How to save model after sklearn gridsearchcv using imblearn pipeline : TypeError: can't pickle _thread.RLock objects

Question

The problem i am facing is this that I have performed grid search using imblearn pipeline and using sklearn gridsearchcv as I was dealing with an extremely unbalanced dataset, but when I try to save the model , I am getting the error 'TypeError: can't pickle _thread.RLock objects'. The statements that I an using to save the model are

Case-1:

import pickle

pickle.dump(grid_result,open(model_filename,'wb'))

Case-2:

from sklearn.externals import joblib

joblib.dump(grid_result.best_estimator_, 'GS_obj.pkl')

1) For a Binary Classification problem, I have defined model architecture as given below

Build Function to create model, required by KerasClassifier

def create_model(optimizer_val='RMSprop',hidden_layer_size=16,activation_fn='relu',dropout_rate=0.1,regularization_fn=tf.keras.regularizers.l1(0.001),kernel_initializer_fn=tf.keras.initializers.glorot_uniform,bias_initializer_fn=tf.keras.initializers.zeros):

    model = tf.keras.models.Sequential([

    tf.keras.layers.Dropout(0.2),                                       

    tf.keras.layers.Input(shape=(D,)),

     tf.keras.layers.Dense(units=1024, activation='relu',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), # 1st Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),

    tf.keras.layers.Dense(units=1024, activation='relu',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #2nd Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),

    tf.keras.layers.Dense(units=1024, activation='relu',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #3rd Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),   

    tf.keras.layers.Dense(units=1024, activation='relu',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #4th Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),       

    tf.keras.layers.Dense(units=1024, activation='relu',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #5th Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),       

    tf.keras.layers.Dense(units=1024, activation='relu',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #6th Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),         

    tf.keras.layers.Dense(units=1024, activation='relu',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #7th Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),       

    tf.keras.layers.Dense(units=1024, activation='relu',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #8th Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),           

    tf.keras.layers.Dense(units=1024, activation=activation_fn,kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #9th Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),     

    tf.keras.layers.Dense(units=1024, activation=activation_fn,kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn), #10th Layer

    tf.keras.layers.BatchNormalization(),   

    tf.keras.layers.Dropout(dropout_rate),   

   tf.keras.layers.Dense(units=1,activation='sigmoid',kernel_regularizer=regularization_fn,kernel_initializer=kernel_initializer_fn,bias_initializer=bias_initializer_fn) ])



    model.compile(optimizer=optimizer_val, loss='binary_crossentropy',metrics=['binary_accuracy'])

    return model



#Create the model with the wrapper

model = tf.keras.wrappers.scikit_learn.KerasClassifier(build_fn=create_model,verbose=2) #We donot specify batch_size and epochs here as it is part of the parameter grid

2)Built the parameter search grid as given below

Initialize the parameter grid

nn_param_grid = {

    'NN_clf__epochs': [500],     

    'NN_clf__batch_size':[32],

    'NN_clf__optimizer_val': ['Adam','SGD'],

    'NN_clf__hidden_layer_size': [1024],

    'NN_clf__activation_fn': ['relu'],     

    'NN_clf__dropout_rate': [0.5],   

    'NN_clf__regularization_fn':['L1L2'],

    'NN_clf__kernel_initializer_fn':['glorot_normal', 'glorot_uniform'], #Works   

    'NN_clf__bias_initializer_fn':[tf.keras.initializers.zeros]   

}

2) Instantiated a pipeline and performed GridSearch as given below

Instantiate the SMOTE object

smote_obj=SMOTE(sampling_strategy='minority',random_state=42,n_jobs=-1)

Instantiate the Scaler Object

scaler_obj=StandardScaler()

Instantiate the Pipeline

steps = [('standardize', scaler_obj),('oversample', smote_obj),('NN_clf', model)]

pipe_clf = Pipeline(steps)

pipe_clf

Perform GridSearchCV

grid = GridSearchCV(estimator=pipe_clf, param_grid=nn_param_grid, verbose=2, cv=10,scoring='precision',return_train_score=False,n_jobs=-1) 

grid_result = grid.fit(X_train, y_train)

Please advise as to how can I save the model as using the given below, I am unable to save the model and getting the error 'TypeError: can't pickle _thread.RLock objects'