http://mxnet.incubator.apache.org/versions/master/install/index.html?platform=Windows&language=R&processor=CPU

1 cran <- getOption("repos")
2 cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/"
3 options(repos = cran)
4 install.packages("mxnet")

安装之前需要指定repository

一起安装的包

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额外的依赖

To run MXNet you also should have OpenCV and OpenBLAS installed.

第一步：数据准备

1 set.seed(0)
2 #随机分配训练集和测试集
3 train.ind = sample(1:nrow(inp), size=ceiling(0.7*nrow(inp)))
4 
5 train.x = data.matrix(inp[train.ind,NIRDATA])
6 train.y = inp[train.ind,NIC]
7 test.x = data.matrix(inp[-train.ind,NIRDATA])
8 test.y = inp[-train.ind,NIC]

第二步：创建网络并训练

1 mx.set.seed(0)
2 
3 model <- mx.mlp(train.x, train.y, hidden_node=c(7), out_node=1, out_activation="rmse",
4                 num.round=2000, array.batch.size=15, learning.rate=0.05, momentum=0.9,
5                 eval.metric=mx.metric.rmse)

hidden_node接受向量，c(100,50)代表两层隐含层，分别具有100和50个节点

out_node输出层

eval.metric=mx.metric.rmse
评估方法，rmse 标准差

评估测试集
predict(model,test.x)->prd

plot(prd,test.y)