Deep learning 是否可以为LSTM使用投票分类器?

Deep learning 是否可以为LSTM使用投票分类器?,deep-learning,lstm,recurrent-neural-network,ensemble-learning,Deep Learning,Lstm,Recurrent Neural Network,Ensemble Learning,在这里,我试图对LSTM、BILSTM、GRU和BIGRU的输出使用投票分类器,但得到的是ValueError:Sequential估计器应该是一个分类器错误。如何解决此错误,我们非常感谢您在这方面提供的任何帮助。 这是我试图运行的代码 from sklearn.ensemble import VotingClassifier from keras.wrappers.scikit_learn import KerasClassifier #create a dictionary of our m

在这里,我试图对LSTM、BILSTM、GRU和BIGRU的输出使用投票分类器,但得到的是ValueError:Sequential估计器应该是一个分类器错误。如何解决此错误,我们非常感谢您在这方面提供的任何帮助。 这是我试图运行的代码

from sklearn.ensemble import VotingClassifier
from keras.wrappers.scikit_learn import KerasClassifier
#create a dictionary of our models
#nn = KerasClassifier()
#nn._estimator_type = "classifier"
estimators=[('lstm', model1), ('bilstm', model2), ('gru', model3),('bigru',model4)]
#create our voting classifier, inputting our models
ensemble = VotingClassifier(estimators, voting='hard')
#fit model to training data
ensemble.fit(x_train, y_train)
这是完整的错误消息

ValueError                                Traceback (most recent call last)
<ipython-input-17-6474190dd669> in <module>()
      9 ensemble = VotingClassifier(estimators, voting='hard')
     10 #fit model to training data
---> 11 ensemble.fit(x_train, y_train)

2 frames
/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_base.py in _validate_estimators(self)
    247                 raise ValueError(
    248                     "The estimator {} should be a {}.".format(
--> 249                         est.__class__.__name__, is_estimator_type.__name__[3:]
    250                     )
    251                 )

ValueError: The estimator Sequential should be a classifier.
ValueError回溯(最近一次调用)
在()
9集合=VotingClassifier(估计器,投票='hard')
10#将模型与培训数据进行拟合
--->11合身(x_系列、y_系列)
2帧
/usr/local/lib/python3.6/dist-packages/sklearn/employee//u base.py in\u validate\u估计器(self)
247提升值错误(
248“估计器{}应为{}格式。”(
-->249 est.\uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu
250                     )
251                 )
ValueError:序列估计器应该是一个分类器。

为此,您需要使用KerasClassifier,它甚至存在于您的代码中(但已注释)。@snoopy博士如果取消注释,我将获得以下错误AttributeError:“KerasClassifier”对象没有属性“call”