RForML之焦点包：nnet

神经网络是深度进修的基本。<br /> 本文先容R做呆板进修焦点包：nnet</p> <p>### 一、nnet包先容<br /> nnet包实现了前馈神经网络和多项对数线性模子。<br /> 前馈神经网络是一种常用的神经网络布局，如下图所示。<br /> ![](http://img.shujuren.org/pictures/QU/5869fe530bacf.jpg)<br /> >**前馈网络**中各个神经元按接管信息的先后分为差异的组。每一组可以看<br /> 作一个神经层。每一层中的神经元接管前一层神经元的输出，并输出到下一层<br /> 神经元。整个网络中的信息是朝一个偏向流传，没有反向的信息流传。前馈网<br /> 络可以用一个有向无环路图暗示。前馈网络可以看作一个函数，通过简朴非线<br /> 性函数的多次复合，实现输入空间到输出空间的巨大映射。这种网络布局简朴，<br /> 易于实现。前馈网络包罗全毗连前馈网络和卷积神经网络等</p> <p>### 二、nnet包安装和加载<br /> R代码<br /> “`{r}<br /> if(!suppressWarnings(require(‘nnet’)))<br /> {<br /> install.packages(‘nnet’)<br /> require(‘nnet’)<br /> }<br /> “`</p> <p>### 三、nnet包应用<br /> #### 第一步：数据获取<br /> “`{r}<br /> ##预测变量X<br /> ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3])<br /> ##方针变量y<br /> targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)) )<br /> “`<br /> #### 第二步：数据集分别（练习集+验证集）<br /> “`{r}<br /> set.seed(1234)<br /> ##70%的数据集作为练习模子<br /> samp <- c(sample(1:50,35), sample(51:100,35), sample(101:150,35))<br /> ir.train <- ir[samp,] targets.train <- targets[samp,] ir.validation <- ir[-samp,] targets.validation <- targets[-samp, ] “`<br /> #### 第三步:模子构建<br /> “`{r}<br /> ir.nnet <- nnet(ir.train, targets.train, size = 2, rang = 0.1,<br /> decay = 5e-4, maxit = 200)<br /> “`<br /> 功效如下：<br /> >weights: 19<br /> initial value 80.747493<br /> iter 10 value 35.797549<br /> iter 20 value 35.256099<br /> iter 30 value 35.040425<br /> iter 40 value 29.509802<br /> iter 50 value 25.348691<br /> iter 60 value 24.081465<br /> iter 70 value 23.873708<br /> iter 80 value 23.828264<br /> iter 90 value 23.790097<br /> iter 100 value 23.753690<br /> iter 110 value 23.733482<br /> iter 120 value 17.479635<br /> iter 130 value 2.655936<br /> iter 140 value 2.124963<br /> iter 150 value 1.707239<br /> iter 160 value 1.289882<br /> iter 170 value 0.938505<br /> iter 180 value 0.755850<br /> iter 190 value 0.698843<br /> iter 200 value 0.678346<br /> final value 0.678346<br /> stopped after 200 iterations</p> <p>#### 第四步：模子应用<br /> “`{r}<br /> test.cl <- function(true, pred) {<br /> true <- max.col(true)<br /> cres <- max.col(pred)<br /> table(true, cres)<br /> }</p> <p>test.cl(targets.validation, predict(ir.nnet, ir.validation))<br /> “`<br /> 功效如下：<br /> ![](http://img.shujuren.org/pictures/HY/586a071665ed1.jpg)</p> <p>### 参考资料<br /> 1 前馈神经网络：https://nndl.github.io/ch5.pdf<br /> 2 nnet函数：https://www.rdocumentation.org/packages/nnet/versions/7.3-12/topics/nnet</p> <p>您在阅读中，有什么发起可能想法，请留言。<br /> 假如您以为本文有收获，请小额赞赏，让我有动力继承写出高质量的文章。</p> <p>![](http://img.shujuren.org/pictures/S2/5867988ca85ca.png)</p> </div> </article> <div class="tagcloud"><ul class='wp-tag-cloud' role='list'><h3>关键字：</h3> </ul> </div> <div class="col-sm-6 col-box bg-gray-light-2 contact-form"> <div role="form" class="wpcf7" id="wpcf7-f17097-o1" lang="zh-CN" dir="ltr"> <div class="screen-reader-response"></div> <form action="/rforml%e4%b9%8b%e7%84%a6%e7%82%b9%e5%8c%85%ef%bc%9annet/#wpcf7-f17097-o1" method="post" class="wpcf7-form" 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