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我想使用 caret 包拟合神经网络模型。有 208 个预测变量，所有这些变量都很重要，不能丢弃。我可以给 size 参数的最大值是 4，超过这个值我会得到一个错误，说有太多的权重。

> ctrl<-trainControl(method = 'cv',number = 5)
> my.grid <- expand.grid(.decay = 0.1, .size =5)
> nn.fit <- train(train_predictors,train_responses[["r2c1"]],method = "nnet",algorithm = 'backprop', tuneGrid = my.grid,trace=F, linout = TRUE,trControl = ctrl)
Something is wrong; all the RMSE metric values are missing:
      RMSE        Rsquared        MAE     
 Min.   : NA   Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA   Max.   : NA  
 NA's   :1     NA's   :1     NA's   :1    
Error: Stopping
In addition: Warning messages:
1: model fit failed for Fold1: decay=0.1, size=5 Error in nnet.default(x, y, w, ...) : too many (1051) weights

2: model fit failed for Fold2: decay=0.1, size=5 Error in nnet.default(x, y, w, ...) : too many (1051) weights

3: model fit failed for Fold3: decay=0.1, size=5 Error in nnet.default(x, y, w, ...) : too many (1051) weights

4: model fit failed for Fold4: decay=0.1, size=5 Error in nnet.default(x, y, w, ...) : too many (1051) weights

5: model fit failed for Fold5: decay=0.1, size=5 Error in nnet.default(x, y, w, ...) : too many (1051) weights

6: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,  :
  There were missing values in resampled performance measures.

该模型在 4 个神经元（大小 = 4）时表现非常糟糕。如果我想要超过 5 个神经元，我该怎么做才能使模型工作？

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2 回答 2

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您始终可以使用...carettrain方法中的可选参数将附加参数传递给基础训练方法（在本例中nnet）。nnet包的 CRAN 文档描述了MaxNwts允许控制隐藏单元的最大数量的参数。

于 2018-06-25T20:16:46.767 回答
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您可以为 nnet 方法指定调整网格中的其他参数。每种方法的可用参数都可以在线获得，但很难找到。这是我将 mxnet 用于 adam nn 的示例：

mxnet_grid_A2 = expand.grid(layer1 = c(10, 12),   
                           layer2 = c(4, 6),
                           layer3 = 2,
                           learningrate = c(0.001, 0.0001),
                           dropout = c(0, 0.2)
                           beta1 = .9,
                           beta2 = 0.999,
                           activation = 'relu')
于 2018-02-13T04:58:27.717 回答