问调整神经网络的大小参数

EN

> 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.