct: 0.5000 | RMS Error: 0.3657
Epoch # 1460, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1470 | TSS Error: 2.0653 | Correct: 0.5625 | RMS Error: 0.3593
Epoch # 1470, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1480 | TSS Error: 2.0950 | Correct: 0.5625 | RMS Error: 0.3619
Epoch # 1480, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1490 | TSS Error: 2.0952 | Correct: 0.5625 | RMS Error: 0.3619
Epoch # 1490, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1500 | TSS Error: 2.2036 | Correct: 0.5000 | RMS Error: 0.3711
Epoch # 1500, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1510 | TSS Error: 2.0999 | Correct: 0.4375 | RMS Error: 0.3623
Epoch # 1510, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1520 | TSS Error: 2.1045 | Correct: 0.4375 | RMS Error: 0.3627
Epoch # 1520, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1530 | TSS Error: 2.0995 | Correct: 0.4375 | RMS Error: 0.3622
Epoch # 1530, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1540 | TSS Error: 2.1255 | Correct: 0.5000 | RMS Error: 0.3645
Epoch # 1540, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1550 | TSS Error: 2.1397 | Correct: 0.5625 | RMS Error: 0.3657
Epoch # 1550, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1560 | TSS Error: 2.0670 | Correct: 0.5625 | RMS Error: 0.3594
Epoch # 1560, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1570 | TSS Error: 2.1305 | Correct: 0.5625 | RMS Error: 0.3649
Epoch # 1570, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1580 | TSS Error: 2.1802 | Correct: 0.6250 | RMS Error: 0.3691
Epoch # 1580, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 1590 | TSS Error: 2.1608 | Correct: 0.5000 | RMS Error: 0.3675
Epoch # 1590, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1600 | TSS Error: 2.1799 | Correct: 0.4375 | RMS Error: 0.3691
Epoch # 1600, Layer = 'output' | Units: 0.4375 | Patterns: 0.2500
Epoch # 1610 | TSS Error: 2.0843 | Correct: 0.5000 | RMS Error: 0.3609
Epoch # 1610, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1620 | TSS Error: 2.0701 | Correct: 0.5625 | RMS Error: 0.3597
Epoch # 1620, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 1630 | TSS Error: 2.1410 | Correct: 0.5000 | RMS Error: 0.3658
Epoch # 1630, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 1640 | TSS Error: 2.1419 | Correct: 0.5625 | RMS Error: 0.3659
Epoch # 1640, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Traceback (most recent call last):
File "<pyshell#24>", line 1, in -toplevel-
train()
File "C:\Documents and Settings\compsci\Desktop\Code\test.py", line 35, in train
n.train()
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1553, in train
(tssErr, totalCorrect, totalCount, totalPCorrect) = self.sweep()
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1682, in sweep
(error, correct, total, pcorrect) = self.step( **datum )
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1614, in step
self.propagate(**args)
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1842, in propagate
layer.activation = self.activationFunction(layer.netinput)
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1919, in activationFunctionASIG
return Numeric.array(map(act, x), 'f') - self._symmetricOffset
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1918, in act
else: return 1.0 / (1.0 + Numeric.exp(-v))
KeyboardInterrupt
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 4
Epoch # 10 | TSS Error: 3.1318 | Correct: 0.7500 | RMS Error: 0.4424
Epoch # 10, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 20 | TSS Error: 3.1158 | Correct: 0.7500 | RMS Error: 0.4413
Epoch # 20, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 30 | TSS Error: 3.1807 | Correct: 0.7500 | RMS Error: 0.4459
