model1 = np.zeros((10, 16, 16), dtype=np.float) model2 = np.zeros((10, 16, 16, 16), dtype=np.float) model3 = np.zeros((10, 16, 16, 16, 16), dtype=np.float) for i in range(10): rule1 = np.zeros((16, 16), dtype=np.float) rule2 = np.zeros((16, 16, 16), dtype=np.float) rule3 = np.zeros((16, 16, 16, 16), dtype=np.float) idxes = check_data(i, training_size) size = len(idxes) d = prep_training_data(idxes) for img in d: btb = BTB(2, img) btb.perception() rule1 = rule1 + btb.lvl1 rule2 = rule2 + btb.lvl2 rule3 = rule3 + btb.lvl3 rule1 = rule1 / size rule2 = rule2 / size rule3 = rule3 / size model1[i, :, :] = rule1 model2[i, :, :, :] = rule2 model3[i, :, :, :, :] = rule3 """ for i in range(10): print(i) for j in range(10):