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@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@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