Grid-Search Voting Classifier containing a Keras model - python-3.x

Question

I am trying to train a VotingClassifier containing a Keras model using GridSearchCV.
Here is the code:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import adam
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.ensemble import VotingClassifier
from sklearn import datasets
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
# pretend data
X, y = datasets.make_classification(n_samples=100, n_features=20)
scaler = StandardScaler()
# create model
def create_model():
model = Sequential()
model.add(Dense(20, kernel_initializer="uniform", activation='relu', input_shape=(20,)))
model.add(Dense(30, kernel_initializer="uniform", activation='relu'))
model.add(Dense(10, kernel_initializer="uniform", activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
optimizer = adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
return model
keras_model = KerasClassifier(build_fn=create_model)
keras_model._estimator_type = "classifier"
eclf = VotingClassifier(
estimators=[('svc',SVC(probability=True)), ('keras_model', keras_model)]
, voting='soft')
# Test - fit the viting classifier without grid search
eclf.fit(X, y)
print('The VotingClassifier can be fit outside of gridsearch\n')
# parameters to grid search
params = [{'svc__C':[0.01,0.1]}, ]
grid = GridSearchCV(eclf,params,cv=2,scoring='accuracy', verbose=1)
grid.fit(X,y)
I get the following error:
ValueError: The estimator KerasClassifier should be a classifier.
When I train the VotingClassifier outside of GridSearchCV no error occurs, however when I train it within GridSearchCV, I get the error message. This other question, VotingClassifier with pipelines as estimators, has the same error (without using GridSearch) and was fixed by a line asserting that the keras model is a classifier which i have also included:
keras_model._estimator_type = "classifier"
This did not fix the problem here.
Any suggestions?