我正在尝试使用darch包来创建几个堆叠RBM的dbn。我是深度学习场景的新手，所以我的问题是：glmnet / randomForest / knn ... etc包中的预测函数等价于什么？
训练dbn后，如何预测外部样本？例如（这是包中提供的示例）
## Not run:
# Generating the datasets
inputs <- matrix(c(0,0,0,1,1,0,1,1),ncol=2,byrow=TRUE)
outputs <- matrix(c(0,1,1,0),nrow=4)
# Generating the darch
darch <- newDArch(c(2,4,1),batchSize=2)
# Pre-Train the darch
darch <- preTrainDArch(darch,inputs,maxEpoch=1000)
# Prepare the layers for backpropagation training for
# backpropagation training the layer functions must be
# set to the unit functions which calculates the also
# derivatives of the function result.
layers <- getLayers(darch)
for(i in length(layers):1){
layers[[i]][[2]] <- sigmoidUnitDerivative
}
setLayers(darch) <- layers
rm(layers)
# Setting and running the Fine-Tune function
setFineTuneFunction(darch) <- backpropagation
darch <- fineTuneDArch(darch,inputs,outputs,maxEpoch=1000)
# Running the darch
darch <- darch <- getExecuteFunction(darch)(darch,inputs)
outputs <- getExecOutputs(darch)
cat(outputs[[length(outputs)]])
现在假设我们有
inputsTest <- matrix(c(0,1,0,0,0,0,1,1),ncol=2,byrow=TRUE)
如何获得输出？
另外，有人可以解释这一行的作用：
darch <- darch <- getExecuteFunction(darch)(darch,inputs)