Epoch # 30, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 40 | TSS Error: 2.1959 | Correct: 0.5625 | RMS Error: 0.3705
Epoch # 40, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 50 | TSS Error: 2.1375 | Correct: 0.5000 | RMS Error: 0.3655
Epoch # 50, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 60 | TSS Error: 2.2511 | Correct: 0.5625 | RMS Error: 0.3751
Epoch # 60, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 70 | TSS Error: 2.1213 | Correct: 0.5625 | RMS Error: 0.3641
Epoch # 70, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 80 | TSS Error: 1.3866 | Correct: 0.6250 | RMS Error: 0.2944
Epoch # 80, Layer = 'output' | Units: 0.6250 | Patterns: 0.5000
Epoch # 90 | TSS Error: 2.1597 | Correct: 0.5625 | RMS Error: 0.3674
Epoch # 90, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 100 | TSS Error: 1.3596 | Correct: 0.7500 | RMS Error: 0.2915
Epoch # 100, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 110 | TSS Error: 2.1811 | Correct: 0.5625 | RMS Error: 0.3692
Epoch # 110, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 120 | TSS Error: 2.5980 | Correct: 0.5625 | RMS Error: 0.4030
Epoch # 120, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 130 | TSS Error: 2.1373 | Correct: 0.5625 | RMS Error: 0.3655
Epoch # 130, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 140 | TSS Error: 2.3264 | Correct: 0.5625 | RMS Error: 0.3813
Epoch # 140, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 150 | TSS Error: 1.2426 | Correct: 0.7500 | RMS Error: 0.2787
Epoch # 150, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 160 | TSS Error: 1.1551 | Correct: 0.7500 | RMS Error: 0.2687
Epoch # 160, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 170 | TSS Error: 1.1079 | Correct: 0.7500 | RMS Error: 0.2631
Epoch # 170, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 180 | TSS Error: 1.2466 | Correct: 0.7500 | RMS Error: 0.2791
Epoch # 180, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 190 | TSS Error: 1.0336 | Correct: 0.7500 | RMS Error: 0.2542
Epoch # 190, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 200 | TSS Error: 1.0856 | Correct: 0.7500 | RMS Error: 0.2605
Epoch # 200, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 210 | TSS Error: 1.0962 | Correct: 0.7500 | RMS Error: 0.2618
Epoch # 210, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 220 | TSS Error: 1.0327 | Correct: 0.7500 | RMS Error: 0.2540
Epoch # 220, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 230 | TSS Error: 1.1611 | Correct: 0.7500 | RMS Error: 0.2694
Epoch # 230, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 240 | TSS Error: 1.0770 | Correct: 0.7500 | RMS Error: 0.2594
Epoch # 240, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 250 | TSS Error: 1.0892 | Correct: 0.7500 | RMS Error: 0.2609
Epoch # 250, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 260 | TSS Error: 1.0751 | Correct: 0.7500 | RMS Error: 0.2592
Epoch # 260, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 270 | TSS Error: 1.0530 | Correct: 0.7500 | RMS Error: 0.2565
Epoch # 270, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 280 | TSS Error: 1.0791 | Correct: 0.7500 | RMS Error: 0.2597
Epoch # 280, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 290 | TSS Error: 1.0905 | Correct: 0.7500 | RMS Error: 0.2611
Epoch # 290, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 300 | TSS Error: 1.1864 | Correct: 0.7500 | RMS Error: 0.2723
Epoch # 300, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 310 | TSS Error: 1.0727 | Correct: 0.7500 | RMS Error: 0.2589
Epoch # 310, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 320 | TSS Error: 2.5995 | Correct: 0.5625 | RMS Error: 0.4031
