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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