> 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, lin...@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...@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, <lin...@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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