神经网络

作者

[编辑] 郑虎;

[审核] .

修改于

2026-04-21

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注记

Hiplot 网站

本页面为 Hiplot Neural Network 插件的源码版本教程，您也可以使用 Hiplot 网站实现无代码绘图，更多信息请查看以下链接:

https://hiplot.cn/basic/neural-network?lang=zh_cn

环境配置

系统: Cross-platform (Linux/MacOS/Windows)
编程语言: R
依赖包: data.table; jsonlite; NeuralNetTools; nnet

# 安装包
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("NeuralNetTools", quietly = TRUE)) {
  install.packages("NeuralNetTools")
}
if (!requireNamespace("nnet", quietly = TRUE)) {
  install.packages("nnet")
}

# 加载包
library(data.table)
library(jsonlite)
library(NeuralNetTools)
library(nnet)

sessioninfo::session_info("attached")

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.3 (2026-03-11)
 os       Ubuntu 24.04.4 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-04-21
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.9.37 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package        * version  date (UTC) lib source
 data.table     * 1.18.2.1 2026-01-27 [1] RSPM
 jsonlite       * 2.0.0    2025-03-27 [1] RSPM
 NeuralNetTools * 1.5.3    2022-01-06 [1] RSPM
 nnet           * 7.3-20   2025-01-01 [3] CRAN (R 4.5.3)

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.3/lib/R/site-library
 [3] /opt/R/4.5.3/lib/R/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

数据准备

# 加载数据
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/neural-network/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)

# 查看数据
head(data)

         Y1        Y2          X1         X2         X3
1 0.7646258 0.5494452 -0.89691455 -1.8923489  0.6408445
2 0.2383994 0.4605024  0.18484918  1.2928042 -1.6013778
3 0.3800247 0.2527468  1.58784533 -0.6182543 -0.7778154
4 0.3545279 0.6319730 -1.13037567  1.0409383 -1.6473925
5 0.3667356 0.4684437 -0.08025176  1.1758795  0.1542662
6 0.5509560 0.4439474  0.13242028 -1.5018321 -1.1756313

可视化

# 神经网络
mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 10,
            maxint = 100, decay = 0)

# weights:  51
initial  value 57.109907 
iter  10 value 0.381450
iter  20 value 0.077735
iter  30 value 0.052872
iter  40 value 0.038199
iter  50 value 0.027784
iter  60 value 0.017599
iter  70 value 0.013435
iter  80 value 0.010025
iter  90 value 0.006170
iter 100 value 0.004559
final  value 0.004559 
stopped after 100 iterations

# plot
par(mar = numeric(4))
plotnet(mod)