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, <[email protected]> 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 > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Raghav RV https://github.com/raghavrv
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