Epoch # 320, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 330 | TSS Error: 1.0564 | Correct: 0.7500 | RMS Error: 0.2570
Epoch # 330, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 340 | TSS Error: 1.0574 | Correct: 0.7500 | RMS Error: 0.2571
Epoch # 340, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 350 | TSS Error: 1.0787 | Correct: 0.7500 | RMS Error: 0.2596
Epoch # 350, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 360 | TSS Error: 1.1371 | Correct: 0.7500 | RMS Error: 0.2666
Epoch # 360, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 370 | TSS Error: 1.1579 | Correct: 0.7500 | RMS Error: 0.2690
Epoch # 370, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 380 | TSS Error: 1.2215 | Correct: 0.7500 | RMS Error: 0.2763
Epoch # 380, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 390 | TSS Error: 1.0534 | Correct: 0.7500 | RMS Error: 0.2566
Epoch # 390, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 400 | TSS Error: 1.1449 | Correct: 0.7500 | RMS Error: 0.2675
Epoch # 400, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 410 | TSS Error: 1.1609 | Correct: 0.7500 | RMS Error: 0.2694
Epoch # 410, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 420 | TSS Error: 1.0810 | Correct: 0.7500 | RMS Error: 0.2599
Epoch # 420, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 430 | TSS Error: 1.0807 | Correct: 0.7500 | RMS Error: 0.2599
Epoch # 430, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 440 | TSS Error: 1.0988 | Correct: 0.7500 | RMS Error: 0.2621
Epoch # 440, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 450 | TSS Error: 1.0875 | Correct: 0.7500 | RMS Error: 0.2607
Epoch # 450, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 460 | TSS Error: 1.1176 | Correct: 0.7500 | RMS Error: 0.2643
Epoch # 460, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 470 | TSS Error: 1.0460 | Correct: 0.7500 | RMS Error: 0.2557
Epoch # 470, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 480 | TSS Error: 1.0644 | Correct: 0.7500 | RMS Error: 0.2579
Epoch # 480, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 490 | TSS Error: 1.0202 | Correct: 0.7500 | RMS Error: 0.2525
Epoch # 490, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 500 | TSS Error: 1.0463 | Correct: 0.7500 | RMS Error: 0.2557
Epoch # 500, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 510 | TSS Error: 1.0889 | Correct: 0.7500 | RMS Error: 0.2609
Epoch # 510, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 520 | TSS Error: 2.6600 | Correct: 0.5625 | RMS Error: 0.4077
Epoch # 520, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 530 | TSS Error: 1.0483 | Correct: 0.7500 | RMS Error: 0.2560
Epoch # 530, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 540 | TSS Error: 1.1120 | Correct: 0.7500 | RMS Error: 0.2636
Epoch # 540, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 550 | TSS Error: 1.1609 | Correct: 0.7500 | RMS Error: 0.2694
Epoch # 550, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 560 | TSS Error: 1.0676 | Correct: 0.7500 | RMS Error: 0.2583
Epoch # 560, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 570 | TSS Error: 1.0444 | Correct: 0.7500 | RMS Error: 0.2555
Epoch # 570, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 580 | TSS Error: 1.0963 | Correct: 0.7500 | RMS Error: 0.2618
Epoch # 580, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 590 | TSS Error: 1.1505 | Correct: 0.7500 | RMS Error: 0.2682
Epoch # 590, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 600 | TSS Error: 1.1710 | Correct: 0.7500 | RMS Error: 0.2705
Epoch # 600, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 610 | TSS Error: 1.0690 | Correct: 0.7500 | RMS Error: 0.2585
