How to create pipeline for Neural Network models?
I am able to achieve this with below code 

Thanks  @JahKnows

scaler = StandardScaler()
model = Sequential()

model.add(Dense(120, input_dim=46,activation='relu'))
model.add(Dropout(0.1, noise_shape=None, seed=None))

model.add(Dense(80, activation='relu'))
model.add(Dropout(0.1, noise_shape=None, seed=None))

model.add(Dense(40, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

pipeline = Pipeline([("scaler",scaler),("model",model)])
pipeline.fit(X_train,y_train, model__epochs=50, model__batch_size=20, model__validation_data = (X_test,y_test))

# evaluate the model
nn_pred = grid_search.predict(X_test)

print('Accuracy of NN on test is:',accuracy_score(y_test,nn_pred))
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))