[Scikit-learn](https://github.com/scikit-learn/scikit-learn) is a pupular meachine learning tool for data mining and data analysis. It supports many kinds of meachine learning models like LinearRegression, LogisticRegression, DecisionTree, SVM etc. How to make the use of scikit-learn more efficiency is a valuable topic.
[Scikit-learn](https://github.com/scikit-learn/scikit-learn) is a pupular meachine learning tool for data mining and data analysis. It supports many kinds of meachine learning models like LinearRegression, LogisticRegression, DecisionTree, SVM etc. How to make the use of scikit-learn more efficiency is a valuable topic.
NNI supports many kinds of tuning algorithms to search the best models and/or hyper-parameters for scikit-learn, and support many kinds of environments like local machine, remote servers and cloud.
This example uses the dataset of digits, which is made up of 1797 8x8 images, and each image is a hand-written digit, the goal is to classify these images into 10 classes.
This example uses the dataset of digits, which is made up of 1797 8x8 images, and each image is a hand-written digit, the goal is to classify these images into 10 classes.
In this example, we use SVC as the model, and choose some parameters of this model, including `"C", "keral", "degree", "gamma" and "coef0"`. For more information of these parameters, please [refer](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html).
### 2.2 regression
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@@ -50,7 +50,7 @@ It is easy to use nni in your sklearn code, there are only a few steps.
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Then you could read these values as a dict from your python code, please get into the step 2.
* __step 2__
* __step 2__
At the beginning of your python code, you should `import nni` to insure the packages works normally.
First, you should use `nni.get_next_parameter()` function to get your parameters given by nni. Then you could use these parameters to update your code.
For example, if you define your search_space.json like following format:
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Then you could use these variables to write your scikit-learn code.
* __step 3__
After you finished your training, you could get your own score of the model, like your percision, recall or MSE etc. NNI needs your score to tuner algorithms and generate next group of parameters, please report the score back to NNI and start next trial job.
* __step 3__
After you finished your training, you could get your own score of the model, like your percision, recall or MSE etc. NNI needs your score to tuner algorithms and generate next group of parameters, please report the score back to NNI and start next trial job.
You just need to use `nni.report_final_result(score)` to communitate with NNI after you process your scikit-learn code. Or if you have multiple scores in the steps of training, you could also report them back to NNI using `nni.report_intemediate_result(score)`. Note, you may not report intemediate result of your job, but you must report back your final result.
@@ -69,16 +69,16 @@ You can refer to [here](ExperimentConfig.md) for more information about how to s
An alternative to writing a trial is to use NNI's syntax for python. Simple as any annotation, NNI annotation is working like comments in your codes. You don't have to make structure or any other big changes to your existing codes. With a few lines of NNI annotation, you will be able to:
* annotate the variables you want to tune
* annotate the variables you want to tune
* specify in which range you want to tune the variables
* annotate which variable you want to report as intermediate result to `assessor`
* annotate which variable you want to report as the final result (e.g. model accuracy) to `tuner`.
* annotate which variable you want to report as the final result (e.g. model accuracy) to `tuner`.
Again, take MNIST as an example, it only requires 2 steps to write a trial with NNI Annotation.
### Step 1 - Update codes with annotations
### Step 1 - Update codes with annotations
The following is a tensorflow code snippet for NNI Annotation, where the highlighted four lines are annotations that help you to:
The following is a tensorflow code snippet for NNI Annotation, where the highlighted four lines are annotations that help you to:
1. tune batch\_size and dropout\_rate
2. report test\_acc every 100 steps
3. at last report test\_acc as final result.
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@@ -111,11 +111,11 @@ with tf.Session() as sess:
+ """@nni.report_final_result(test_acc)"""
```
**NOTE**:
**NOTE**:
-`@nni.variable` will take effect on its following line, which is an assignment statement whose leftvalue must be specified by the keyword `name` in `@nni.variable`.
-`@nni.report_intermediate_result`/`@nni.report_final_result` will send the data to assessor/tuner at that line.
-`@nni.report_intermediate_result`/`@nni.report_final_result` will send the data to assessor/tuner at that line.
For more information about annotation syntax and its usage, please refer to [Annotation](AnnotationSpec.md).
For more information about annotation syntax and its usage, please refer to [Annotation](AnnotationSpec.md).
权重分配（转移）在加速 NAS 中有关键作用，而找到有效的权重共享方式仍是热门的研究课题。 NNI 提供了一个键值存储，用于存储和加载权重。 Tuner 和 Trial 使用 KV 客户端库来访问存储。
NNI 上的权重共享示例。
### [**待实现**] 支持 One-Shot NAS
One-Shot NAS 是流行的，能在有限的时间和资源预算内找到较好的神经网络结构的方法。 本质上，它会基于搜索空间来构建完整的图，并使用梯度下降最终找到最佳子图。 它有不同的训练方法，如：[training subgraphs (per mini-batch)](https://arxiv.org/abs/1802.03268) ，[training full graph through dropout](http://proceedings.mlr.press/v80/bender18a/bender18a.pdf)，以及 [training with architecture weights (regularization)](https://arxiv.org/abs/1806.09055) 。 这里会关注第一种方法，即训练子图（ENAS）。
