如何使用从经过训练的keras模型中提取的tensorflow模型

from keras.models import load_model
model = load_model('path_to_keras_model.h5')

import tensorflow as tf
tf.compat.v1.disable_eager_execution()  # 禁用TensorFlow 2.0的eager execution

sess = tf.compat.v1.keras.backend.get_session()
tf.compat.v1.saved_model.simple_save(
    sess,
    'path_to_save_tf_model',
    inputs={'input_name': model.input},
    outputs={t.name: t for t in model.outputs}
)

import tensorflow as tf
loaded_model = tf.compat.v1.saved_model.load_v2('path_to_save_tf_model')

# 获取输入和输出张量
input_tensor = loaded_model.signatures['serving_default'].inputs[0]
output_tensor = loaded_model.signatures['serving_default'].outputs[0]

# 进行预测
predictions = loaded_model(input_tensor)