如何在Keras中使用类权重进行图像分割

import numpy as np
import keras
from keras.models import Model
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate

def unet(input_size=(256, 256, 3)):
    inputs = Input(input_size)
    
    # 编码器
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    
    # 解码器
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)
    up1 = UpSampling2D(size=(2, 2))(conv2)
    
    # 输出层
    outputs = Conv2D(1, 1, activation='sigmoid')(up1)
    
    model = Model(inputs=inputs, outputs=outputs)
    return model

model = unet()

class_weights = {0: 1, 1: 5}  # 类别0的权重为1，类别1的权重为5

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=32, epochs=10, class_weight=class_weights)