我创建了一个神经网络模型并创建了一个集成学习模型，即投票模型。我将神经网络与随机森林和 xgboost 相结合。现在我保存了模型并尝试将其加载到另一个 Jupiter 笔记本，但出现此错误 AttributeError: Can't get attribute 'create_model' on <module 'main'>

这是在第一个笔记本中创建模型的代码

from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
import numpy
 
# Function to create model, required for KerasClassifier
def create_model(input_shape=66):
    #x_shape= data_x.shape
    #input_dim=x_shape[1]
    # create model
    model = Sequential()
    model.add(Dense(12, input_dim=66, activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1,activation='sigmoid'))
# Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

seed = 7
numpy.random.seed(seed)
Kc_model = KerasClassifier(
                create_model, # Pass in function
                input_shape=66, # Pass in the dimensions to above function
                epochs=100,
                batch_size=32,
                verbose=False)
Kc_model._estimator_type = "classifier"



Kc_model.fit(x_train, y_train, epochs=100,batch_size=10)

rf = RandomForestClassifier(max_depth=15, random_state=0)
rf.fit(x_train,y_train)
rf_y_pred = rf.predict(x_test)

#Model Score
print("The accuracy score for Random Forest Classifier is")
print("Accuracy:{}%".format(round(metrics.accuracy_score(y_test, rf_y_pred)*100)))
print("Training:{}%".format(round(rf.score(x_train, y_train)*100)))
print("Test set: {}%".format(round(rf.score(x_test, y_test)*100)))

xgboost_model = XGBClassifier()
xgboost_model.fit(x_train, y_train)
xgboost_y_pred = xgboost_model.predict(x_test)

print("The accuracy score for Voting XGB Classifier is")
print("Accuracy:{}%".format(round(metrics.accuracy_score(y_test, xgboost_y_pred)*100)))

print("Training:{}%".format(round(xgboost_model.score(x_train, y_train)*100)))
print("Test set: {}%".format(round(xgboost_model.score(x_test, y_test)*100)))

from keras.wrappers.scikit_learn import KerasClassifier
import scikeras
from tensorflow import keras
voting = VotingClassifier(
    estimators = [('rf',rf),('xgboost_model',xgboost_model),('Kc_model',Kc_model) ],
    voting='soft')
#reshaping=y_test.reshape(2712,1)
voting_model =voting.fit(x_train, y_train)
voting_pred = voting_model.predict(x_test)
#Model Score
print("The accuracy score for Voting Classifier is")
print("Training:{}%".format(round(voting_model.score(x_train, y_train)*100)))
print("Test set: {}%".format(round(voting_model.score(x_test, y_test)*100)))

import pickle
# save
with open('voting_model.pkl','wb') as f:
    pickle.dump(Kc_model,f)

在我尝试加载模型的第二个笔记本中，出现错误，如下所示

import pickle
import pandas as pd
with open('voting_model.pkl', 'rb') as f:
    Kc_model = pickle.load(f)

import pickle
# save
with open('voting_model.pkl','wb') as f:
    pickle.dump(Kc_model.model, f)

with open('voting_model.pkl', 'rb') as f:
    model = pickle.load(f)

# Predict something.
model.predict(X_test)

# Save this as well.
with open('voting_model_classes.pkl', 'wb') as f:
    pickle.dump(Kc_model.classes_, f)

import pickle
from keras.wrappers.scikit_learn import KerasClassifier

def load_model():
    with open('voting_model.pkl', 'rb') as f:
        return pickle.load(f)
    
def load_classes():
    with open('voting_model_classes.pkl', 'rb') as f:
        return pickle.load(f)
    
Kc_model = KerasClassifier(
                load_model,
                epochs=100,
                batch_size=32,
                verbose=False)
Kc_model._estimator_type = "classifier"
# We need to manually call it because it will only be called once the classifier is re-fitted.
Kc_model.model = load_model()
Kc_model.classes_ = load_classes()

# Now you can use Kc_model as KerasClassifier.