Skip to content
This repository has been archived by the owner on Nov 17, 2023. It is now read-only.

Commit

Permalink
refine tutorial
Browse files Browse the repository at this point in the history
  • Loading branch information
@hetong007
hetong007 committed Nov 3, 2015
1 parent 2feb6c5 commit 5dd2cae
Show file tree
Hide file tree
Showing 2 changed files with 6 additions and 130 deletions.
8 changes: 3 additions & 5 deletions R-package/vignettes/fiveMinutesNeuralNetwork.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@ The following code piece is showing a possible usage of `mx.mlp`:
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,epoch.end.callback=mx.callback.log.train.metric(100))
eval.metric=mx.metric.accuracy)
```

Note that `mx.set.seed` is the correct function to control the random process in `mxnet`. You can see the accuracy in each round during training. It is also easy to make prediction and evaluate.
Expand Down Expand Up @@ -96,8 +96,7 @@ next we can make prediction with this structure and other parameters with `mx.mo
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,
epoch.end.callback=mx.callback.log.train.metric(100))
learning.rate=2e-6, momentum=0.9, eval.metric=mx.metric.rmse)
```

It is also easy to make prediction and evaluate
Expand All @@ -122,8 +121,7 @@ This is an example for mean absolute error. We can simply plug it in the trainin
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,
epoch.end.callback=mx.callback.log.train.metric(100))
learning.rate=2e-6, momentum=0.9, eval.metric=demo.metric.mae)
```

Congratulations! Now you have learnt the basic for using `mxnet`. Please check the other tutorials for advanced features.
Expand Down
128 changes: 3 additions & 125 deletions doc/R-package/fiveMinutesNeuralNetwork.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -63,52 +63,32 @@ The following code piece is showing a possible usage of `mx.mlp`:
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,epoch.end.callback=mx.callback.log.train.metric(100))
eval.metric=mx.metric.accuracy)
```

```
## Auto detect layout of input matrix, use rowmajor..
## Start training with 1 devices
## [1] Train-accuracy=0.488888888888889
## Batch [0] Train-accuracy=0.488888888888889
## [2] Train-accuracy=0.514285714285714
## Batch [0] Train-accuracy=0.514285714285714
## [3] Train-accuracy=0.514285714285714
## Batch [0] Train-accuracy=0.514285714285714
## [4] Train-accuracy=0.514285714285714
## Batch [0] Train-accuracy=0.514285714285714
## [5] Train-accuracy=0.514285714285714
## Batch [0] Train-accuracy=0.514285714285714
## [6] Train-accuracy=0.523809523809524
## Batch [0] Train-accuracy=0.523809523809524
## [7] Train-accuracy=0.619047619047619
## Batch [0] Train-accuracy=0.619047619047619
## [8] Train-accuracy=0.695238095238095
## Batch [0] Train-accuracy=0.695238095238095
## [9] Train-accuracy=0.695238095238095
## Batch [0] Train-accuracy=0.695238095238095
## [10] Train-accuracy=0.761904761904762
## Batch [0] Train-accuracy=0.761904761904762
## [11] Train-accuracy=0.828571428571429
## Batch [0] Train-accuracy=0.828571428571429
## [12] Train-accuracy=0.771428571428571
## Batch [0] Train-accuracy=0.771428571428571
## [13] Train-accuracy=0.742857142857143
## Batch [0] Train-accuracy=0.742857142857143
## [14] Train-accuracy=0.733333333333333
## Batch [0] Train-accuracy=0.733333333333333
## [15] Train-accuracy=0.771428571428571
## Batch [0] Train-accuracy=0.771428571428571
## [16] Train-accuracy=0.847619047619048
## Batch [0] Train-accuracy=0.847619047619048
## [17] Train-accuracy=0.857142857142857
## Batch [0] Train-accuracy=0.857142857142857
## [18] Train-accuracy=0.838095238095238
## Batch [0] Train-accuracy=0.838095238095238
## [19] Train-accuracy=0.838095238095238
## Batch [0] Train-accuracy=0.838095238095238
## [20] Train-accuracy=0.838095238095238
## Batch [0] Train-accuracy=0.838095238095238
```

