当我尝试使用与n_job不等于1的cross_val_score时遇到错误 .
我的系统是Intel-i7 cpu,Windows10,python3.6,Spyder .
以下是我的代码:
from numpy.random import randn
import pandas as pd
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from keras.models import Sequential
from keras.layers import Dense
# build a data set
dataset = pd.DataFrame(randn(100, 2), columns='X1 X2'.split())
dataset["Y"]=dataset["X1"]+dataset["X2"]
# seperate X and y
X = dataset.iloc[:, 0:2].values
Y = dataset.iloc[:, 2].values
# define classifier
def build_classifier():
classifier = Sequential()
classifier.add(Dense(units = 2, kernel_initializer = 'uniform', activation = 'relu', input_dim = 2))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
return classifier
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 1, epochs = 4)
class testnjob():
def run():
accuracies = cross_val_score(estimator = classifier, X = X, y = Y, cv = 5, n_jobs = -1)
return(accuracies)
if __name__ == '__main__':
accuracies = testnjob.run()
错误消息是:
ImportError: [joblib] Attempting to do parallel computing without protecting
your import on a system that does not support forking. To use parallel-
computing in a script, you must protect your main loop using
"if __name__ == '__main__'". Please see the joblib documentation on Parallel
for more information
如果我设置n_jobs = 1,代码就有效 .
有没有办法解决这个问题?
补充:代码适用于linux虚拟机 . 我在Virtualbox,anaconda(python 3.6)spyder(Tensorflow后端)上尝试使用Ubuntu .
补充:我在pycharm中尝试了相同的代码,出现了不同的错误消息:
AttributeError: Can't get attribute 'build_classifier' on
<module '__main__' (built-in)>
谢谢!
1 回答
你可以尝试这个,因为你使用spyder它应该工作正常:
Code
希望这可以帮助 .
spyder和n_jobs的类似问题通过我的帖子解决link
EDIT
我修改了代码的最后一部分,它在Windows 8.1上运行正常 .
另外,我使用:Theano后端 .
Changed part:
截图: