如何在Keras中以一种简单的方式分配class_weight？

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

# 假设我们有一个二分类任务，类别0和类别1
num_classes = 2

# 生成一些样本数据
X_train = np.random.random((1000, 10))
y_train = np.random.randint(num_classes, size=(1000, 1))

# 计算每个类别的权重
class_weights = {0: 1, 1: 2}  # 类别0的权重为1，类别1的权重为2

# 创建模型
model = Sequential()
model.add(Dense(64, activation='relu', input_dim=10))
model.add(Dense(1, activation='sigmoid'))

# 编译模型并设置class_weight参数
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, class_weight=class_weights, epochs=10, batch_size=32)