TypeError: can't pickle _thread.lock objects

`# -*- coding: utf-8 -*-
"""
Created on Mon Jan 15 16:13:44 2018

@author: Joish
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dataset = pd.read_csv("Churn_Modelling.csv")
X = dataset.iloc[:,3:13].values
Y = dataset.iloc[:,13:].values

from sklearn.preprocessing import OneHotEncoder,LabelEncoder,StandardScaler

enc1=LabelEncoder()
enc2=LabelEncoder()
X[:,1] = enc1.fit_transform(X[:,1])
X[:,2] = enc2.fit_transform(X[:,2])

one = OneHotEncoder(categorical_features=[1])
X=one.fit_transform(X).toarray()

X = X[:,1:]

from sklearn.model_selection import train_test_split
Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,Y,random_state=0,test_size=0.2)

scale = StandardScaler()
scale.fit_transform(Xtrain)
scale.transform(Xtest)

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

def func1():
    net = Sequential()
    net.add(Dense(input_dim=11,units=6,activation="relu",kernel_initializer='uniform'))
    net.add(Dense(units=6,activation="relu",kernel_initializer='uniform'))
    net.add(Dense(units=1,activation="sigmoid",kernel_initializer='uniform'))
    net.compile(optimizer='adam',metrics=['accuracy'],loss='binary_crossentropy')

    return net

classfier = KerasClassifier(build_fn=func1(),batch_size=10, epochs=100)
cross = cross_val_score(estimator=classfier, X=Xtrain, y=Ytrain, cv=10 , n_jobs=-1)

ERROR: Traceback (most recent call last):

File "", line 1, in cross = cross_val_score(estimator=classfier, X=Xtrain, y=Ytrain, cv=10 , n_jobs=-1)

File "C:\Users\Joish\Anaconda3\envs\project\lib\site-packages\sklearn\model_selection_validation.py", line 342, in cross_val_score pre_dispatch=pre_dispatch)

File "C:\Users\Joish\Anaconda3\envs\project\lib\site-packages\sklearn\model_selection_validation.py", line 206, in cross_validate for train, test in cv.split(X, y, groups))

File "C:\Users\Joish\Anaconda3\envs\project\lib\site-packages\sklearn\externals\joblib\parallel.py", line 779, in call while self.dispatch_one_batch(iterator):

File "C:\Users\Joish\Anaconda3\envs\project\lib\site-packages\sklearn\externals\joblib\parallel.py", line 620, in dispatch_one_batch tasks = BatchedCalls(itertools.islice(iterator, batch_size))

File "C:\Users\Joish\Anaconda3\envs\project\lib\site-packages\sklearn\externals\joblib\parallel.py", line 127, in init self.items = list(iterator_slice)

File "C:\Users\Joish\Anaconda3\envs\project\lib\site-packages\sklearn\model_selection_validation.py", line 206, in for train, test in cv.split(X, y, groups))

File "C:\Users\Joish\Anaconda3\envs\project\lib\site-packages\sklearn\base.py", line 62, in clone new_object_params[name] = clone(param, safe=False)

File "C:\Users\Joish\Anaconda3\envs\project\lib\site-packages\sklearn\base.py", line 53, in clone return copy.deepcopy(estimator)

File "C:\Users\Joish\Anaconda3\envs\project\lib\copy.py", line 180, in deepcopy y = _reconstruct(x, memo, *rv)

File "C:\Users\Joish\Anaconda3\envs\project\lib\copy.py", line 280, in _reconstruct state = deepcopy(state, memo)