Manual Bias

Parameters

title	Manual Bias
class	class vsoc.training.BatchSizeTraining$
learningRate	1.0E-04
trainingData	playerpos_x A 500000
batchSizeTrainingDataRelative	0.10
testData	playerpos_x B 1000
iterations	200
optAlgo	STOCHASTIC_GRADIENT_DESCENT
numHiddenNodes	100, 200, 300, 500
regularisation	None
seed	-8876029723576768028, -3375934931721753594, 1696412033065026326, 6283369899114212938, -3587145959788761006, -49633614034392359, 839473069744556995, 2684180910583920968, -7386426029039824287, 3837550597042215702, -4997332816031131463, 3815956045927450641, -3403545420327942156, 3661589999326108919, 2455182172019959569, 3162067559174869087, -1669667726484573068, -397364268861304359, -7913912163456363611, 7909811908685690708, 2311254609887171408, 8435778613472697536, 504319445395541907, -433047265908778508, -5423116490601089927, 8381378201148666576, -6904400535536458560, -4437293041802599002, 5946303504748415202, 7150743982293367765, 2242790324642662994, -3814375723625398089

Manually adopting the output bias leads to the following result.



Manually added bias values

A_100	-1.1
A_200	-1.35
A_300	-1.5
A_500	1.5
B_100	-0.8
B_200	1.7
B_300	1.6
B_500	1.6
C_100	-1.0
C_200	-1.5
C_300	1.4
C_500	1.6

Conclusion

Manually adopting the output bias leads to better results

Open question

Why does the learning algorithm not minimize the output bias ?