docs/zh/api/r/fiveMinutesNeuralNetwork.md - mxnet-test - Git at Google

 # 5分钟用MXNET上手神经网络

 这是给初次使用`mxnet`R包的小白教程第一弹。你将学习到如何在5分钟内构件一个神经网络完成回归分析。

 我们将想你展示如何分别执行分类任务和回归任务。这里的我们使用的数据来自 `mlbench` 包。

 ## 前言

 本教程由Rmd编辑完成。

 - 你可以直接访问主站版本的教程：[MXNet R Document](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
 - 你也可以从[这里](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/fiveMinutesNeuralNetwork.Rmd) 下载到Rmarkdown源文件

 ## 分类

 首先，让我们加载数据并且做预处理：

 ```r
 require(mlbench)
 ```

 ```
 ## Loading required package: mlbench
 ```

 ```r
 require(mxnet)
 ```

 ```
 ## Loading required package: mxnet
 ## Loading required package: methods
 ```

 ```r
 data(Sonar, package="mlbench")

 Sonar[,61] = as.numeric(Sonar[,61])-1
 train.ind = c(1:50, 100:150)
 train.x = data.matrix(Sonar[train.ind, 1:60])
 train.y = Sonar[train.ind, 61]
 test.x = data.matrix(Sonar[-train.ind, 1:60])
 test.y = Sonar[-train.ind, 61]
 ```

 下一步我们将用多层感知器作为分类器。在 `mxnet` 中，我们有一个`mx.mlp`函数，因此我们可以建立一个多层神经网络来做分类或者回归。

 `mx.mlp`需要我们提供以下参数

 - 训练数据和标签。
 - 每个隐层的隐层节点数。
 - 输出层的节点数。
 - 激活函数类型。
 - 数据损失函数类型。
 - 计算引擎 (GPU 或者 CPU)。
 - 其他给到 `mx.model.FeedForward.create` 的参数。

 下面的代码段展示了`mx.mlp`的可能用法：

 ```r
 mx.set.seed(0)
 model <- mx.mlp(train.x, train.y, hidden_node=10, out_node=2, out_activation="softmax",
                 num.round=20, array.batch.size=15, learning.rate=0.07, momentum=0.9,
                 eval.metric=mx.metric.accuracy)
 ```

 ```
 ## Auto detect layout of input matrix, use rowmajor..
 ## Start training with 1 devices
 ## [1] Train-accuracy=0.488888888888889
 ## [2] Train-accuracy=0.514285714285714
 ## [3] Train-accuracy=0.514285714285714
 ## [4] Train-accuracy=0.514285714285714
 ## [5] Train-accuracy=0.514285714285714
 ## [6] Train-accuracy=0.523809523809524
 ## [7] Train-accuracy=0.619047619047619
 ## [8] Train-accuracy=0.695238095238095
 ## [9] Train-accuracy=0.695238095238095
 ## [10] Train-accuracy=0.761904761904762
 ## [11] Train-accuracy=0.828571428571429
 ## [12] Train-accuracy=0.771428571428571
 ## [13] Train-accuracy=0.742857142857143
 ## [14] Train-accuracy=0.733333333333333
 ## [15] Train-accuracy=0.771428571428571
 ## [16] Train-accuracy=0.847619047619048
 ## [17] Train-accuracy=0.857142857142857
 ## [18] Train-accuracy=0.838095238095238
 ## [19] Train-accuracy=0.838095238095238
 ## [20] Train-accuracy=0.838095238095238
 ```

 注意到在 `mxnet`中用来控制随机数种子的函数是 `mx.set.seed`. 你可以看到每一次迭代训练的准确性，这方便我们做预测和评估函数。

 想要知道具体发生了什么，我们可以在R中轻松看到计算图：

 ```{r}
 graph.viz(model$symbol$as.json())
 ```

 [![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png)](https://github.com/dmlc/mxnet)

 ```r
 preds = predict(model, test.x)
 ```

 ```
 ## Auto detect layout of input matrix, use rowmajor..
 ```

 ```r
 pred.label = max.col(t(preds))-1
 table(pred.label, test.y)
 ```

 ```
 ##           test.y
 ## pred.label  0  1
 ##          0 24 14
 ##          1 36 33
 ```