Epoch # 610, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 620 | TSS Error: 1.0507 | Correct: 0.7500 | RMS Error: 0.2563
Epoch # 620, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 630 | TSS Error: 1.0654 | Correct: 0.7500 | RMS Error: 0.2580
Epoch # 630, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 640 | TSS Error: 1.0435 | Correct: 0.7500 | RMS Error: 0.2554
Epoch # 640, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 650 | TSS Error: 1.0580 | Correct: 0.7500 | RMS Error: 0.2571
Epoch # 650, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 660 | TSS Error: 1.0934 | Correct: 0.7500 | RMS Error: 0.2614
Epoch # 660, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 670 | TSS Error: 1.0454 | Correct: 0.7500 | RMS Error: 0.2556
Epoch # 670, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 680 | TSS Error: 1.0949 | Correct: 0.7500 | RMS Error: 0.2616
Epoch # 680, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 690 | TSS Error: 1.0731 | Correct: 0.7500 | RMS Error: 0.2590
Epoch # 690, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 700 | TSS Error: 1.1536 | Correct: 0.7500 | RMS Error: 0.2685
Epoch # 700, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 710 | TSS Error: 1.1000 | Correct: 0.7500 | RMS Error: 0.2622
Epoch # 710, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 720 | TSS Error: 1.0454 | Correct: 0.7500 | RMS Error: 0.2556
Epoch # 720, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 730 | TSS Error: 1.1010 | Correct: 0.7500 | RMS Error: 0.2623
Epoch # 730, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 740 | TSS Error: 1.0790 | Correct: 0.7500 | RMS Error: 0.2597
Epoch # 740, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 750 | TSS Error: 1.0407 | Correct: 0.7500 | RMS Error: 0.2550
Epoch # 750, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 760 | TSS Error: 1.0741 | Correct: 0.7500 | RMS Error: 0.2591
Epoch # 760, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 770 | TSS Error: 1.0780 | Correct: 0.7500 | RMS Error: 0.2596
Epoch # 770, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 780 | TSS Error: 1.0244 | Correct: 0.7500 | RMS Error: 0.2530
Epoch # 780, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 790 | TSS Error: 1.0371 | Correct: 0.7500 | RMS Error: 0.2546
Epoch # 790, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 800 | TSS Error: 1.0869 | Correct: 0.7500 | RMS Error: 0.2606
Epoch # 800, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 810 | TSS Error: 1.0703 | Correct: 0.7500 | RMS Error: 0.2586
Epoch # 810, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 820 | TSS Error: 1.0353 | Correct: 0.7500 | RMS Error: 0.2544
Epoch # 820, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Traceback (most recent call last):
File "<pyshell#25>", line 1, in -toplevel-
train()
File "C:\Documents and Settings\compsci\Desktop\Code\test.py", line 35, in train
n.train()
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1553, in train
(tssErr, totalCorrect, totalCount, totalPCorrect) = self.sweep()
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1682, in sweep
(error, correct, total, pcorrect) = self.step( **datum )
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1642, in step
self.change_weights() # else change weights in sweep
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 2038, in change_weights
connection.toLayer.numConnects))
File "C:\Python24\Lib\site-packages\Numeric\Numeric.py", line 175, in put
multiarray.put (a, ind, array(v, copy=0).astype(a.typecode()))
KeyboardInterrupt
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 6
Epoch # 10 | TSS Error: 3.1841 | Correct: 0.7500 | RMS Error: 0.4461
