如何确保Keras模型权重在每次模型拟合时被随机初始化

import numpy as np
import random
import tensorflow as tf
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense

seed_value = 42

# 设置Python的随机种子
random.seed(seed_value)

# 设置Numpy的随机种子
np.random.seed(seed_value)

# 设置TensorFlow的随机种子
tf.random.set_seed(seed_value)

model = Sequential()
model.add(Dense(64, activation='relu', input_dim=100))
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

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

model.fit(x_train, y_train, epochs=10, batch_size=32, verbose=1)