> here is another question, when I use neural network lib routine, can I save > the trained network for use at the next time?
Maybe have a look at the model persistence section at http://scikit-learn.org/stable/modules/model_persistence.html or http://cmry.github.io/notes/serialize Cheers, Sebastian > On Nov 24, 2016, at 8:08 PM, lin...@ruijie.com.cn wrote: > > @ Sebastian Raschka > thanks for your analyzing , > here is another question, when I use neural network lib routine, can I save > the trained network for use at the next time? > Just like the following: > > Foo1.py > … > Clf.fit(x,y) > Result_network = clf.save() > … > > Foo2.py > … > Clf = Load(result_network) > Res = Clf.predict(newsample) > … > > So I needn’t fit the train-set everytime > 发件人: scikit-learn > [mailto:scikit-learn-bounces+linjia=ruijie.com...@python.org] 代表 Sebastian > Raschka > 发送时间: 2016年11月24日 3:06 > 收件人: Scikit-learn user and developer mailing list > 主题: Re: [scikit-learn] question about using > sklearn.neural_network.MLPClassifier？ > > 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) > ``` > > The default is set 100 units in the hidden layer, but theoretically, it > should work with 2 hidden logistic units (I think that’s the typical > textbook/class example). I think what happens is that it gets stuck in local > minima depending on the random weight initialization. E.g., the following > works just fine: > > from sklearn.neural_network import MLPClassifier > X = [[0, 0], [0, 1], [1, 0], [1, 1]] > y = [0, 1, 1, 0] > clf = MLPClassifier(solver='lbfgs', > activation='logistic', > alpha=0.0, > hidden_layer_sizes=(2,), > learning_rate_init=0.1, > max_iter=1000, > random_state=20) > clf.fit(X, y) > res = clf.predict([[0, 0], [0, 1], [1, 0], [1, 1]]) > print(res) > print(clf.loss_) > > > but changing the random seed to 1 leads to: > > [0 1 1 1] > 0.34660921283 > > For comparison, I used a more vanilla MLP (1 hidden layer with 2 units and > logistic activation as well; > https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb), > essentially resulting in the same problem: > > > <image001.png><image002.png> > > > > > On Nov 23, 2016, at 6:26 AM, lin...@ruijie.com.cn wrote: > > 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...@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, <lin...@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 > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