Note that `mx.set.seed` is the correct function to control the random process in `mxnet`. You can see the accuracy in each round during training. It is also easy to make prediction and evaluate.
Expand Down Expand Up @@ -175,113 +155,62 @@ next we can make prediction with this structure and other parameters with `mx.mo
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,
epoch.end.callback=mx.callback.log.train.metric(100))
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
## Batch [0] Train-rmse=16.063282524034
## [2] Train-rmse=12.2792375712573
## Batch [0] Train-rmse=12.2792375712573
## [3] Train-rmse=11.1984634005885
## Batch [0] Train-rmse=11.1984634005885
## [4] Train-rmse=10.2645236892904
## Batch [0] Train-rmse=10.2645236892904
## [5] Train-rmse=9.49711005504284
## Batch [0] Train-rmse=9.49711005504284
## [6] Train-rmse=9.07733734175182
## Batch [0] Train-rmse=9.07733734175182
## [7] Train-rmse=9.07884450847991
## Batch [0] Train-rmse=9.07884450847991
## [8] Train-rmse=9.10463850277417
## Batch [0] Train-rmse=9.10463850277417
## [9] Train-rmse=9.03977049028532
## Batch [0] Train-rmse=9.03977049028532
## [10] Train-rmse=8.96870685004475
## Batch [0] Train-rmse=8.96870685004475
## [11] Train-rmse=8.93113287361574
## Batch [0] Train-rmse=8.93113287361574
## [12] Train-rmse=8.89937257821847
## Batch [0] Train-rmse=8.89937257821847
## [13] Train-rmse=8.87182096922953
## Batch [0] Train-rmse=8.87182096922953
## [14] Train-rmse=8.84476075083586
## Batch [0] Train-rmse=8.84476075083586
## [15] Train-rmse=8.81464673014974
## Batch [0] Train-rmse=8.81464673014974
## [16] Train-rmse=8.78672567900196
## Batch [0] Train-rmse=8.78672567900196
## [17] Train-rmse=8.76265872846474
## Batch [0] Train-rmse=8.76265872846474
## [18] Train-rmse=8.73946101419974
## Batch [0] Train-rmse=8.73946101419974
## [19] Train-rmse=8.71651926303267
## Batch [0] Train-rmse=8.71651926303267
## [20] Train-rmse=8.69457600919277
## Batch [0] Train-rmse=8.69457600919277
## [21] Train-rmse=8.67354928674563
## Batch [0] Train-rmse=8.67354928674563
## [22] Train-rmse=8.65328755392436
## Batch [0] Train-rmse=8.65328755392436
## [23] Train-rmse=8.63378039680078
## Batch [0] Train-rmse=8.63378039680078
## [24] Train-rmse=8.61488162586984
## Batch [0] Train-rmse=8.61488162586984
## [25] Train-rmse=8.5965105183022
## Batch [0] Train-rmse=8.5965105183022
## [26] Train-rmse=8.57868133563275
## Batch [0] Train-rmse=8.57868133563275
## [27] Train-rmse=8.56135851937663
## Batch [0] Train-rmse=8.56135851937663
## [28] Train-rmse=8.5444819772098
## Batch [0] Train-rmse=8.5444819772098
## [29] Train-rmse=8.52802114610432
## Batch [0] Train-rmse=8.52802114610432
## [30] Train-rmse=8.5119504512622
## Batch [0] Train-rmse=8.5119504512622
## [31] Train-rmse=8.49624261719241
## Batch [0] Train-rmse=8.49624261719241
## [32] Train-rmse=8.48087453238701
## Batch [0] Train-rmse=8.48087453238701
## [33] Train-rmse=8.46582689119887
## Batch [0] Train-rmse=8.46582689119887
## [34] Train-rmse=8.45107881002491
## Batch [0] Train-rmse=8.45107881002491
## [35] Train-rmse=8.43661331401712
## Batch [0] Train-rmse=8.43661331401712
## [36] Train-rmse=8.42241575909639
## Batch [0] Train-rmse=8.42241575909639
## [37] Train-rmse=8.40847217331365
## Batch [0] Train-rmse=8.40847217331365
## [38] Train-rmse=8.39476931796395
## Batch [0] Train-rmse=8.39476931796395
## [39] Train-rmse=8.38129658373974
## Batch [0] Train-rmse=8.38129658373974
## [40] Train-rmse=8.36804269059018
## Batch [0] Train-rmse=8.36804269059018
## [41] Train-rmse=8.35499817678397
## Batch [0] Train-rmse=8.35499817678397
## [42] Train-rmse=8.34215505742154
## Batch [0] Train-rmse=8.34215505742154
## [43] Train-rmse=8.32950441908131
## Batch [0] Train-rmse=8.32950441908131
## [44] Train-rmse=8.31703985777311
## Batch [0] Train-rmse=8.31703985777311
## [45] Train-rmse=8.30475363906755
## Batch [0] Train-rmse=8.30475363906755
## [46] Train-rmse=8.29264031506106
## Batch [0] Train-rmse=8.29264031506106
## [47] Train-rmse=8.28069372820073
## Batch [0] Train-rmse=8.28069372820073
## [48] Train-rmse=8.26890902770415
## Batch [0] Train-rmse=8.26890902770415
## [49] Train-rmse=8.25728089053853
## Batch [0] Train-rmse=8.25728089053853
## [50] Train-rmse=8.24580511500735
## Batch [0] Train-rmse=8.24580511500735
```