 需要注意的是在多层预测中，`mxnet`输出矩阵是`nclass` x `nexamples`，每行都对应着每个类的概率。

 ## 回归

 让我们先再一次做数据预处理。

 ```r
 data(BostonHousing, package="mlbench")

 train.ind = seq(1, 506, 3)
 train.x = data.matrix(BostonHousing[train.ind, -14])
 train.y = BostonHousing[train.ind, 14]
 test.x = data.matrix(BostonHousing[-train.ind, -14])
 test.y = BostonHousing[-train.ind, 14]
 ```

 虽然我们可以通过改变`out_activation`复用 `mx.mlp` 来做回归，这次我们还是会介绍一个弹性的方式来配置`mxnet`的神经网络。在`mxnet`中，这个配置通过"符号"系统来实现，这个系统将各个节点连接起来，比如激活、弃用比率等等。配置一个多层神经网络，我们可以通过下面的方式来完成：

 ```r
 # 定义输入数据
 data <- mx.symbol.Variable("data")
 # 一个全联通的隐层
 # 数据: 输入源
 # num_hidden: 隐层的神经节点数量
 fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)

 # 每个输出层使用线性回归
 lro <- mx.symbol.LinearRegressionOutput(fc1)
 ```

 这让新网络可以优化方差损失。

 上面的函数已经完成了回归的主要工作，我们现在可以在一个简单数据集上做训练。在这个配置中，我们弃用隐层，因此输入层是直接联通到输出层。

 ```r
 mx.set.seed(0)
 model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
                                      ctx=mx.cpu(), num.round=50, array.batch.size=20,
                                      learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
 ```

 ```
 ## Auto detect layout of input matrix, use rowmajor..
 ## Start training with 1 devices
 ## [1] Train-rmse=16.063282524034
 ## [2] Train-rmse=12.2792375712573
 ## [3] Train-rmse=11.1984634005885
 ## [4] Train-rmse=10.2645236892904
 ## [5] Train-rmse=9.49711005504284
 ## [6] Train-rmse=9.07733734175182
 ## [7] Train-rmse=9.07884450847991
 ## [8] Train-rmse=9.10463850277417
 ## [9] Train-rmse=9.03977049028532
 ## [10] Train-rmse=8.96870685004475
 ## [11] Train-rmse=8.93113287361574
 ## [12] Train-rmse=8.89937257821847
 ## [13] Train-rmse=8.87182096922953
 ## [14] Train-rmse=8.84476075083586
 ## [15] Train-rmse=8.81464673014974
 ## [16] Train-rmse=8.78672567900196
 ## [17] Train-rmse=8.76265872846474
 ## [18] Train-rmse=8.73946101419974
 ## [19] Train-rmse=8.71651926303267
 ## [20] Train-rmse=8.69457600919277
 ## [21] Train-rmse=8.67354928674563
 ## [22] Train-rmse=8.65328755392436
 ## [23] Train-rmse=8.63378039680078
 ## [24] Train-rmse=8.61488162586984
 ## [25] Train-rmse=8.5965105183022
 ## [26] Train-rmse=8.57868133563275
 ## [27] Train-rmse=8.56135851937663
 ## [28] Train-rmse=8.5444819772098
 ## [29] Train-rmse=8.52802114610432
 ## [30] Train-rmse=8.5119504512622
 ## [31] Train-rmse=8.49624261719241
 ## [32] Train-rmse=8.48087453238701
 ## [33] Train-rmse=8.46582689119887
 ## [34] Train-rmse=8.45107881002491
 ## [35] Train-rmse=8.43661331401712
 ## [36] Train-rmse=8.42241575909639
 ## [37] Train-rmse=8.40847217331365
 ## [38] Train-rmse=8.39476931796395
 ## [39] Train-rmse=8.38129658373974
 ## [40] Train-rmse=8.36804269059018
 ## [41] Train-rmse=8.35499817678397
 ## [42] Train-rmse=8.34215505742154
 ## [43] Train-rmse=8.32950441908131
 ## [44] Train-rmse=8.31703985777311
 ## [45] Train-rmse=8.30475363906755
 ## [46] Train-rmse=8.29264031506106
 ## [47] Train-rmse=8.28069372820073
 ## [48] Train-rmse=8.26890902770415
 ## [49] Train-rmse=8.25728089053853
 ## [50] Train-rmse=8.24580511500735
 ```

