如何在keras中实现损失函数的ssim？

import tensorflow as tf
from tensorflow.keras import backend as K

def ssim_loss(y_true, y_pred):
    # SSIM参数
    C1 = 0.01 ** 2
    C2 = 0.03 ** 2

    # 均值
    mu_true = K.mean(y_true)
    mu_pred = K.mean(y_pred)

    # 方差
    var_true = K.var(y_true)
    var_pred = K.var(y_pred)

    # 协方差
    covar_true_pred = K.mean((y_true - mu_true) * (y_pred - mu_pred))

    # SSIM计算公式
    numerator = (2 * mu_true * mu_pred + C1) * (2 * covar_true_pred + C2)
    denominator = (K.square(mu_true) + K.square(mu_pred) + C1) * (var_true + var_pred + C2)
    ssim = numerator / denominator

    # 返回1-SSIM作为损失函数
    return 1 - ssim

model.compile(optimizer='adam', loss=ssim_loss)