如何在Keras上使用Glorot重新初始化预训练模型的权重？

import keras
from keras.models import load_model
from keras.initializers import glorot_uniform

model = load_model('pretrained_model.h5')

initializer = glorot_uniform()
for layer in model.layers:
    if hasattr(layer, 'kernel_initializer'):
        layer.kernel.initializer.run(session=keras.backend.get_session())
    if hasattr(layer, 'bias_initializer'):
        layer.bias.initializer.run(session=keras.backend.get_session())

for layer in model.layers:
    layer.trainable = False

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_val, y_val))