import keras
from keras.layers import Convolution1D
from skopt.space import Integer
from skopt.utils import use_named_args
from keras.optimizers import Adam
from skopt import plots, gp_minimize
import random
import numpy as np


space = [Integer(2, 4, name='kernel_size', dtype=int)]

@use_named_args(space)
def objective_function(kernel_size):
    model = keras.Sequential()
    model.add(Convolution1D(filters=64, kernel_size=kernel_size))
    model.compile(optimizer=Adam(), loss='binary_crossentropy',
                  metrics=['acc'])
    return random.randint(0, 100)

res = gp_minimize(objective_function, space, n_calls=10, verbose=True)