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[英]neuralnet package in R - how to obtain weights prior to training convergence?
[英]how to initialize weights with the neuralnet package?
我在R中使用Neuronet软件包,但是当我想为网络初始化一定数量的初始权重时遇到问题。 我尝试根据从默认的随机权重生成的结果中得出的结果来做,但是一点都不幸运。
这是我应该初始权重的部分:
weigths<-c(-0.3,0.2,
0.2,0.05,
0,2,-0.1,
-0.1,0.2,0.2)
net=neuralnet(to~x1+x2,tdata,hidden=2,threshold=0.01,constant.weights=weights)
因为我正在考虑权重遵循以下模式:
Intercept.to.1layhid1 -5.0556934519949
x1.to.1layhid1 10.9208362719511
x2.to.1layhid1 12.9996270590530
Intercept.to.1layhid2 3.7047601228351
x1.to.1layhid2 -2.5636252939619
x2.to.1layhid2 -2.5759077405754
Intercept.to.to -1.6494794336705
1layhid.1.to.to 1.3502874764968
1layhid.2.to.to 1.6969811621181
但是当我应用它时,我得到了错误:
Error in constant.weights != 0
有什么帮助吗?
谢谢
您正在寻找startweights
参数来初始化自定义权重。 在文档中:
help(neuralnet)
startweights:
a vector containing starting values for the weights.
The weights will not be randomly initialized.
constant.weights
用于指定固定的权重,这些权重是您通过exclude
聚合来exclude
。
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