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buaabai avatar buaabai commented on June 15, 2024

a应该是论文中的scaling factor,也就是代码中的alpha,每层权重应该对应着同一个alpha,将每层的alpha提取出来就可以得到(-1,0,1)这样的权重了。
a is supposed to be the scaling factor in the paper, which is corresponded by alpha in codes. each layer has its own alpha, u can divide weights by alpha to get weights like (-1, 0, 1).

from ternary-weights-network.

manwu1994 avatar manwu1994 commented on June 15, 2024

感谢回复!明白了!但是好像不是每一层对应同一个alpha,而是每一个神经元对应一个alpha,因此想问一下您有试过提取alpha参数吗?非常感谢!

from ternary-weights-network.

manwu1994 avatar manwu1994 commented on June 15, 2024

感谢回复!明白了!但是好像不是每一层对应同一个alpha,而是每一个神经元对应一个alpha,因此想问一下您有试过提取alpha参数吗?非常感谢!

嗯嗯不好意思我理解有误,是一层对应一个alpha,那如果要获取(-1,0,1)的权重,还得提取delta的参数,对吧?谢谢您的时间!

from ternary-weights-network.

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