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)
原文：https://www.cnblogs.com/qianheng/p/10850162.html