question about using sklearn.neural_network.MLPClassifier？
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
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
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-neuraln... 发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@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, <linjia@ruijie.com.cn<mailto: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<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn -- Raghav RV https://github.com/raghavrv
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/...), essentially resulting in the same problem:
On Nov 23, 2016, at 6:26 AM, linjia@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-neuraln...
发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@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, <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
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
@ 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.cn@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/...), essentially resulting in the same problem: [cid:image001.png@01D246FB.965B30E0][cid:image002.png@01D246FB.965B30E0] On Nov 23, 2016, at 6:26 AM, linjia@ruijie.com.cn<mailto:linjia@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-neuraln... 发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@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, <linjia@ruijie.com.cn<mailto: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<mailto: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<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn
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, linjia@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.cn@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/...), essentially resulting in the same problem:
<image001.png><image002.png>
On Nov 23, 2016, at 6:26 AM, linjia@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-neuraln...
发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@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, <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
_______________________________________________ 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
Yes, it is what I need, Thanks you very much! -----邮件原件----- 发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@python.org] 代表 Sebastian Raschka 发送时间: 2016年11月25日 10:51 收件人: Scikit-learn user and developer mailing list 主题: Re: [scikit-learn] 答复: question about using sklearn.neural_network.MLPClassifier？
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, linjia@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.cn@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/...), essentially resulting in the same problem:
<image001.png><image002.png>
On Nov 23, 2016, at 6:26 AM, linjia@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-n euralnetwork-not-working-for-xor
发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@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, <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
_______________________________________________ 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
Hello everyone, I use ' IsolationForest' to pick up the outlier data today and I notice there is a ' contamination ' parameter in IsolationForest function, and its default value is 0.1 = 10% So is there a way to pick the outlier without assigning the proportion of outliers in the data set? For example, in dataset [2,3,2,4,2,3,1,2,3,1,2, 999, 2,3,2,1,2,3], we can easily pick the '999' as an outlier entry out of the set according to the consciousness And I read some paper about outlier detect recently, many of them need number of outlier and distance as input parameter in advance, is there algorithm more intelligently ? -----邮件原件----- 发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@python.org] 代表 Sebastian Raschka 发送时间: 2016年11月25日 10:51 收件人: Scikit-learn user and developer mailing list 主题: Re: [scikit-learn] 答复: question about using sklearn.neural_network.MLPClassifier？
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, linjia@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.cn@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/...), essentially resulting in the same problem:
<image001.png><image002.png>
On Nov 23, 2016, at 6:26 AM, linjia@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-n euralnetwork-not-working-for-xor
发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@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, <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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-- Raghav RV https://github.com/raghavrv
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many of them need number of outlier and distance as input parameter in advance, is there algorithm more intelligently ?
With ‘intelligently’ you mean ‘more automatic’ (fewer hyperparameters to define manually)? In my opinion, “outlier” is a highly context-specific definition, thus, it’s really up to you to decide what to count as an outlier or not for your application. E.g., a simple non-parametric approach would be to say that point P is an outlier if P > Q3 + 1.5 * IQR, or P < Q1 - 1.5 * IQR where Q1 and Q3 are the first and third quartile of the dataset, respectively, and IQR = interquartile range (Q3-Q1). Similarly you could use thresholds based on variance or standard deviation, etc. so that you don’t need to specify the number of outliers if that’s not what you want
On Nov 25, 2016, at 6:38 AM, linjia@ruijie.com.cn wrote:
Hello everyone, I use ' IsolationForest' to pick up the outlier data today and I notice there is a ' contamination ' parameter in IsolationForest function, and its default value is 0.1 = 10% So is there a way to pick the outlier without assigning the proportion of outliers in the data set? For example, in dataset [2,3,2,4,2,3,1,2,3,1,2, 999, 2,3,2,1,2,3], we can easily pick the '999' as an outlier entry out of the set according to the consciousness And I read some paper about outlier detect recently, many of them need number of outlier and distance as input parameter in advance, is there algorithm more intelligently ?
-----邮件原件----- 发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@python.org] 代表 Sebastian Raschka 发送时间: 2016年11月25日 10:51 收件人: Scikit-learn user and developer mailing list 主题: Re: [scikit-learn] 答复: question about using sklearn.neural_network.MLPClassifier？
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, linjia@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.cn@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/...), essentially resulting in the same problem:
<image001.png><image002.png>
On Nov 23, 2016, at 6:26 AM, linjia@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-n euralnetwork-not-working-for-xor
发件人: scikit-learn [mailto:scikit-learn-bounces+linjia=ruijie.com.cn@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, <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
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
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participants (3)
-
linjia＠ruijie.com.cn -
Raghav R V -
Sebastian Raschka