Epoch # 10, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 20 | TSS Error: 1.6865 | Correct: 0.6875 | RMS Error: 0.3247
Epoch # 20, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 30 | TSS Error: 0.1423 | Correct: 1.0000 | RMS Error: 0.0943
Epoch # 30, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
Final # 30 | TSS Error: 0.1423 | Correct: 1.0000 | RMS Error: 0.0943
Final # 30, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 2 X 1 2 X 2 X 1 1 2 X 1 1
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 1 X 1 X 1 X 1 X 3 1 X 1 X 1 X 1 X
# wrong: 20
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 6
Epoch # 10 | TSS Error: 2.6625 | Correct: 0.4375 | RMS Error: 0.4079
Epoch # 10, Layer = 'output' | Units: 0.4375 | Patterns: 0.0000
Epoch # 20 | TSS Error: 2.6162 | Correct: 0.5625 | RMS Error: 0.4044
Epoch # 20, Layer = 'output' | Units: 0.5625 | Patterns: 0.0000
Epoch # 30 | TSS Error: 1.1824 | Correct: 0.7500 | RMS Error: 0.2718
Epoch # 30, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 40 | TSS Error: 1.1057 | Correct: 0.7500 | RMS Error: 0.2629
Epoch # 40, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 50 | TSS Error: 1.1812 | Correct: 0.7500 | RMS Error: 0.2717
Epoch # 50, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 60 | TSS Error: 0.3309 | Correct: 0.8750 | RMS Error: 0.1438
Epoch # 60, Layer = 'output' | Units: 0.8750 | Patterns: 0.7500
----------------------------------------------------
Final # 62 | TSS Error: 0.2669 | Correct: 1.0000 | RMS Error: 0.1292
Final # 62, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 2 X 2 X 2 X 2 X 1 2 X 2 X 2 X 2 X
Shape 2 2 2 0 X 0 X 2 2 0 X 0 X 2 0 X
Shape 3 3 2 X 1 X 1 X 2 X 3 2 X 1 X 1 X 2 X
# wrong: 21
>>>
>>> train()
# shapes = 4
tolerance = 0.3
hidden = 6
Epoch # 10 | TSS Error: 3.0838 | Correct: 0.7500 | RMS Error: 0.4390
Epoch # 10, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 20 | TSS Error: 2.6365 | Correct: 0.5625 | RMS Error: 0.4059
Epoch # 20, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 30 | TSS Error: 2.5394 | Correct: 0.5625 | RMS Error: 0.3984
Epoch # 30, Layer = 'output' | Units: 0.5625 | Patterns: 0.0000
Epoch # 40 | TSS Error: 2.8459 | Correct: 0.5625 | RMS Error: 0.4217
Epoch # 40, Layer = 'output' | Units: 0.5625 | Patterns: 0.0000
Epoch # 50 | TSS Error: 1.2280 | Correct: 0.7500 | RMS Error: 0.2770
Epoch # 50, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 60 | TSS Error: 1.1319 | Correct: 0.7500 | RMS Error: 0.2660
Epoch # 60, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 70 | TSS Error: 2.3578 | Correct: 0.5625 | RMS Error: 0.3839
Epoch # 70, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 80 | TSS Error: 2.2626 | Correct: 0.5625 | RMS Error: 0.3760
Epoch # 80, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 90 | TSS Error: 1.2415 | Correct: 0.7500 | RMS Error: 0.2786
Epoch # 90, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 100 | TSS Error: 1.2531 | Correct: 0.7500 | RMS Error: 0.2799
Epoch # 100, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 110 | TSS Error: 1.2882 | Correct: 0.8125 | RMS Error: 0.2837
Epoch # 110, Layer = 'output' | Units: 0.8125 | Patterns: 0.5000
Epoch # 120 | TSS Error: 1.2938 | Correct: 0.8125 | RMS Error: 0.2844
Epoch # 120, Layer = 'output' | Units: 0.8125 | Patterns: 0.5000
Epoch # 130 | TSS Error: 1.8842 | Correct: 0.7500 | RMS Error: 0.3432
Epoch # 130, Layer = 'output' | Units: 0.7500 | Patterns: 0.2500
Epoch # 140 | TSS Error: 2.5703 | Correct: 0.5625 | RMS Error: 0.4008
Epoch # 140, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 150 | TSS Error: 2.6805 | Correct: 0.5625 | RMS Error: 0.4093
Epoch # 150, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 160 | TSS Error: 1.2711 | Correct: 0.7500 | RMS Error: 0.2819
