So, what do you recommend I should do?
I wish to implement model like this
convnet_model = convnet_model_()
first_input = Input(shape=(224,224,3))
first_conv = Conv2D(96, kernel_size=(8, 8),strides=(16,16), padding='same')(first_input)
first_max = MaxPool2D(pool_size=(3,3),strides = (4,4),padding='same')(first_conv)
first_max = Flatten()(first_max)
first_max = Lambda(lambda  x: K.l2_normalize(x,axis=1))(first_max)
second_input = Input(shape=(224,224,3))
second_conv = Conv2D(96, kernel_size=(8, 8),strides=(32,32), padding='same')(second_input)
second_max = MaxPool2D(pool_size=(7,7),strides = (2,2),padding='same')(second_conv)
second_max = Flatten()(second_max)
second_max = Lambda(lambda  x: K.l2_normalize(x,axis=1))(second_max)
merge_one = concatenate([first_max, second_max])

merge_two = concatenate([merge_one, convnet_model.output])
emb = Dense(4096)(merge_two)
l2_norm_final = Lambda(lambda  x: K.l2_normalize(x,axis=1))(emb)

final_model = Model(inputs=[first_input, second_input, convnet_model.input], outputs=l2_norm_final)