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