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modify the tutorial in v1 rec sys to match the training result #2003

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32 changes: 16 additions & 16 deletions doc/tutorials/rec/ml_regression_cn.rst
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Expand Up @@ -297,21 +297,21 @@ meta文件 :code:`meta.bin` 的结构如下：

.. code-block:: text

I0601 08:07:22.832059 10549 TrainerInternal.cpp:157] Batch=100 samples=160000 AvgCost=4.13494 CurrentCost=4.13494 Eval: CurrentEval:
I0503 08:16:51.955983 2289 TrainerInternal.cpp:165] Batch=100 samples=160000 AvgCost=6.35491 CurrentCost=6.35491 Eval: CurrentEval:

I0601 08:07:50.672627 10549 TrainerInternal.cpp:157] Batch=200 samples=320000 AvgCost=3.80957 CurrentCost=3.48421 Eval: CurrentEval:
I0503 08:18:15.284365 2289 TrainerInternal.cpp:165] Batch=200 samples=320000 AvgCost=6.28487 CurrentCost=6.21484 Eval: CurrentEval:

I0601 08:08:18.877369 10549 TrainerInternal.cpp:157] Batch=300 samples=480000 AvgCost=3.68145 CurrentCost=3.42519 Eval: CurrentEval:
I0503 08:20:04.080775 2289 TrainerInternal.cpp:165] Batch=300 samples=480000 AvgCost=6.24399 CurrentCost=6.16221 Eval: CurrentEval:

I0601 08:08:46.863963 10549 TrainerInternal.cpp:157] Batch=400 samples=640000 AvgCost=3.6007 CurrentCost=3.35847 Eval: CurrentEval:
I0503 08:21:30.722985 2289 TrainerInternal.cpp:165] Batch=400 samples=640000 AvgCost=6.22783 CurrentCost=6.17936 Eval: CurrentEval:

I0503 08:22:45.917711 2289 TrainerInternal.cpp:165] Batch=500 samples=800000 AvgCost=6.21672 CurrentCost=6.1723 Eval: CurrentEval:

I0503 08:23:33.867655 2289 TrainerInternal.cpp:181] Pass=0 Batch=565 samples=902826 AvgCost=6.20941 Eval:

I0503 08:23:53.043184 2289 Tester.cpp:115] Test samples=97383 cost=6.15964 Eval:
I0503 08:23:53.043618 2289 GradientMachine.cpp:64] Saving parameters to ./output/pass-00000

I0601 08:09:15.413025 10549 TrainerInternal.cpp:157] Batch=500 samples=800000 AvgCost=3.54811 CurrentCost=3.33773 Eval: CurrentEval:
I0601 08:09:36.058670 10549 TrainerInternal.cpp:181] Pass=0 Batch=565 samples=902826 AvgCost=3.52368 Eval:
I0601 08:09:46.215489 10549 Tester.cpp:101] Test samples=97383 cost=3.32155 Eval:
I0601 08:09:46.215966 10549 GradientMachine.cpp:132] Saving parameters to ./output/model/pass-00000
I0601 08:09:46.233397 10549 ParamUtil.cpp:99] save dir ./output/model/pass-00000
I0601 08:09:46.233438 10549 Util.cpp:209] copy trainer_config.py to ./output/model/pass-00000
I0601 08:09:46.233541 10549 ParamUtil.cpp:147] fileName trainer_config.py

模型被保存在 :code:`output/` 目录中。你可以在任何时候用 :code:`Ctrl-C` 来停止训练。

Expand All @@ -328,22 +328,22 @@ meta文件 :code:`meta.bin` 的结构如下：

.. code-block:: text

Best pass is 00009, error is 3.06949, which means predict get error as 0.875998002281
evaluating from pass output/pass-00009
Best pass is 00047, error is 6.14036, which means predict get error as 1.23898748985
evaluating from pass output/pass-00047

然后，你可以预测任何用户对于任何一部电影的评价，运行下面命令即可：

.. code-block:: bash

python prediction.py 'output/pass-00009/'
python prediction.py 'output/pass-00047/'

