Question

我正在使用Neuralnet包来训练分类器。训练数据如下所示：

> head(train_data)
   mvar_12      mvar_40 v10       mvar_1   mvar_2  Labels
1 136.51551310       6   0   656.78784220      0      0
2 145.10739860      87   0    14.21413596      0      0
3 194.74940330       4   0   196.62888080      0      0
4 202.38663480       2   0   702.27307720      0      1
5  60.14319809       9   0    -1.00000000     -1      0
6  95.46539380       6   0   539.09479640      0      0

代码如下：

n <- names(train_data)
f <- as.formula(paste("Labels ~", paste(n[!n %in% "Labels"], collapse = " + ")))
library(neuralnet)
nn <- neuralnet(f, tr_nn, hidden = 4, threshold = 0.01,        
                stepmax = 1e+05, rep = 1, 
                lifesign.step = 1000,
                algorithm = "rprop+")

当我尝试对测试集进行预测时出现问题：

pred <- compute(nn, cv_data)

cv_data的样子：

> head(cv_data)
   mvar_12      mvar_40 v10      mvar_1    mvar_2
1 213.84248210       1   9  -1.000000000     -1
2 110.73985680       0   0  -1.000000000     -1
3 152.74463010      14   0 189.521812800     -1
4  64.91646778       7   0  47.854257730     -1
5 141.28878280      12   0 248.557857500      5
6  55.36992840       2   0   4.785425773     -1

对此我得到一个错误说：

Error in nrow[w] * ncol[w] : non-numeric argument to binary operator
In addition: Warning message:
In is.na(weights) : is.na() applied to non-(list or vector) of type 'NULL'

为什么我会收到此错误以及如何解决？

Answer 1

我刚刚遇到了同样的问题。检查compute函数的源代码，我们可以看到它假设一个结果属性（即weights）仅在网络完成培训时定义完美。

> trace("compute",edit=TRUE)
function (x, covariate, rep = 1) {
    nn <- x
    linear.output <- nn$linear.output
    weights <- nn$weights[[rep]]
    [...]
}

我认为真正的问题在于，neuralnet一旦达到stepmax值就不会保存当前网络，导致compute代码中出现此错误。

修改

似乎你可以通过注释 65 ＆amp;行来避免这种重置。 calculate.neuralnet函数的 66

> fixInNamespace("calculate.neuralnet", pos="package:neuralnet") [...] #if (reached.threshold > threshold) # return(result = list(output.vector = NULL, weights = NULL)) [...]

然后一切都像魅力一样：）

Answer 2

尝试将threshold调整为高于0.01的值，或将stepmax调整为超过1e06，或使用0.1的threshold，然后从那里减少它。您还可以添加lifesign = "full"参数，以1000步的增量观察模型创建性能，以真正拨入阈值。这“解决了”我的非二元错误，但模型的准确性，均方误差和其他结果不如直接结果那么令人满意。

Answer 3

执行str（cv_data）并确保它们都是数字。

Answer 4

因为你从未在函数startweights中设置neuralnet() 根据文件

neuralnet(formula, data, hidden = 1, threshold = 0.01,
    stepmax = 1e+05, rep = 1, startweights = NULL,
    learningrate.limit = NULL,
    learningrate.factor = list(minus = 0.5, plus = 1.2),
    learningrate=NULL, lifesign = "none",
    lifesign.step = 1000, algorithm = "rprop+",
    err.fct = "sse", act.fct = "logistic",
    linear.output = TRUE, exclude = NULL,
    constant.weights = NULL, likelihood = FALSE)

startweights            a vector containing starting values for the weights. The weights will not be randomly initialized.

请注意，默认值为NULL，不会随机初始化。尝试在那里放一些东西，看看是否有效。

R：nrow [w] * ncol [w]中的错误：二进制运算符的非数字参数，同时使用Neuralnet包

4 个答案: