%%time
seed = 7
numpy.random.seed(seed)
batch_size = 128
epochs = 10

model_CV = KerasClassifier(build_fn=create_model, epochs=epochs,
                           batch_size=batch_size, verbose=1)
# define the grid search parameters
init_mode = ['uniform', 'lecun_uniform', 'normal', 'zero',
             'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']

param_grid = dict(init_mode=init_mode)
grid = GridSearchCV(estimator=model_CV, param_grid=param_grid, n_jobs=-1, cv=3)
grid_result = grid.fit(x_train, y_train)