预测程序将读取用户的输入，然后输出预测分数。用户预测的命令行界面如下：

.. code-block:: text

Input movie_id: 9
Input user_id: 4
Prediction Score is 2.56
Prediction Score is 2.65
Input movie_id: 8
Input user_id: 2
Prediction Score is 3.13
Prediction Score is 2.97
29 changes: 14 additions & 15 deletions doc/tutorials/rec/ml_regression_en.rst
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Expand Up @@ -296,21 +296,20 @@ If training process starts successfully, the output likes follow:

.. code-block:: text

I0601 08:07:22.832059 10549 TrainerInternal.cpp:157] Batch=100 samples=160000 AvgCost=4.13494 CurrentCost=4.13494 Eval: CurrentEval:
I0503 08:16:51.955983 2289 TrainerInternal.cpp:165] Batch=100 samples=160000 AvgCost=6.35491 CurrentCost=6.35491 Eval: CurrentEval:

I0601 08:07:50.672627 10549 TrainerInternal.cpp:157] Batch=200 samples=320000 AvgCost=3.80957 CurrentCost=3.48421 Eval: CurrentEval:
I0503 08:18:15.284365 2289 TrainerInternal.cpp:165] Batch=200 samples=320000 AvgCost=6.28487 CurrentCost=6.21484 Eval: CurrentEval:

I0601 08:08:18.877369 10549 TrainerInternal.cpp:157] Batch=300 samples=480000 AvgCost=3.68145 CurrentCost=3.42519 Eval: CurrentEval:
I0503 08:20:04.080775 2289 TrainerInternal.cpp:165] Batch=300 samples=480000 AvgCost=6.24399 CurrentCost=6.16221 Eval: CurrentEval:

I0601 08:08:46.863963 10549 TrainerInternal.cpp:157] Batch=400 samples=640000 AvgCost=3.6007 CurrentCost=3.35847 Eval: CurrentEval:
I0503 08:21:30.722985 2289 TrainerInternal.cpp:165] Batch=400 samples=640000 AvgCost=6.22783 CurrentCost=6.17936 Eval: CurrentEval:

I0601 08:09:15.413025 10549 TrainerInternal.cpp:157] Batch=500 samples=800000 AvgCost=3.54811 CurrentCost=3.33773 Eval: CurrentEval:
I0601 08:09:36.058670 10549 TrainerInternal.cpp:181] Pass=0 Batch=565 samples=902826 AvgCost=3.52368 Eval:
I0601 08:09:46.215489 10549 Tester.cpp:101] Test samples=97383 cost=3.32155 Eval:
I0601 08:09:46.215966 10549 GradientMachine.cpp:132] Saving parameters to ./output/model/pass-00000
I0601 08:09:46.233397 10549 ParamUtil.cpp:99] save dir ./output/model/pass-00000
I0601 08:09:46.233438 10549 Util.cpp:209] copy trainer_config.py to ./output/model/pass-00000
I0601 08:09:46.233541 10549 ParamUtil.cpp:147] fileName trainer_config.py
I0503 08:22:45.917711 2289 TrainerInternal.cpp:165] Batch=500 samples=800000 AvgCost=6.21672 CurrentCost=6.1723 Eval: CurrentEval:

I0503 08:23:33.867655 2289 TrainerInternal.cpp:181] Pass=0 Batch=565 samples=902826 AvgCost=6.20941 Eval:

I0503 08:23:53.043184 2289 Tester.cpp:115] Test samples=97383 cost=6.15964 Eval:
I0503 08:23:53.043618 2289 GradientMachine.cpp:64] Saving parameters to ./output/pass-00000

The model is saved in :code:`output/` directory. You can use :code:`Ctrl-C` to stop training whenever you want.

Expand All @@ -327,8 +326,8 @@ You will see messages like this:

.. code-block:: text

Best pass is 00009, error is 3.06949, which means predict get error as 0.875998002281
evaluating from pass output/pass-00009
Best pass is 00047, error is 6.14036, which means predict get error as 1.23898748985
evaluating from pass output/pass-00047

Then, you can predict what any user will rate a movie. Just run

Expand All @@ -342,7 +341,7 @@ Predictor will read user input, and predict scores. It has a command-line user i

Input movie_id: 9
Input user_id: 4
Prediction Score is 2.56
Prediction Score is 2.65
Input movie_id: 8
Input user_id: 2
Prediction Score is 3.13
Prediction Score is 2.97