Epoch # 160, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 170 | TSS Error: 2.2334 | Correct: 0.5625 | RMS Error: 0.3736
Epoch # 170, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 180 | TSS Error: 2.1728 | Correct: 0.5000 | RMS Error: 0.3685
Epoch # 180, Layer = 'output' | Units: 0.5000 | Patterns: 0.0000
Epoch # 190 | TSS Error: 2.7463 | Correct: 0.4375 | RMS Error: 0.4143
Epoch # 190, Layer = 'output' | Units: 0.4375 | Patterns: 0.0000
Epoch # 200 | TSS Error: 2.1377 | Correct: 0.6875 | RMS Error: 0.3655
Epoch # 200, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 210 | TSS Error: 3.3574 | Correct: 0.1875 | RMS Error: 0.4581
Epoch # 210, Layer = 'output' | Units: 0.1875 | Patterns: 0.0000
Epoch # 220 | TSS Error: 2.0997 | Correct: 0.5625 | RMS Error: 0.3623
Epoch # 220, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 230 | TSS Error: 3.3456 | Correct: 0.3750 | RMS Error: 0.4573
Epoch # 230, Layer = 'output' | Units: 0.3750 | Patterns: 0.0000
Epoch # 240 | TSS Error: 2.1284 | Correct: 0.5625 | RMS Error: 0.3647
Epoch # 240, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 250 | TSS Error: 2.0898 | Correct: 0.5625 | RMS Error: 0.3614
Epoch # 250, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 260 | TSS Error: 2.1904 | Correct: 0.6250 | RMS Error: 0.3700
Epoch # 260, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 270 | TSS Error: 2.1119 | Correct: 0.6250 | RMS Error: 0.3633
Epoch # 270, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 280 | TSS Error: 3.4259 | Correct: 0.1875 | RMS Error: 0.4627
Epoch # 280, Layer = 'output' | Units: 0.1875 | Patterns: 0.0000
Epoch # 290 | TSS Error: 2.0545 | Correct: 0.6875 | RMS Error: 0.3583
Epoch # 290, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 300 | TSS Error: 2.1493 | Correct: 0.6875 | RMS Error: 0.3665
Epoch # 300, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 310 | TSS Error: 2.1279 | Correct: 0.6250 | RMS Error: 0.3647
Epoch # 310, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 320 | TSS Error: 2.1853 | Correct: 0.5625 | RMS Error: 0.3696
Epoch # 320, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 330 | TSS Error: 2.1562 | Correct: 0.6250 | RMS Error: 0.3671
Epoch # 330, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 340 | TSS Error: 2.1224 | Correct: 0.5625 | RMS Error: 0.3642
Epoch # 340, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 350 | TSS Error: 2.1093 | Correct: 0.6250 | RMS Error: 0.3631
Epoch # 350, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 360 | TSS Error: 2.1472 | Correct: 0.5000 | RMS Error: 0.3663
Epoch # 360, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 370 | TSS Error: 2.0972 | Correct: 0.6875 | RMS Error: 0.3620
Epoch # 370, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 380 | TSS Error: 2.1226 | Correct: 0.5625 | RMS Error: 0.3642
Epoch # 380, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 390 | TSS Error: 2.0827 | Correct: 0.5625 | RMS Error: 0.3608
Epoch # 390, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 400 | TSS Error: 2.1144 | Correct: 0.6875 | RMS Error: 0.3635
Epoch # 400, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 410 | TSS Error: 2.1673 | Correct: 0.6875 | RMS Error: 0.3680
Epoch # 410, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 420 | TSS Error: 2.0885 | Correct: 0.6875 | RMS Error: 0.3613
Epoch # 420, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 430 | TSS Error: 2.0913 | Correct: 0.6875 | RMS Error: 0.3615
Epoch # 430, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 440 | TSS Error: 2.0479 | Correct: 0.6250 | RMS Error: 0.3578
Epoch # 440, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 450 | TSS Error: 3.3693 | Correct: 0.3750 | RMS Error: 0.4589
