我想使用插入符号包来拟合神经网络模型。有208个预测变量,所有这些都很重要,不能丢弃。 我可以给size参数的最大值是4,超过该值我会收到一个错误,说明权重太多。
> 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.
该模型对4个神经元(大小= 4)表现非常糟糕。如果我想拥有超过5个神经元,我该怎么做才能使模型有效?
答案 0 :(得分:1)
您始终可以使用插入符...
方法中的train
可选参数将其他参数传递给基础训练方法(在本例中为nnet
)。 nnet程序包的CRAN文档描述了MaxNwts
参数,该参数可以控制最大隐藏单元数。
答案 1 :(得分:0)
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在线获得,但很难找到。以下是使用mxnet for 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')