How to remove the pickling error while doing k-model cross validation using scikit learn through the keras library

Question

This was the code for building the artificial neural network and the classifier. It was simple churn modelling for determining whether a customer will leave a bank or not.

---

#Building the ANN
import keras
from keras.models import Sequential
from keras.layers import Dense

#Initializing the ANN
 classifier = Sequential()

#Adding input layer and hidden layer iinto ANN
classifier.add(Dense(6, kernel_initializer = 'glorot_uniform', activation = 'relu', input_shape = 
(11,)))

#Adding second hidden layer
classifier.add(Dense(6, kernel_initializer = 'glorot_uniform', activation = 'relu'))

#Adding the output/final layer
classifier.add(Dense(1, kernel_initializer = 'glorot_uniform', activation = 'sigmoid'))

#Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics=('accuracy'))

#Fitting the ANN on trainig set using fit method
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)

#Making prediction and analyzing the dataset
y_prediction = classifier.predict(X_test)

#Converting the probablities into definite results for model validation
y_prediction = (y_prediction > 0.5)

#Making confusion matrix for evaluating the resuts
from sklearn.metrics import confusion_matrix  
cm = confusion_matrix(y_test, y_prediction)

#Evaluating, improving and tuning the ANN
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 build_classifier():
   classifier = Sequential()
   classifier.add(Dense(6, kernel_initializer = 'glorot_uniform', activation = 'relu', input_shape = 
   (11,)))
   classifier.add(Dense(6, kernel_initializer = 'glorot_uniform', activation = 'relu'))
   classifier.add(Dense(1, kernel_initializer = 'glorot_uniform', activation = 'sigmoid'))
   classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics=('accuracy'))
   return classifier

classifier = KerasClassifier(build_fn = build_classifier(), batch_size = 10, epochs = 100)
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
 ---

And this was the error

---

RemoteTraceback: """ Traceback (most recent call last): File "C:\Users\BSNL\anaconda3\lib\site-packages\joblib\externals\loky\backend\queues.py", line 153, in _ feed obj = dumps(obj, reducers=reducers) File "C:\Users\BSNL\anaconda3\lib\site-packages\joblib\externals\loky\backend\reduction.py", line 271, in dumps dump(obj, buf, reducers=reducers, protocol=protocol) File "C:\Users\BSNL\anaconda3\lib\site-packages\joblib\externals\loky\backend\reduction.py", line 264, in dump _LokyPickler(file, reducers=reducers, protocol=protocol).dump(obj) File "C:\Users\BSNL\anaconda3\lib\site-packages\joblib\externals\cloudpickle\cloudpickle_fast.py", line 563, in dump return Pickler.dump(self, obj) TypeError: cannot pickle 'weakref' object """

The above exception was the direct cause of the following exception:

Traceback (most recent call last):

File "C:\Users\BSNL\Documents\Deep_Learning_A_Z\Volume 1 - Supervised Deep Learning\Part 1 - Artificial Neural Networks (ANN)\Section 4 - Building an ANN\ANN.py", line 78, in accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)

File "C:\Users\BSNL\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f return f(**kwargs)

File "C:\Users\BSNL\anaconda3\lib\site-packages\sklearn\model_selection_validation.py", line 401, in cross_val_score cv_results = cross_validate(estimator=estimator, X=X, y=y, groups=groups,

File "C:\Users\BSNL\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 72, in inner_f return f(**kwargs)

File "C:\Users\BSNL\anaconda3\lib\site-packages\sklearn\model_selection_validation.py", line 242, in cross_validate scores = parallel(

File "C:\Users\BSNL\anaconda3\lib\site-packages\joblib\parallel.py", line 1061, in call self.retrieve()

File "C:\Users\BSNL\anaconda3\lib\site-packages\joblib\parallel.py", line 940, in retrieve self._output.extend(job.get(timeout=self.timeout))

File "C:\Users\BSNL\anaconda3\lib\site-packages\joblib_parallel_backends.py", line 542, in wrap_future_result return future.result(timeout=timeout)

File "C:\Users\BSNL\anaconda3\lib\concurrent\futures_base.py", line 432, in result return self.__get_result()

File "C:\Users\BSNL\anaconda3\lib\concurrent\futures_base.py", line 388, in __get_result raise self._exception

PicklingError: Could not pickle the task to send it to the workers.

Answer 1

putting n_jobs = 1 worked because I had also did a mistake of putting brackets in Kerasclassifier's build_fn argument i.e. use build_classifier instead of build_classifier().