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fix tutorial
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Anirudh Acharya committed Aug 10, 2018
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108 changes: 55 additions & 53 deletions docs/tutorials/r/fiveMinutesNeuralNetwork.md
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Expand Up @@ -235,7 +235,8 @@ 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)
})
```
Expand All @@ -251,58 +252,59 @@ 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
## Warning message in mx.model.select.layout.train(X, y):
## “Auto detect layout of input matrix, use rowmajor.
## ”Start training with 1 devices
## [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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