如何将字符串转换为tensorflow模型？

import tensorflow as tf
from tensorflow import keras
import numpy as np

# 1. 定义模型结构
model = keras.Sequential([
    keras.layers.Dense(10, input_shape=(10,), activation='relu'),
    keras.layers.Dense(1)
])

# 2. 加载模型权重
model.load_weights('model_weights.h5')

# 3. 解析字符串
def parse_string(string):
    # 解析字符串为数值特征
    features = string.split(',')
    features = [float(feature) for feature in features]
    return features

# 4. 数据预处理
def preprocess_features(features):
    # 特征缩放到[0, 1]范围
    features = np.array(features)
    features = (features - np.min(features)) / (np.max(features) - np.min(features))
    return features

# 5. 输入模型
def string_to_model_output(string):
    features = parse_string(string)
    preprocessed_features = preprocess_features(features)
    model_input = np.array([preprocessed_features])
    model_output = model.predict(model_input)
    return model_output

# 测试转换效果
input_string = '1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0'
output = string_to_model_output(input_string)
print(output)