R平台玩深度进修教程

本文先容R平台玩深度进修的内容。</p> <p></p> <p>### R平台深度进修情况构建<br /> #### 第一步：安装Anaconda软件并建设情况r-tensorflow<br /> Anaconda软件安装，请阅读文章《**[Anaconda软件安装和浅易利用教程](http://shujuren.org/article/472.html "Anaconda软件安装和浅易利用教程")**》<br /> 建设情况r-tensorflow的呼吁<br /> “`<br /> conda create –name r-tensorflow python=3.6<br /> “`<br /> **【温馨提示】**为了可以或许在R平台上面利用深度进修框架tensorflow，请必然建设情况r-tensorflow。</p> <p>#### 第二步：在r-tensorflow情况下安装tensorflow和keras<br /> r-tensorflow情况下安装tensorflow和keras，请阅读下面文章。<br /> 1 《**[Anaconda软件安装TensorFlow教程](http://shujuren.org/article/477.html)**》<br /> 2 《**[Anaconda软件安装Keras教程](http://shujuren.org/article/478.html)**》</p> <p>#### 第三步：安装R和RStudio软件<br /> R软件官网：**http://r-project.org**<br /> RStudio软件官网：**http://rstudio.com**</p> <p>#### 第四步：打开RStudio软件安装keras包<br /> R代码<br /> “`<br /> install.packages("keras")<br /> “`</p> <p>前面4步是搭建R平台的深度进修事情情况，后头先容R平台深度进修的应用。</p> <p>### MNIST图像识别问题<br /> 对MNIST数据集做手写数字识别，[**MNIST**](https://en.wikipedia.org/wiki/MNIST_database)包括一系列28×28的灰色手写数字图像，如下所示：<br /> </p> <p>#### 第一步：数据筹备<br /> 数据筹备包罗数据获取/数据支解/数据处理惩罚（重构和归一化和one-hot编码）<br /> R代码<br /> “`<br /> mnist <- dataset_mnist()<br /> x.train <- mnist$train$x<br /> y.train <- mnist$train$y<br /> x.test <- mnist$test$x<br /> y.test <- mnist$test$y<br /> dim(x.train) <- c(nrow(x.train), 28*28)<br /> dim(x.test) <- c(nrow(x.test), 28*28)<br /> x.train <- x.train / 255<br /> x.test <- x.test / 255<br /> y.train <- to_categorical(y.train, 10)<br /> y.test <- to_categorical(y.test, 10)<br /> str(x.train)<br /> str(y.train)<br /> “`<br /> #### 第二步：模子设计<br /> 模子设计包罗神经网络布局设计/损失函数+优化要领+怀抱准则配置<br /> R代码<br /> “`<br /> model <- keras_model_sequential()<br /> model %>%<br /> layer_dense(units = 256, activation = ‘relu’, input_shape = c(784)) %>%<br /> layer_dropout(rate = 0.4) %>%<br /> layer_dense(units = 128, activation = ‘relu’) %>%<br /> layer_dropout(rate = 0.3) %>%<br /> layer_dense(units = 10, activation = ‘softmax’)<br /> summary(model)</p> <p>model %>% compile(<br /> loss = ‘categorical_crossentropy’,<br /> optimizer = optimizer_rmsprop(),<br /> metrics = c(‘accuracy’)<br /> )<br /> “`</p> <p>#### 第三步：模子拟合和评价<br /> R代码<br /> “`<br /> history <- model %>% fit(<br /> x.train, y.train,<br /> epochs = 30,<br /> batch_size = 128,<br /> validation_split = 0.2<br /> )<br /> plot(history)<br /> model %>% evaluate(x.test, y.test)<br /> “`<br /> 功效：<br /> </p> <p><br /> #### 第四步：模子应用<br /> R代码<br /> “`<br /> model %>% predict_classes(x.test)<br /> “`<br /> 手写数字识别部门功效<br /> </p> <p>您在阅读中，有什么发起可能想法，请评论。<br /> 想插手圈子，请加微信。</p> </div><!-- .entry-content --> </article><!-- #post-## -->  <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="/r%e5%b9%b3%e5%8f%b0%e7%8e%a9%e6%b7%b1%e5%ba%a6%e8%bf%9b%e4%bf%ae%e6%95%99%e7%a8%8b/#wpcf7-f17097-o1" 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