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

[Sebastian Raschka]

> 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:






> 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-neuralnetwork-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
> 
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
> 
> 
> 
>  
> -- 
> Raghav RV
> https://github.com/raghavrv
>  
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20161123/67ff21b1/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Unknown-2.png
Type: image/png
Size: 9601 bytes
Desc: not available
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20161123/67ff21b1/attachment-0002.png>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: Unknown-1.png
Type: image/png
Size: 10222 bytes
Desc: not available
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20161123/67ff21b1/attachment-0003.png>