我在张量流中创建一个神经网络，需要最小化以下损失函数： 其中表示真实值，表示预测值。由于损失函数不可微，因此在使用梯度下降时会成为一个问题。$\frac{max(y,p)}{min(y,p)}$$y$$p$

更新，现在我正在尝试实现：
$p > y \rightarrow loss = log(p)-log(y)$
$p < y \rightarrow loss = log(y)-log(p)$
$p = y \rightarrow loss = log(y)-log(y) = log(p)-log(p)$

这是我的代码：

def custom_loss(y_true, pred):
    if pred > y_true:
        custom_loss = K.log(pred) - K.log(y_true)
    elif pred < y_true:
        custom_loss = K.log(y_true) - K.log(pred)
    else:
        custom_loss = K.log(pred)-K.log(pred)

    return custom_loss


if __name__ == '__main__':
    run = 1

    X = np.load("vectors_normal_2way.npy")

    with open("target2.pickle", "rb") as file:
        target_dict = pickle.load(file)

    target_strings = [*target_dict]

    Y = np.array([])

    for target_value in target_strings:
        Y = np.append(Y, target_dict.get(target_value))

    for i in range(run):
        X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)
        model = get_model()
        start = time.time()
        model.fit(X_train, Y_train, verbose = 1, epochs = 1, batch_size = 32)
        end_time = time.time()-start
        pred = model.predict(X_test)[:, 0]

但我收到以下错误：

tensorflow.python.framework.errors_impl.InvalidArgumentError:  The second input must be a scalar, but it has shape [32]
     [[{{node gradient_tape/custom_loss/cond/StatelessIf/gradient_tape/custom_loss/weighted_loss/Mul/_17}}]] [Op:__inference_train_function_1040]
 

当我有 batch_size = 1 时代码可以运行