如何在Keras中使用Hausdorff度量？

from keras import backend as K
from keras.losses import mean_squared_error

def hausdorff_distance(y_true, y_pred):
    # 计算真实值和预测值之间的距离矩阵
    distance_matrix = K.sqrt(K.sum(K.square(K.expand_dims(y_true, axis=1) - K.expand_dims(y_pred, axis=0)), axis=-1))
    
    # 计算真实值到预测值的最小距离
    min_distance_true = K.min(distance_matrix, axis=1)
    
    # 计算预测值到真实值的最小距离
    min_distance_pred = K.min(distance_matrix, axis=0)
    
    # 返回最大的最小距离作为Hausdorff距离
    return K.max(K.concatenate([min_distance_true, min_distance_pred], axis=0))

model.compile(optimizer='adam', loss=hausdorff_distance, metrics=[hausdorff_distance])