 这很容易做预测和评估。

 ```r
 preds = predict(model, test.x)
 ```

 ```
 ## Auto detect layout of input matrix, use rowmajor..
 ```

 ```r
 sqrt(mean((preds-test.y)^2))
 ```

 ```
 ## [1] 7.800502
 ```

 现在，我们有4个预先定义的矩阵，分别是："accuracy", "rmse", "mae" and "rmsle". 你可能会对如何自定义评估矩阵感到困惑。`mxnet` 为自定义提供了一个接口：

 ```r
 demo.metric.mae <- mx.metric.custom("mae", function(label, pred) {
   res <- mean(abs(label-pred))
   return(res)
 })
 ```

 这里是一个平均绝对误差的一个例子。我们在一个训练函数中可以简单看看：

 ```r
 mx.set.seed(0)
 model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
                                      ctx=mx.cpu(), num.round=50, array.batch.size=20,
                                      learning.rate=2e-6, momentum=0.9, eval.metric=demo.metric.mae)
 ```

 ```
 ## Auto detect layout of input matrix, use rowmajor..
 ## Start training with 1 devices
 ## [1] Train-mae=13.1889538083225
 ## [2] Train-mae=9.81431959337658
 ## [3] Train-mae=9.21576419870059
 ## [4] Train-mae=8.38071537613869
 ## [5] Train-mae=7.45462437611487
 ## [6] Train-mae=6.93423301743136
 ## [7] Train-mae=6.91432357016537
 ## [8] Train-mae=7.02742733055105
 ## [9] Train-mae=7.00618194618469
 ## [10] Train-mae=6.92541576984028
 ## [11] Train-mae=6.87530243690643
 ## [12] Train-mae=6.84757369098564
 ## [13] Train-mae=6.82966501611388
 ## [14] Train-mae=6.81151759574811
 ## [15] Train-mae=6.78394182841811
 ## [16] Train-mae=6.75914719419347
 ## [17] Train-mae=6.74180388773481
 ## [18] Train-mae=6.725853071279
 ## [19] Train-mae=6.70932178215848
 ## [20] Train-mae=6.6928868798746
 ## [21] Train-mae=6.6769521329138
 ## [22] Train-mae=6.66184809505939
 ## [23] Train-mae=6.64754504809777
 ## [24] Train-mae=6.63358514060577
 ## [25] Train-mae=6.62027640889088
 ## [26] Train-mae=6.60738245232238
 ## [27] Train-mae=6.59505546771818
 ## [28] Train-mae=6.58346195800437
 ## [29] Train-mae=6.57285477783945
 ## [30] Train-mae=6.56259003960424
 ## [31] Train-mae=6.5527790788975
 ## [32] Train-mae=6.54353428422991
 ## [33] Train-mae=6.5344172368447
 ## [34] Train-mae=6.52557652526432
 ## [35] Train-mae=6.51697905850079
 ## [36] Train-mae=6.50847898812758
 ## [37] Train-mae=6.50014844106303
 ## [38] Train-mae=6.49207674844397
 ## [39] Train-mae=6.48412070125341
 ## [40] Train-mae=6.47650500999557
 ## [41] Train-mae=6.46893867486053
 ## [42] Train-mae=6.46142131653097
 ## [43] Train-mae=6.45395035048326
 ## [44] Train-mae=6.44652914123403
 ## [45] Train-mae=6.43916216409869
 ## [46] Train-mae=6.43183777381976
 ## [47] Train-mae=6.42455544223388
 ## [48] Train-mae=6.41731406417158
 ## [49] Train-mae=6.41011292926139
 ## [50] Train-mae=6.40312503493494
 ```