Epoch # 450, Layer = 'output' | Units: 0.3750 | Patterns: 0.0000
Epoch # 460 | TSS Error: 2.1147 | Correct: 0.5000 | RMS Error: 0.3635
Epoch # 460, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 470 | TSS Error: 2.1514 | Correct: 0.5625 | RMS Error: 0.3667
Epoch # 470, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 480 | TSS Error: 2.1192 | Correct: 0.5625 | RMS Error: 0.3639
Epoch # 480, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 490 | TSS Error: 2.1521 | Correct: 0.5625 | RMS Error: 0.3668
Epoch # 490, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 500 | TSS Error: 3.4058 | Correct: 0.4375 | RMS Error: 0.4614
Epoch # 500, Layer = 'output' | Units: 0.4375 | Patterns: 0.0000
Epoch # 510 | TSS Error: 2.1404 | Correct: 0.6875 | RMS Error: 0.3658
Epoch # 510, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 520 | TSS Error: 2.1180 | Correct: 0.5625 | RMS Error: 0.3638
Epoch # 520, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 530 | TSS Error: 2.1269 | Correct: 0.5625 | RMS Error: 0.3646
Epoch # 530, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 540 | TSS Error: 3.3854 | Correct: 0.3750 | RMS Error: 0.4600
Epoch # 540, Layer = 'output' | Units: 0.3750 | Patterns: 0.0000
Epoch # 550 | TSS Error: 3.3568 | Correct: 0.3125 | RMS Error: 0.4580
Epoch # 550, Layer = 'output' | Units: 0.3125 | Patterns: 0.0000
Epoch # 560 | TSS Error: 2.1287 | Correct: 0.6875 | RMS Error: 0.3648
Epoch # 560, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 570 | TSS Error: 2.1588 | Correct: 0.5625 | RMS Error: 0.3673
Epoch # 570, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 580 | TSS Error: 2.1589 | Correct: 0.6875 | RMS Error: 0.3673
Epoch # 580, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 590 | TSS Error: 2.1548 | Correct: 0.5625 | RMS Error: 0.3670
Epoch # 590, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 600 | TSS Error: 2.1494 | Correct: 0.5625 | RMS Error: 0.3665
Epoch # 600, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 610 | TSS Error: 3.4346 | Correct: 0.4375 | RMS Error: 0.4633
Epoch # 610, Layer = 'output' | Units: 0.4375 | Patterns: 0.0000
Epoch # 620 | TSS Error: 2.2122 | Correct: 0.5625 | RMS Error: 0.3718
Epoch # 620, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 630 | TSS Error: 2.1604 | Correct: 0.7500 | RMS Error: 0.3675
Epoch # 630, Layer = 'output' | Units: 0.7500 | Patterns: 0.2500
Epoch # 640 | TSS Error: 3.3468 | Correct: 0.3750 | RMS Error: 0.4574
Epoch # 640, Layer = 'output' | Units: 0.3750 | Patterns: 0.0000
Epoch # 650 | TSS Error: 2.1096 | Correct: 0.6250 | RMS Error: 0.3631
Epoch # 650, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 660 | TSS Error: 3.4174 | Correct: 0.1875 | RMS Error: 0.4622
Epoch # 660, Layer = 'output' | Units: 0.1875 | Patterns: 0.0000
Epoch # 670 | TSS Error: 2.1340 | Correct: 0.5625 | RMS Error: 0.3652
Epoch # 670, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 680 | TSS Error: 2.1075 | Correct: 0.5625 | RMS Error: 0.3629
Epoch # 680, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 690 | TSS Error: 2.0986 | Correct: 0.6250 | RMS Error: 0.3622
Epoch # 690, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 700 | TSS Error: 2.1482 | Correct: 0.5000 | RMS Error: 0.3664
Epoch # 700, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Epoch # 710 | TSS Error: 2.1199 | Correct: 0.6250 | RMS Error: 0.3640
Epoch # 710, Layer = 'output' | Units: 0.6250 | Patterns: 0.2500
Epoch # 720 | TSS Error: 2.1808 | Correct: 0.5625 | RMS Error: 0.3692
Epoch # 720, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 730 | TSS Error: 2.1379 | Correct: 0.6875 | RMS Error: 0.3655
