K-fold cross validation of scikit-learn with confusion matrix of Keras

I intend to display confusion matrix using Keras while K-fold of scikit-learn. My code using Keras is:

import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense

seed = 7
numpy.random.seed(seed)

# load dataset
dataframe = pandas.read_csv("BolMov.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:24].astype(float)
Y = dataset[:,24]

model = Sequential()
model.add(Dense(16, activation='relu'))
model.add(Dense(7, activation='softmax')
model.compile('adam', 'categorical_crossentropy', metrics=['accuracy'])

y_cat = to_categorical(Y)
result = model.fit(X, y_cat, verbose=0, epochs=50)

plot_loss_accuracy(result)

y_pred = model.predict_classes(X, verbose=0)

print(classification_report(y, y_pred))
plot_confusion_matrix(model, X, y)

How should I use kfold in this code? Here the author is calling a function. What I believe is that if I do so in my code, the model.fit() will be executed twice – once for my Keras code and another time (internally) for the KerasClassifier(). I want that the model.fit() executes once only. Help from anyone is appreciated.