使用带有插入符号训练的神经网络并调整参数 [英] Using neuralnet with caret train and adjusting the parameters

问题描述

所以我读过一篇论文，该论文使用神经网络对一个数据集进行建模，该数据集与我目前使用的数据集相似.我有 160 个描述符变量，我想为 160 个案例建模(回归建模).我阅读的论文使用了以下参数:-

So I've read a paper that had used neural networks to model out a dataset which is similar to a dataset I'm currently using. I have 160 descriptor variables that I want to model out for 160 cases (regression modelling). The paper I read used the following parameters:-

'对于每个分割，都为 10 个单独的训练测试折叠中的每一个开发了一个模型.具有 33 个输入神经元和 16 个隐藏神经元的三层反向传播网络用于在线权重更新、0.25 学习率和 0.9 动量.对于每个折叠，从总共 50 个不同的随机初始权重起点进行学习，并且允许网络迭代学习时期，直到验证集的平均绝对误差 (MAE) 达到最小值.'

'For each split, a model was developed for each of the 10 individual train-test folds. A three layer back-propagation net with 33 input neurons and 16 hidden neurons was used with online weight updates, 0.25 learning rate, and 0.9 momentum. For each fold, learning was conducted from a total of 50 different random initial weight starting points and the network was allowed to iterate through learning epochs until the mean absolute error (MAE) for the validation set reached a minimum. '

现在他们使用了一个叫做 Emergent 的专业软件来做到这一点，这是一个非常专业的神经元网络模型软件.但是，由于我之前在 R 中做过以前的模型，所以我必须坚持下去.所以我使用 caret train 函数来进行 10 次交叉验证，使用神经网络包进行 10 次.我做了以下事情:-

Now they used a specialist software called Emergent in order to do this, which is a very specialised neuronal network model software. However, as I've done previous models before in R, I have to keep to it. So I'm using the caret train function in order to do 10 cross fold validation, 10 times with the neuralnet package. I did the following:-

cadets.nn <- train(RT..seconds.~., data = cadet, method = "neuralnet", algorithm = 'backprop', learningrate = 0.25, hidden = 3, trControl = ctrl, linout = TRUE)

我这样做是为了尝试尽可能接近论文中使用的参数调整参数，但是我收到以下错误消息:-

I did this to try and tune the parameters as closely to the ones used in the paper, however I get the following error message:-

  layer1 layer2 layer3 RMSE Rsquared RMSESD RsquaredSD
1      1      0      0  NaN      NaN     NA         NA
2      3      0      0  NaN      NaN     NA         NA
3      5      0      0  NaN      NaN     NA         NA
Error in train.default(x, y, weights = w, ...) : 
  final tuning parameters could not be determined
In addition: There were 50 or more warnings (use warnings() to see the first 50)

你知道我做错了什么吗?它在我做 nnet 时工作，但我无法调整参数以使其类似于我试图模仿的论文中使用的参数.

Do you know what I'm doing wrong? It works when I do nnet, but I can't tune the parameters for that to make it similar to the ones used in the paper I'm trying to mimic.

这是我在警告()中得到的五十次:-

This is what I get in the warnings() fifty times:-

1: In eval(expr, envir, enclos) :
  model fit failed for Fold01.Rep01: layer1=1, layer2=0, layer3=0 Error in neuralnet(form, data = data, hidden = nodes, ...) : 
  formal argument "hidden" matched by multiple actual arguments

2: In data.frame(..., check.names = FALSE) :
  row names were found from a short variable and have been discarded
3: In eval(expr, envir, enclos) :
  model fit failed for Fold01.Rep01: layer1=3, layer2=0, layer3=0 Error in neuralnet(form, data = data, hidden = nodes, ...) : 
  formal argument "hidden" matched by multiple actual arguments

4: In data.frame(..., check.names = FALSE) :
  row names were found from a short variable and have been discarded
5: In eval(expr, envir, enclos) :
  model fit failed for Fold01.Rep01: layer1=5, layer2=0, layer3=0 Error in neuralnet(form, data = data, hidden = nodes, ...) : 
  formal argument "hidden" matched by multiple actual arguments

谢谢！

推荐答案

train 为你设置 hidden (基于 layer 给出的值>-layer3.您试图指定该参数两次，因此:

train sets hidden for you (based on the values given by layer-layer3. You are trying to specify that argument twice, hence:

形参隐藏"与多个实参匹配

formal argument "hidden" matched by multiple actual arguments

HTH,

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