 恭喜你！现在你已经学会如何基本使用`mxnet`。请查看其它教程学习更多高级功能。
	# 5分钟用MXNET上手神经网络

	这是给初次使用`mxnet`R包的小白教程第一弹。你将学习到如何在5分钟内构件一个神经网络完成回归分析。

	我们将想你展示如何分别执行分类任务和回归任务。这里的我们使用的数据来自 `mlbench` 包。

	## 前言

	本教程由Rmd编辑完成。

	- 你可以直接访问主站版本的教程：[MXNet R Document](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html)
	- 你也可以从[这里](https://github.com/dmlc/mxnet/blob/master/R-package/vignettes/fiveMinutesNeuralNetwork.Rmd) 下载到Rmarkdown源文件

	## 分类

	首先，让我们加载数据并且做预处理：

	```r
	require(mlbench)
	```

	```
	## Loading required package: mlbench
	```

	```r
	require(mxnet)
	```

	```
	## Loading required package: mxnet
	## Loading required package: methods
	```

	```r
	data(Sonar, package="mlbench")

	Sonar[,61] = as.numeric(Sonar[,61])-1
	train.ind = c(1:50, 100:150)
	train.x = data.matrix(Sonar[train.ind, 1:60])
	train.y = Sonar[train.ind, 61]
	test.x = data.matrix(Sonar[-train.ind, 1:60])
	test.y = Sonar[-train.ind, 61]
	```

	下一步我们将用多层感知器作为分类器。在 `mxnet` 中，我们有一个`mx.mlp`函数，因此我们可以建立一个多层神经网络来做分类或者回归。

	`mx.mlp`需要我们提供以下参数

	- 训练数据和标签。
	- 每个隐层的隐层节点数。
	- 输出层的节点数。
	- 激活函数类型。
	- 数据损失函数类型。
	- 计算引擎 (GPU 或者 CPU)。
	- 其他给到 `mx.model.FeedForward.create` 的参数。

	下面的代码段展示了`mx.mlp`的可能用法：

	```r
	mx.set.seed(0)
	model <- mx.mlp(train.x, train.y, hidden_node=10, out_node=2, out_activation="softmax",
	num.round=20, array.batch.size=15, learning.rate=0.07, momentum=0.9,
	eval.metric=mx.metric.accuracy)
	```

	```
	## Auto detect layout of input matrix, use rowmajor..
	## Start training with 1 devices
	## [1] Train-accuracy=0.488888888888889
	## [2] Train-accuracy=0.514285714285714
	## [3] Train-accuracy=0.514285714285714
	## [4] Train-accuracy=0.514285714285714
	## [5] Train-accuracy=0.514285714285714
	## [6] Train-accuracy=0.523809523809524
	## [7] Train-accuracy=0.619047619047619
	## [8] Train-accuracy=0.695238095238095
	## [9] Train-accuracy=0.695238095238095
	## [10] Train-accuracy=0.761904761904762
	## [11] Train-accuracy=0.828571428571429
	## [12] Train-accuracy=0.771428571428571
	## [13] Train-accuracy=0.742857142857143
	## [14] Train-accuracy=0.733333333333333
	## [15] Train-accuracy=0.771428571428571
	## [16] Train-accuracy=0.847619047619048
	## [17] Train-accuracy=0.857142857142857
	## [18] Train-accuracy=0.838095238095238
	## [19] Train-accuracy=0.838095238095238
	## [20] Train-accuracy=0.838095238095238
	```

	注意到在 `mxnet`中用来控制随机数种子的函数是 `mx.set.seed`. 你可以看到每一次迭代训练的准确性，这方便我们做预测和评估函数。

	想要知道具体发生了什么，我们可以在R中轻松看到计算图：

	```{r}
	graph.viz(model$symbol$as.json())
	```

	[![](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png)](https://github.com/dmlc/mxnet)

	```r
	preds = predict(model, test.x)
	```

	```
	## Auto detect layout of input matrix, use rowmajor..
	```

	```r
	pred.label = max.col(t(preds))-1
	table(pred.label, test.y)
	```

	```
	## test.y
	## pred.label 0 1
	## 0 24 14
	## 1 36 33
	```

