According to the docs, in CEL the weights you have to provide are manual rescaling weights given to each class. This is so the classes with the lowest amount of occurrences will have the same influence on the loss than the classes with the highest amount of occurrences.
Here is a small script that could help you get these weights:
n_classes = 13
total_pxls = np.zeros((n_classes,1))
for img_name in file_list:
    print("image: %s"%img_name)
    new_lbl = cv2.imread(img_name,-1)
    new_lbl = np.array(new_lbl, dtype=np.uint8)
    for k in range(0,n_classes):
        total_pxls[k] = np.sum(new_lbl==k)+total_pxls[k]

no_total_pxls = np.sum(total_pxls[1::])
medians = [total_pxls[k]/no_total_pxls for k in range(1,n_classes)]
median_all = np.mean(medians)
my_freqs = median_all/(np.float64(medians)+(10E-14))
print(['%4.4f'%x for x in np.round(np.float64(my_freqs),3)])#these are the weights