It is also easy to make prediction and evaluate
Expand Down Expand Up @@ -320,113 +249,62 @@ This is an example for mean absolute error. We can simply plug it in the trainin
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,
epoch.end.callback=mx.callback.log.train.metric(100))
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
## Batch [0] Train-mae=13.1889538083225
## [2] Train-mae=9.81431959337658
## Batch [0] Train-mae=9.81431959337658
## [3] Train-mae=9.21576419870059
## Batch [0] Train-mae=9.21576419870059
## [4] Train-mae=8.38071537613869
## Batch [0] Train-mae=8.38071537613869
## [5] Train-mae=7.45462437611487
## Batch [0] Train-mae=7.45462437611487
## [6] Train-mae=6.93423301743136
## Batch [0] Train-mae=6.93423301743136
## [7] Train-mae=6.91432357016537
## Batch [0] Train-mae=6.91432357016537
## [8] Train-mae=7.02742733055105
## Batch [0] Train-mae=7.02742733055105
## [9] Train-mae=7.00618194618469
## Batch [0] Train-mae=7.00618194618469
## [10] Train-mae=6.92541576984028
## Batch [0] Train-mae=6.92541576984028
## [11] Train-mae=6.87530243690643
## Batch [0] Train-mae=6.87530243690643
## [12] Train-mae=6.84757369098564
## Batch [0] Train-mae=6.84757369098564
## [13] Train-mae=6.82966501611388
## Batch [0] Train-mae=6.82966501611388
## [14] Train-mae=6.81151759574811
## Batch [0] Train-mae=6.81151759574811
## [15] Train-mae=6.78394182841811
## Batch [0] Train-mae=6.78394182841811
## [16] Train-mae=6.75914719419347
## Batch [0] Train-mae=6.75914719419347
## [17] Train-mae=6.74180388773481
## Batch [0] Train-mae=6.74180388773481
## [18] Train-mae=6.725853071279
## Batch [0] Train-mae=6.725853071279
## [19] Train-mae=6.70932178215848
## Batch [0] Train-mae=6.70932178215848
## [20] Train-mae=6.6928868798746
## Batch [0] Train-mae=6.6928868798746
## [21] Train-mae=6.6769521329138
## Batch [0] Train-mae=6.6769521329138
## [22] Train-mae=6.66184809505939
## Batch [0] Train-mae=6.66184809505939
## [23] Train-mae=6.64754504809777
## Batch [0] Train-mae=6.64754504809777
## [24] Train-mae=6.63358514060577
## Batch [0] Train-mae=6.63358514060577
## [25] Train-mae=6.62027640889088
## Batch [0] Train-mae=6.62027640889088
## [26] Train-mae=6.60738245232238
## Batch [0] Train-mae=6.60738245232238
## [27] Train-mae=6.59505546771818
## Batch [0] Train-mae=6.59505546771818
## [28] Train-mae=6.58346195800437
## Batch [0] Train-mae=6.58346195800437
## [29] Train-mae=6.57285477783945
## Batch [0] Train-mae=6.57285477783945
## [30] Train-mae=6.56259003960424
## Batch [0] Train-mae=6.56259003960424
## [31] Train-mae=6.5527790788975
## Batch [0] Train-mae=6.5527790788975
## [32] Train-mae=6.54353428422991
## Batch [0] Train-mae=6.54353428422991
## [33] Train-mae=6.5344172368447
## Batch [0] Train-mae=6.5344172368447
## [34] Train-mae=6.52557652526432
## Batch [0] Train-mae=6.52557652526432
## [35] Train-mae=6.51697905850079
## Batch [0] Train-mae=6.51697905850079
## [36] Train-mae=6.50847898812758
## Batch [0] Train-mae=6.50847898812758
## [37] Train-mae=6.50014844106303
## Batch [0] Train-mae=6.50014844106303
## [38] Train-mae=6.49207674844397
## Batch [0] Train-mae=6.49207674844397
## [39] Train-mae=6.48412070125341
## Batch [0] Train-mae=6.48412070125341
## [40] Train-mae=6.47650500999557
## Batch [0] Train-mae=6.47650500999557
## [41] Train-mae=6.46893867486053
## Batch [0] Train-mae=6.46893867486053
## [42] Train-mae=6.46142131653097
## Batch [0] Train-mae=6.46142131653097
## [43] Train-mae=6.45395035048326
## Batch [0] Train-mae=6.45395035048326
## [44] Train-mae=6.44652914123403
## Batch [0] Train-mae=6.44652914123403
## [45] Train-mae=6.43916216409869
## Batch [0] Train-mae=6.43916216409869
## [46] Train-mae=6.43183777381976
## Batch [0] Train-mae=6.43183777381976
## [47] Train-mae=6.42455544223388
## Batch [0] Train-mae=6.42455544223388
## [48] Train-mae=6.41731406417158
## Batch [0] Train-mae=6.41731406417158
## [49] Train-mae=6.41011292926139
## Batch [0] Train-mae=6.41011292926139
## [50] Train-mae=6.40312503493494
## Batch [0] Train-mae=6.40312503493494
```

Congratulations! Now you have learnt the basic for using `mxnet`. Please check the other tutorials for advanced features.
Expand Down

0 comments on commit 5dd2cae

Please sign in to comment.