[scikit-learn] 答复: 答复: question about using sklearn.neural_network.MLPClassifier？

[linjia at ruijie.com.cn]

Yes, it is what I need, Thanks you very much!


-----邮件原件-----
发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn at python.org] 代表 Sebastian Raschka
发送时间: 2016年11月25日 10:51
收件人: Scikit-learn user and developer mailing list
主题: Re: [scikit-learn] 答复: question about using sklearn.neural_network.MLPClassifier？

> here is another question, when I use neural network lib routine, can I save the trained network for use at the next time?


Maybe have a look at the model persistence section at http://scikit-learn.org/stable/modules/model_persistence.html or http://cmry.github.io/notes/serialize

Cheers,
Sebastian


> On Nov 24, 2016, at 8:08 PM, linjia at ruijie.com.cn wrote:
> 
> @ Sebastian Raschka
> thanks for your analyzing ,
> here is another question, when I use neural network lib routine, can I save the trained network for use at the next time?
> Just like the following:
>  
> Foo1.py
> …
> Clf.fit(x,y)
> Result_network = clf.save()
> …
>  
> Foo2.py
> …
> Clf = Load(result_network)
> Res = Clf.predict(newsample)
> …
>  
> So I needn’t fit the train-set everytime
> 发件人: scikit-learn 
> [mailto:scikit-learn-bounces+linjia=ruijie.com.cn at python.org] 代表 
> Sebastian Raschka
> 发送时间: 2016年11月24日 3:06
> 收件人: Scikit-learn user and developer mailing list
> 主题: Re: [scikit-learn] question about using 
> sklearn.neural_network.MLPClassifier？
>  
> If you keep everything at their default values, it seems to work -
>  
> ```py
> from sklearn.neural_network import MLPClassifier X = [[0, 0], [0, 1], 
> [1, 0], [1, 1]] y = [0, 1, 1, 0] clf = MLPClassifier(max_iter=1000) 
> clf.fit(X, y) res = clf.predict([[0, 0], [0, 1], [1, 0], [1, 1]])
> print(res)
> ```
>  
> The default is set 100 units in the hidden layer, but theoretically, it should work with 2 hidden logistic units (I think that’s the typical textbook/class example). I think what happens is that it gets stuck in local minima depending on the random weight initialization. E.g., the following works just fine:
>  
> from sklearn.neural_network import MLPClassifier X = [[0, 0], [0, 1], 
> [1, 0], [1, 1]] y = [0, 1, 1, 0] clf = MLPClassifier(solver='lbfgs',
>                     activation='logistic', 
>                     alpha=0.0, 
>                     hidden_layer_sizes=(2,),
>                     learning_rate_init=0.1,
>                     max_iter=1000,
>                     random_state=20)
> clf.fit(X, y)
> res = clf.predict([[0, 0], [0, 1], [1, 0], [1, 1]])
> print(res)
> print(clf.loss_)
>  
>  
> but changing the random seed to 1 leads to:
>  
> [0 1 1 1]
> 0.34660921283
>  
> For comparison, I used a more vanilla MLP (1 hidden layer with 2 units and logistic activation as well; https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb), essentially resulting in the same problem:
>  
>  
> <image001.png><image002.png>
>  
>  
> 
> 
> On Nov 23, 2016, at 6:26 AM, linjia at ruijie.com.cn wrote:
> 
> Yes，you are right @ Raghav R V, thx!
> 
> However, i found the key param is ‘hidden_layer_sizes=[2]’,  I wonder if I misunderstand the meaning of parameter of hidden_layer_sizes?
>  
> Is  it related to the topic : 
> http://stackoverflow.com/questions/36819287/mlp-classifier-of-scikit-n
> euralnetwork-not-working-for-xor
>  
>  
> 发件人: scikit-learn 
> [mailto:scikit-learn-bounces+linjia=ruijie.com.cn at python.org] 代表 
> Raghav R V
> 发送时间: 2016年11月23日 19:04
> 收件人: Scikit-learn user and developer mailing list
> 主题: Re: [scikit-learn] question about using 
> sklearn.neural_network.MLPClassifier？
>  
> Hi,
>  
> If you keep everything at their default values, it seems to work -
>  
> ```py
> from sklearn.neural_network import MLPClassifier X = [[0, 0], [0, 1], 
> [1, 0], [1, 1]] y = [0, 1, 1, 0] clf = MLPClassifier(max_iter=1000) 
> clf.fit(X, y) res = clf.predict([[0, 0], [0, 1], [1, 0], [1, 1]])
> print(res)
> ```
> 
> On Wed, Nov 23, 2016 at 10:27 AM, <linjia at ruijie.com.cn> wrote:
> Hi everyone
>  
>       I try to use sklearn.neural_network.MLPClassifier to test the XOR operation, but I found the result is not satisfied. The following is code, can you tell me if I use the lib incorrectly?
>  
> from sklearn.neural_network import MLPClassifier X = [[0, 0], [0, 1], 
> [1, 0], [1, 1]] y = [0, 1, 1, 0] clf = MLPClassifier(solver='adam', 
> activation='logistic', alpha=1e-3, hidden_layer_sizes=(2,), 
> max_iter=1000) clf.fit(X, y) res = clf.predict([[0, 0], [0, 1], [1, 
> 0], [1, 1]])
> print(res)
>  
>  
> #result is [0 0 0 0], score is 0.5
> 
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> 
> 
>  
> --
> Raghav RV
> https://github.com/raghavrv
>  
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