	需要注意的是在多层预测中，`mxnet`输出矩阵是`nclass` x `nexamples`，每行都对应着每个类的概率。

	## 回归

	让我们先再一次做数据预处理。

	```r
	data(BostonHousing, package="mlbench")

	train.ind = seq(1, 506, 3)
	train.x = data.matrix(BostonHousing[train.ind, -14])
	train.y = BostonHousing[train.ind, 14]
	test.x = data.matrix(BostonHousing[-train.ind, -14])
	test.y = BostonHousing[-train.ind, 14]
	```

	虽然我们可以通过改变`out_activation`复用 `mx.mlp` 来做回归，这次我们还是会介绍一个弹性的方式来配置`mxnet`的神经网络。在`mxnet`中，这个配置通过"符号"系统来实现，这个系统将各个节点连接起来，比如激活、弃用比率等等。配置一个多层神经网络，我们可以通过下面的方式来完成：

	```r
	# 定义输入数据
	data <- mx.symbol.Variable("data")
	# 一个全联通的隐层
	# 数据: 输入源
	# num_hidden: 隐层的神经节点数量
	fc1 <- mx.symbol.FullyConnected(data, num_hidden=1)

	# 每个输出层使用线性回归
	lro <- mx.symbol.LinearRegressionOutput(fc1)
	```

	这让新网络可以优化方差损失。

	上面的函数已经完成了回归的主要工作，我们现在可以在一个简单数据集上做训练。在这个配置中，我们弃用隐层，因此输入层是直接联通到输出层。

	```r
	mx.set.seed(0)
	model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
	ctx=mx.cpu(), num.round=50, array.batch.size=20,
	learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
	```

	```
	## Auto detect layout of input matrix, use rowmajor..
	## Start training with 1 devices
	## [1] Train-rmse=16.063282524034
	## [2] Train-rmse=12.2792375712573
	## [3] Train-rmse=11.1984634005885
	## [4] Train-rmse=10.2645236892904
	## [5] Train-rmse=9.49711005504284
	## [6] Train-rmse=9.07733734175182
	## [7] Train-rmse=9.07884450847991
	## [8] Train-rmse=9.10463850277417
	## [9] Train-rmse=9.03977049028532
	## [10] Train-rmse=8.96870685004475
	## [11] Train-rmse=8.93113287361574
	## [12] Train-rmse=8.89937257821847
	## [13] Train-rmse=8.87182096922953
	## [14] Train-rmse=8.84476075083586
	## [15] Train-rmse=8.81464673014974
	## [16] Train-rmse=8.78672567900196
	## [17] Train-rmse=8.76265872846474
	## [18] Train-rmse=8.73946101419974
	## [19] Train-rmse=8.71651926303267
	## [20] Train-rmse=8.69457600919277
	## [21] Train-rmse=8.67354928674563
	## [22] Train-rmse=8.65328755392436
	## [23] Train-rmse=8.63378039680078
	## [24] Train-rmse=8.61488162586984
	## [25] Train-rmse=8.5965105183022
	## [26] Train-rmse=8.57868133563275
	## [27] Train-rmse=8.56135851937663
	## [28] Train-rmse=8.5444819772098
	## [29] Train-rmse=8.52802114610432
	## [30] Train-rmse=8.5119504512622
	## [31] Train-rmse=8.49624261719241
	## [32] Train-rmse=8.48087453238701
	## [33] Train-rmse=8.46582689119887
	## [34] Train-rmse=8.45107881002491
	## [35] Train-rmse=8.43661331401712
	## [36] Train-rmse=8.42241575909639
	## [37] Train-rmse=8.40847217331365
	## [38] Train-rmse=8.39476931796395
	## [39] Train-rmse=8.38129658373974
	## [40] Train-rmse=8.36804269059018
	## [41] Train-rmse=8.35499817678397
	## [42] Train-rmse=8.34215505742154
	## [43] Train-rmse=8.32950441908131
	## [44] Train-rmse=8.31703985777311
	## [45] Train-rmse=8.30475363906755
	## [46] Train-rmse=8.29264031506106
	## [47] Train-rmse=8.28069372820073
	## [48] Train-rmse=8.26890902770415
	## [49] Train-rmse=8.25728089053853
	## [50] Train-rmse=8.24580511500735
	```

