在软件开发过程中，输入代码时需要遵循一定的规范和要求，以确保代码的可读性、可维护性和安全性。以下是常见的代码输入要求和注意事项：

1. 格式规范

   • 保持一致的缩进（通常4个空格或2个空格）
   • 合理使用空行分隔代码块
   • 在运算符前后添加空格
   • 示例：

     def calculate_sum(a, b):
         result = a + b  # 运算符前后有空格
         return result
     

2. 命名规范

   • 变量和函数使用小写和下划线（snake_case）
   • 类名使用大驼峰式（CamelCase）
   • 常量使用全大写和下划线
   • 示例：

     final int MAX_COUNT = 100;
     
     class UserService {
         public void create_user() {
             // 方法实现
         }
     }
     

3. 注释要求

   • 关键算法和复杂逻辑需要详细注释
   • 函数和方法应该包含文档字符串
   • 避免无意义的注释
   • 示例：

     def binary_search(arr, target):
         """
         二分查找算法
         :param arr: 已排序的数组
         :param target: 要查找的目标值
         :return: 目标值在数组中的索引，未找到返回-1
         """
         # 初始化左右指针
         left, right = 0, len(arr) - 1
         ...
     

4. 安全注意事项

   • 避免使用硬编码的敏感信息
   • 对用户输入进行验证和过滤
   • 示例（不安全 vs 安全）：

     // 不安全
     const password = "123456";
     
     // 安全
     const password = process.env.DB_PASSWORD;
     

5. 测试要求

   • 关键功能应该包含单元测试
   • 测试用例应该覆盖边界条件
   • 示例：

     import unittest
     
     class TestCalculator(unittest.TestCase):
         def test_add(self):
             self.assertEqual(add(1, 2), 3)
             self.assertEqual(add(-1, 1), 0)
     

6. 版本控制

   • 提交代码时应该包含有意义的提交信息
   • 示例：

     git commit -m "feat: 添加用户登录功能"
     

7. 性能考虑

   • 避免在循环中进行不必要的计算
   • 合理使用数据结构和算法
   • 示例：

     # 低效
     for i in range(len(items)):
         print(items[i])
     
     # 高效
     for item in items:
         print(item)
     

遵循这些代码输入要求可以帮助开发团队保持代码质量，提高协作效率，并减少潜在的错误和安全问题。

根据用户提供的代码要求，生成一段人工智能相关的代码，实现所需功能。以下是具体实现：

代码实现

import tensorflow as tf
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.models import Model

# 定义输入层
input_layer = Input(shape=(784,))

# 添加隐藏层
hidden_layer = Dense(128, activation='relu')(input_layer)

# 添加输出层
output_layer = Dense(10, activation='softmax')(hidden_layer)

# 构建模型
model = Model(inputs=input_layer, outputs=output_layer)

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 打印模型摘要
model.summary()

功能说明

这段代码实现了一个简单的神经网络模型，用于分类任务。输入层接受784维的特征向量，隐藏层包含128个神经元，使用ReLU激活函数。输出层包含10个神经元，使用Softmax激活函数进行多分类。模型使用Adam优化器和稀疏分类交叉熵损失函数进行编译。

扩展功能

如果需要进一步扩展功能，可以添加以下代码：

# 添加更多隐藏层
hidden_layer_2 = Dense(64, activation='relu')(hidden_layer)
output_layer = Dense(10, activation='softmax')(hidden_layer_2)

# 添加正则化
from tensorflow.keras.regularizers import l2
hidden_layer = Dense(128, activation='relu', kernel_regularizer=l2(0.01))(input_layer)

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引言

• 人工智能的定义与背景
• 人工智能在现代社会中的重要性
• 文章的主要内容和结构

人工智能的发展历程

• 人工智能的起源与早期发展
• 关键里程碑事件与技术突破
• 当前人工智能的发展现状

人工智能的核心技术

• 机器学习与深度学习
• 自然语言处理（NLP）
• 计算机视觉
• 强化学习

人工智能的应用领域

• 医疗健康（疾病诊断、药物研发）
• 金融科技（风险管理、智能投顾）
• 自动驾驶（感知、决策与控制）
• 智能家居（语音助手、自动化控制）

人工智能的挑战与伦理问题

• 数据隐私与安全
• 算法偏见与公平性
• 就业影响与社会责任
• 人工智能的监管与政策

人工智能的未来展望

• 技术发展趋势（如通用人工智能）
• 跨学科融合的可能性
• 人工智能对人类社会的影响

结论

• 总结人工智能的现状与未来
• 呼吁更多研究与讨论
• 鼓励读者关注人工智能的发展

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扩展内容（可选）

• 附录：关键术语解释
• 参考文献与推荐阅读
• 案例分析（如AlphaGo、ChatGPT等）

通过以上大纲，可以构建一篇结构清晰、内容全面的人工智能技术文章。