diff --git a/docs/tutorials/r/fiveMinutesNeuralNetwork.md b/docs/tutorials/r/fiveMinutesNeuralNetwork.md index 9104e8f05c2f..a2ce5ecd3761 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.