企业官网建设流程全解析

由于我的机器非常老旧，所以采用的是依靠cpu训练的模式搭建的。

• ‌硬件特性‌：该机型通常搭载Intel酷睿标压处理器、核显，无独立高性能GPU，训练时优先使用CPU模式，避免显存不足报错。
• ‌软件环境‌：安装Python 3.7~3.8版本，搭配TensorFlow 2.x稳定版，避免高版本框架带来的兼容性问题。
• ‌依赖库安装‌：执行命令安装所需工具包：pip install tensorflow opencv-python numpy matplotlib
• 最后系统选取的是版本如下：
• pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple/
• 从国内镜像站中下载的，没有设置出现了网速不好的情况断开的异常。
• pip install tensorflow opencv-python numpy matplotlib
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple/
Collecting tensorflow
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Collecting libclang>=13.0.0
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Collecting opt_einsum>=2.3.2
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Collecting termcolor>=1.1.0
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Collecting wrapt>=1.11.0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4a/62/ecc969b13b141fef89b888c9760821cb01a86ac8fc953911592c8e1e1522/wrapt-2.3.0-cp311-cp311-win_amd64.whl (80 kB)
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Collecting keras>=3.12.0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8d/b3/c9b848bbdba18e765a8051917c0cc82585b64278ce87895f2e521a27438f/keras-3.15.1-py3-none-any.whl (2.4 MB)
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Collecting h5py<3.15.0,>=3.11.0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/db/0c/6c3f879a0f8e891625817637fad902da6e764e36919ed091dc77529004ac/h5py-3.14.0-cp311-cp311-win_amd64.whl (2.9 MB)
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Collecting ml_dtypes<1.0.0,>=0.5.1
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Collecting contourpy>=1.0.1
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Collecting cycler>=0.10
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Collecting fonttools>=4.28.2
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Collecting kiwisolver>=1.3.1
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/be/6c/28f17390b62b8f2f520e2915095b3c94d88681ecf0041e75389d9667f202/kiwisolver-1.5.0-cp311-cp311-win_amd64.whl (73 kB)
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Collecting pyparsing>=3
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Collecting wheel<1.0,>=0.23.0
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/87/1b/9e33c09813d65e248f7f773119148a612516a4bea93e9c6f545f78455b7c/wheel-0.47.0-py3-none-any.whl (32 kB)
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Collecting namex
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Installing collected packages: namex, libclang, wrapt, wheel, termcolor, pyparsing, optree, opt_einsum, opencv-python, ml_dtypes, kiwisolver, h5py, google_pasta, gast, fonttools, cycler, contourpy, absl-py, matplotlib, astunparse, keras, tensorflow
Successfully installed absl-py-2.5.0 astunparse-1.6.3 contourpy-1.3.3 cycler-0.12.1 fonttools-4.63.0 gast-0.7.0 google_pasta-0.2.0 h5py-3.14.0 keras-3.15.1 kiwisolver-1.5.0 libclang-18.1.1 matplotlib-3.11.1 ml_dtypes-0.5.4 namex-0.1.0 opencv-python-5.0.0.93 opt_einsum-3.4.0 optree-0.19.1 pyparsing-3.3.2 tensorflow-2.21.0 termcolor-3.3.0 wheel-0.47.0 wrapt-2.3.0
• 准备好工具后下载数据源：
• ‌数据集获取‌：下载Kaggle官方的Dogs vs. Cats数据集，从中选取猫狗图片各1000~1500张
• 数据清洗‌：剔除损坏、非猫狗类的异常图片，避免训练过程中断。
  预处理操作‌：将所有图片统一resize为224×224尺寸，按8:2比例划分为训练集和验证集，完成数值归一化、图像随机翻转/亮度调节等增强操作，提升模型泛化能力。
• 在实现图片统一size的时候我们采用了：批处理

• 使用 OpenCV（推荐，稳定性高，适合 AI 训练）

  OpenCV 在处理图像解码异常和大规模批量任务时比 Pillow 更健壮，且支持断点续传逻辑

• pip install opencv-python tqdm
  

• 这个脚本实现了转换：

到这里就基本上实现了环境的搭建和数据源的基本准备条件。

接下来就是开始做数据分配和训练的工作。

预处理操作‌：将所有图片统一resize为224×224尺寸，按8:2比例划分为训练集和验证集，完成数值归一化、图像随机翻转/亮度调节等增强操作，提升模型泛化能力。

由于我的电脑比较老旧，所以我在数据源的处理上使用了1500张图的，设计原则。

那么用于训练就是1200张，用于验证就是300张这样的一个比例操作。

三、基于迁移学习训练基础模型

• ‌预训练模型选择‌：选用轻量型的MobileNet V2作为基础模型，冻结大部分预训练层，仅微调最后几层分类头，大幅减少训练耗时。
• ‌训练配置‌：设置批量大小为16，初始学习率设为0.0001，训练轮次控制在20~30轮，开启早停机制防止过拟合。
• ‌训练监控‌：训练过程中实时观察损失值和准确率变化，在E431的CPU环境下，该规模的数据集完整训练耗时约2~3小时。
• 在这里呢就使用了这个脚本进行的训练。
• 训练数据源是有格式要求的。
• 设置相关参数会得到：

• 四、转换生成catsvsdogs.tflite文件

• ‌模型导出‌：将训练完成的Keras格式模型（.h5或SavedModel格式）保存到本地。
• ‌格式转换‌：使用TensorFlow官方的tf.lite.TFLiteConverter工具加载基础模型，可选择动态范围量化策略，进一步压缩模型体积、提升后续推理速度。
• ‌导出验证‌：转换完成后得到最终的catsvsdogs.tflite文件，可通过加载模型输入单张测试图片，验证分类输出结果是否正常。
• 把生成的.h5文件进行转换：
• 这个脚本完成的这个功能。最终我们得到了：
• 当我们训练出来了这个模型后，那么它的识别率有多么高呢？需要通过图片来进行验证。
• 我在网上找了相关的方案，比如colab 在线网站操作比对结果，出现各种问题，不稳定。然后准备使用tf原生的官网方法，应该是没有维护了导致不行。
• 最终使用了streamlit 方案搭建了本地的验证网站。
• 那么接下来就重点介绍这个方式。
• 考虑到 的性能和开发便捷性，推荐使用 ‌Python + Streamlit‌。它无需编写 HTML/CSS/JS，仅需几十行 Python 代码即可生成交互式网页，且完美支持加载 TFLite 模型
• 安装相关环境：
• pip install streamlit pillow tensorflow
  
• 这个地方比较坑多。
• 使用如下脚本实现这个功能：
• 错误最多的就是：非字符串什么的错误。
• 运行：

• D:\AIsource\archive\PetImages>streamlit run app.py
  XXXXX Uvicorn server started on :::8501

  You can now view your Streamlit app in your browser.

  Local URL: http://localhost:8501
  Network URL: http://192.168.106.191:8501

  Help agents write better Streamlit apps?
  Install the official Streamlit skills by running streamlit skills in your terminal.

  WARNING: All log messages before absl::InitializeLog() is called are written to STDERR

• 最终的效果：

• 到这里整个训练验证的工程就完成了。