[scikit-learn] question about using sklearn.neural_network.MLPClassifier?

Raghav R V ragvrv at gmail.com
Wed Nov 23 06:04:15 EST 2016


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 at 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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