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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/08/10 20:17:41 UTC
[GitHub] sandeep-krishnamurthy closed pull request #12117: [MXNET-782] Fix
Custom Metric Creation in R tutorial
sandeep-krishnamurthy closed pull request #12117: [MXNET-782] Fix Custom Metric Creation in R tutorial
URL: https://github.com/apache/incubator-mxnet/pull/12117
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diff --git a/docs/tutorials/r/fiveMinutesNeuralNetwork.md b/docs/tutorials/r/fiveMinutesNeuralNetwork.md
index 9104e8f05c2..a2ce5ecd376 100644
--- a/docs/tutorials/r/fiveMinutesNeuralNetwork.md
+++ b/docs/tutorials/r/fiveMinutesNeuralNetwork.md
@@ -1,18 +1,21 @@
Develop a Neural Network with MXNet in Five Minutes
=============================================
-This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package.
+This tutorial is designed for new users of the `mxnet` package for R. It shows how to construct a neural network to do regression in 5 minutes. It shows how to perform classification and regression tasks, respectively. The data we use is in the `mlbench` package. Instructions to install R and MXNet's R package in different environments can be found [here](http://mxnet.incubator.apache.org/install/index.html?platform=Linux&language=R&processor=CPU).
## Classification
-
-
+ ```
+ ## Loading required package: mlbench
+ ```
```r
- require(mlbench)
+ if (!require(mlbench)) {
+ install.packages('mlbench')
+ }
```
```
- ## Loading required package: mlbench
+ ## Loading required package: mxnet
```
```r
@@ -20,8 +23,7 @@ This tutorial is designed for new users of the `mxnet` package for R. It shows h
```
```
- ## Loading required package: mxnet
- ## Loading required package: methods
+ ## Loading required datasets
```
```r
@@ -235,7 +237,8 @@ Currently, we have four predefined metrics: "accuracy", "rmse", "mae", and "rmsl
```r
demo.metric.mae <- mx.metric.custom("mae", function(label, pred) {
- res <- mean(abs(label-pred))
+ pred <- mx.nd.reshape(pred, shape = 0)
+ res <- mx.nd.mean(mx.nd.abs(label-pred))
return(res)
})
```
@@ -253,56 +256,56 @@ This is an example of the mean absolute error metric. Simply plug it into the tr
```
## 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
+ ## [1] Train-mae=14.953625731998
+ ## [2] Train-mae=11.4802955521478
+ ## [3] Train-mae=8.50700579749213
+ ## [4] Train-mae=7.30591265360514
+ ## [5] Train-mae=7.38049803839789
+ ## [6] Train-mae=7.36036252975464
+ ## [7] Train-mae=7.06519222259521
+ ## [8] Train-mae=6.9962231847975
+ ## [9] Train-mae=6.96296903822157
+ ## [10] Train-mae=6.9046172036065
+ ## [11] Train-mae=6.87867620256212
+ ## [12] Train-mae=6.85872554779053
+ ## [13] Train-mae=6.81936407089233
+ ## [14] Train-mae=6.79135354359945
+ ## [15] Train-mae=6.77438741260105
+ ## [16] Train-mae=6.75365140702989
+ ## [17] Train-mae=6.73369296391805
+ ## [18] Train-mae=6.71600982877943
+ ## [19] Train-mae=6.69932826360067
+ ## [20] Train-mae=6.6852519777086
+ ## [21] Train-mae=6.67343420452542
+ ## [22] Train-mae=6.66315894656711
+ ## [23] Train-mae=6.65314838621351
+ ## [24] Train-mae=6.64388704299927
+ ## [25] Train-mae=6.63480265935262
+ ## [26] Train-mae=6.62583245171441
+ ## [27] Train-mae=6.61697626113892
+ ## [28] Train-mae=6.60842116673787
+ ## [29] Train-mae=6.60040124257406
+ ## [30] Train-mae=6.59264140658908
+ ## [31] Train-mae=6.58551020092434
+ ## [32] Train-mae=6.57864215638902
+ ## [33] Train-mae=6.57178926467896
+ ## [34] Train-mae=6.56495311525133
+ ## [35] Train-mae=6.55813185373942
+ ## [36] Train-mae=6.5513252152337
+ ## [37] Train-mae=6.54453214009603
+ ## [38] Train-mae=6.53775374094645
+ ## [39] Train-mae=6.53098879920112
+ ## [40] Train-mae=6.52423816257053
+ ## [41] Train-mae=6.51764053768582
+ ## [42] Train-mae=6.51121346155802
+ ## [43] Train-mae=6.5047902001275
+ ## [44] Train-mae=6.49837123023139
+ ## [45] Train-mae=6.49216641320123
+ ## [46] Train-mae=6.48598252402412
+ ## [47] Train-mae=6.4798010720147
+ ## [48] Train-mae=6.47362396452162
+ ## [49] Train-mae=6.46745183732775
+ ## [50] Train-mae=6.46128723356459
```
Congratulations! You've learned the basics for using MXNet in R. To learn how to use MXNet's advanced features, see the other tutorials.
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