Epoch # 730, Layer = 'output' | Units: 0.6875 | Patterns: 0.2500
Epoch # 740 | TSS Error: 2.1196 | Correct: 0.5625 | RMS Error: 0.3640
Epoch # 740, Layer = 'output' | Units: 0.5625 | Patterns: 0.2500
Epoch # 750 | TSS Error: 2.2049 | Correct: 0.5000 | RMS Error: 0.3712
Epoch # 750, Layer = 'output' | Units: 0.5000 | Patterns: 0.2500
Traceback (most recent call last):
File "<pyshell#30>", line 1, in -toplevel-
train()
File "C:\Documents and Settings\compsci\Desktop\Code\test.py", line 35, in train
n.train()
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1553, in train
(tssErr, totalCorrect, totalCount, totalPCorrect) = self.sweep()
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1682, in sweep
(error, correct, total, pcorrect) = self.step( **datum )
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1642, in step
self.change_weights() # else change weights in sweep
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 2038, in change_weights
connection.toLayer.numConnects))
File "C:\Python24\lib\site-packages\pyrobot\brain\conx.py", line 1996, in deltaWeight
newDweight = e * wed + m * dweightLast # gradient descent
KeyboardInterrupt
>>>
>>>
>>>
>>> train()
# shapes = 4
tolerance = 0.5
hidden = 5
Epoch # 10 | TSS Error: 3.1776 | Correct: 0.7500 | RMS Error: 0.4456
Epoch # 10, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 20 | TSS Error: 2.9743 | Correct: 0.7500 | RMS Error: 0.4312
Epoch # 20, Layer = 'output' | Units: 0.7500 | Patterns: 0.0000
Epoch # 30 | TSS Error: 0.8893 | Correct: 0.9375 | RMS Error: 0.2358
Epoch # 30, Layer = 'output' | Units: 0.9375 | Patterns: 0.7500
----------------------------------------------------
Final # 31 | TSS Error: 0.8255 | Correct: 1.0000 | RMS Error: 0.2271
Final # 31, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 3 X 3 X 2 X 3 X 1 3 X 2 X 3 X 3 X
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 3 3 3 3 3 3 3 3 3
# wrong: 16
>>>
>>> train()
# shapes = 4
tolerance = 0.9
hidden = 5
----------------------------------------------------
Final # 1 | TSS Error: 4.2167 | Correct: 1.0000 | RMS Error: 0.5134
Final # 1, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 2 X 2 X 2 X 2 X 2 X 2 X 2 X 2 X 2 X 2 X
Shape 1 0 X 0 X 0 X 0 X 0 X 0 X 2 X 0 X 0 X 2 X
Shape 2 0 X 2 2 2 2 0 X 2 2 2 2
Shape 3 0 X 0 X 0 X 0 X 0 X 0 X 0 X 0 X 0 X 0 X
# wrong: 32
>>>
>>> train()
# shapes = 4
tolerance = 0.2
hidden = 5
Epoch # 10 | TSS Error: 3.1587 | Correct: 0.0000 | RMS Error: 0.4443
Epoch # 10, Layer = 'output' | Units: 0.0000 | Patterns: 0.0000
Epoch # 20 | TSS Error: 1.7330 | Correct: 0.4375 | RMS Error: 0.3291
Epoch # 20, Layer = 'output' | Units: 0.4375 | Patterns: 0.0000
Epoch # 30 | TSS Error: 1.2656 | Correct: 0.6875 | RMS Error: 0.2812
Epoch # 30, Layer = 'output' | Units: 0.6875 | Patterns: 0.5000
Epoch # 40 | TSS Error: 1.2743 | Correct: 0.7500 | RMS Error: 0.2822
Epoch # 40, Layer = 'output' | Units: 0.7500 | Patterns: 0.5000
Epoch # 50 | TSS Error: 0.7488 | Correct: 0.8125 | RMS Error: 0.2163
Epoch # 50, Layer = 'output' | Units: 0.8125 | Patterns: 0.5000
Epoch # 60 | TSS Error: 0.1610 | Correct: 0.9375 | RMS Error: 0.1003
Epoch # 60, Layer = 'output' | Units: 0.9375 | Patterns: 0.7500
Epoch # 70 | TSS Error: 0.1066 | Correct: 1.0000 | RMS Error: 0.0816
Epoch # 70, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
Final # 70 | TSS Error: 0.1066 | Correct: 1.0000 | RMS Error: 0.0816
Final # 70, Layer = 'output' | Units: 1.0000 | Patterns: 1.0000
----------------------------------------------------
>>> test()
Variations 0 1 2 3 4 5 6 7 8 9
Shape 0 0 0 0 0 0 0 0 0 0 0
Shape 1 1 2 X 2 X 2 X 2 X 1 2 X 2 X 2 X 2 X
Shape 2 2 0 X 0 X 0 X 0 X 2 0 X 0 X 0 X 0 X
Shape 3 3 2 X 2 X 2 X 2 X 3 2 X 2 X 2 X 2 X
# wrong: 24
