sandeep-krishnamurthy closed pull request #12117: [MXNET-782] Fix Custom Metric Creation in R tutorial URL: https://github.com/apache/incubator-mxnet/pull/12117
This is a PR merged from a forked repository. As GitHub hides the original diff on merge, it is displayed below for the sake of provenance: As this is a foreign pull request (from a fork), the diff is supplied below (as it won't show otherwise due to GitHub magic): 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. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
