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  • apache mxnet 深度学习神经网络小试

    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

    package ‘brew’ successfully unpacked and MD5 sums checked
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    package ‘mxnet’ successfully unpacked and MD5 sums checked

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