	这很容易做预测和评估。

	```r
	preds = predict(model, test.x)
	```

	```
	## Auto detect layout of input matrix, use rowmajor..
	```

	```r
	sqrt(mean((preds-test.y)^2))
	```

	```
	## [1] 7.800502
	```

	现在，我们有4个预先定义的矩阵，分别是："accuracy", "rmse", "mae" and "rmsle". 你可能会对如何自定义评估矩阵感到困惑。`mxnet` 为自定义提供了一个接口：

	```r
	demo.metric.mae <- mx.metric.custom("mae", function(label, pred) {
	res <- mean(abs(label-pred))
	return(res)
	})
	```

	这里是一个平均绝对误差的一个例子。我们在一个训练函数中可以简单看看：

	```r
	mx.set.seed(0)
	model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
	ctx=mx.cpu(), num.round=50, array.batch.size=20,
	learning.rate=2e-6, momentum=0.9, eval.metric=demo.metric.mae)
	```

	```
	## Auto detect layout of input matrix, use rowmajor..
	## Start training with 1 devices
	## [1] Train-mae=13.1889538083225
	## [2] Train-mae=9.81431959337658
	## [3] Train-mae=9.21576419870059
	## [4] Train-mae=8.38071537613869
	## [5] Train-mae=7.45462437611487
	## [6] Train-mae=6.93423301743136
	## [7] Train-mae=6.91432357016537
	## [8] Train-mae=7.02742733055105
	## [9] Train-mae=7.00618194618469
	## [10] Train-mae=6.92541576984028
	## [11] Train-mae=6.87530243690643
	## [12] Train-mae=6.84757369098564
	## [13] Train-mae=6.82966501611388
	## [14] Train-mae=6.81151759574811
	## [15] Train-mae=6.78394182841811
	## [16] Train-mae=6.75914719419347
	## [17] Train-mae=6.74180388773481
	## [18] Train-mae=6.725853071279
	## [19] Train-mae=6.70932178215848
	## [20] Train-mae=6.6928868798746
	## [21] Train-mae=6.6769521329138
	## [22] Train-mae=6.66184809505939
	## [23] Train-mae=6.64754504809777
	## [24] Train-mae=6.63358514060577
	## [25] Train-mae=6.62027640889088
	## [26] Train-mae=6.60738245232238
	## [27] Train-mae=6.59505546771818
	## [28] Train-mae=6.58346195800437
	## [29] Train-mae=6.57285477783945
	## [30] Train-mae=6.56259003960424
	## [31] Train-mae=6.5527790788975
	## [32] Train-mae=6.54353428422991
	## [33] Train-mae=6.5344172368447
	## [34] Train-mae=6.52557652526432
	## [35] Train-mae=6.51697905850079
	## [36] Train-mae=6.50847898812758
	## [37] Train-mae=6.50014844106303
	## [38] Train-mae=6.49207674844397
	## [39] Train-mae=6.48412070125341
	## [40] Train-mae=6.47650500999557
	## [41] Train-mae=6.46893867486053
	## [42] Train-mae=6.46142131653097
	## [43] Train-mae=6.45395035048326
	## [44] Train-mae=6.44652914123403
	## [45] Train-mae=6.43916216409869
	## [46] Train-mae=6.43183777381976
	## [47] Train-mae=6.42455544223388
	## [48] Train-mae=6.41731406417158
	## [49] Train-mae=6.41011292926139
	## [50] Train-mae=6.40312503493494
	```

	恭喜你！现在你已经学会如何基本使用`mxnet`。请查看其它教程学习更多高级功能。