{"id":124570,"date":"2020-06-26T18:58:16","date_gmt":"2020-06-26T10:58:16","guid":{"rendered":"http:\/\/4563.org\/?p=124570"},"modified":"2020-06-26T18:58:16","modified_gmt":"2020-06-26T10:58:16","slug":"%e7%a6%8f%e5%88%a9-%e4%bc%81%e4%b8%9a%e7%ba%a7%e9%aa%8c%e8%af%81%e7%a0%81%e8%af%86%e5%88%ab%e6%96%b9%e6%a1%88-%e5%90%ab%e9%80%9a%e7%94%a8%e6%a8%a1%e5%9e%8b%e5%bc%80%e6%ba%90","status":"publish","type":"post","link":"http:\/\/4563.org\/?p=124570","title":{"rendered":"[\u798f\u5229] \u4f01\u4e1a\u7ea7\u9a8c\u8bc1\u7801\u8bc6\u522b\u65b9\u6848 | \u542b\u901a\u7528\u6a21\u578b+\u5f00\u6e90"},"content":{"rendered":"
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[\u798f\u5229] \u4f01\u4e1a\u7ea7\u9a8c\u8bc1\u7801\u8bc6\u522b\u65b9\u6848 | \u542b\u901a\u7528\u6a21\u578b+\u5f00\u6e90 <\/h1>\n

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\u8cc7\u6df1\u5927\u4f6c : kerlomz <\/span> <\/i> 130<\/span> <\/div>\n
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    \n
  1. \u524d\u8a00<\/li>\n<\/ol>\n

    \u7f51\u4e0a\u5173\u4e8e\u9a8c\u8bc1\u7801\u8bc6\u522b\u7684\u5f00\u6e90\u9879\u76ee\u4f17\u591a\uff0c\u4f46\u5927\u591a\u662f\u5b66\u672f\u578b\u6587\u7ae0\u6216\u8005\u4ec5\u4ec5\u662f\u4e00\u4e2a\u6d4b\u8bd5 demo\uff0c\u90a3\u4e48\u4f01\u4e1a\u7ea7\u7684\u9a8c\u8bc1\u7801\u8bc6\u522b\u7a76\u7adf\u662f\u600e\u6837\u7684\u5462\uff1f\u524d\u65b9\u9ad8\u80fd\u9884\u8b66\uff0c\u8fd9\u662f\u4e00\u4e2a\u751f\u4ea7\u6c34\u51c6\u7684\u9a8c\u8bc1\u7801\u8bc6\u522b\u9879\u76ee\uff0c\u7b14\u8005\u53ef\u4ee5\u5411\u4f60\u4eec\u4fdd\u8bc1\uff0c\u5b83\u4e00\u5b9a\u4f1a\u662f\u5404\u4f4d\u6240\u89c1\u8fc7\u7684\u6587\u7ae0\u4e2d\u6700\u5b9e\u7528\u7684\uff0c\u4f60\u751a\u81f3\u53ef\u4ee5\u4e0d\u9700\u8981\u61c2\u4ee3\u7801\u5199\u4ee3\u7801\u5c31\u80fd\u8f7b\u677e\u4f7f\u7528\u5b83\u8bad\u7ec3\u4e00\u4e2a 99 \u8bc6\u522b\u7387\u7684\u6a21\u578b\u3002\u8fd9\u624d\u662f\u4f01\u4e1a\u7ea7\u5e94\u8be5\u6709\u7684\u6837\u5b50\uff1a\u7b97\u6cd5\u5f00\u53d1\u8d1f\u8d23\u6846\u67b6\uff0c\u8bad\u7ec3\u53ea\u9700\u8981\u4e00\u4e2a\u5b9e\u4e60\u751f\u3002\u4e0d\u4ec5\u64cd\u4f5c\u4e0a\u7b80\u5355\uff0c\u5728\u53ef\u7528\u6027\u548c\u7a33\u5b9a\u6027\u4e0a\u4e5f\u662f\u7ecf\u5f97\u8d77\u8003\u7814\u3002\u6027\u80fd\u4e0a\uff0c\u7b14\u8005\u4f7f\u7528\u817e\u8baf\u4e91 1 \u6838 1G \u7684\u673a\u5668\u6d4b\u8bd5\uff1a\u5355\u6b21\u8bc6\u522b\u5e73\u5747\u5728 12ms \u5de6\u53f3\uff0c\u518d\u4e5f\u4e0d\u9700\u8981 GPU \u90e8\u7f72\u4e86\uff0cCPU \u4e00\u6837\u53ef\u4ee5\u65e5\u8c03\u767e\u4e07\u3002<\/p>\n

    \u4e0d\u5c11\u521d\u5b66\u8005\u548c\u7b14\u8005\u53cd\u5e94\uff0c\u5b89\u88c5\u73af\u5883\u592a\u96be\u4e86\uff0c\u6ca1\u5173\u7cfb\uff0c\u90fd\u7ed9\u4f60\u4eec\u5b89\u6392\u597d\u4e86\uff0c\u4e00\u884c pip \u5c31\u80fd\u641e\u5b9a\u73af\u5883\u7684 MuggleOCR \u3002 \u4ed3\u5e93\u5730\u5740\uff1ahttps:\/\/pypi.org\/project\/muggle-ocr<\/p>\n

    MuggleOCR \u7684\u4f53\u79ef\u6709 6MB\uff0c\u5176\u4e2d\u9644\u5e26\u4e86\u4e24\u4e2a\u901a\u7528\u6a21\u578b\uff1a\u7b80\u5355\u901a\u7528\u9a8c\u8bc1\u7801\uff0c\u666e\u901a OCR \u3002\u7b80\u800c\u8a00\u4e4b\u5c31\u662f\uff0c\u518d\u4e5f\u4e0d\u7528\u6101\u9a8c\u8bc1\u7801\u7684\u6837\u672c\u4e0d\u597d\u6807\u6ce8\u4e86\uff0c\u5b83\u5c06\u662f\u5404\u4f4d\u6807\u6ce8\u6837\u672c\u7684\u5229\u5668\uff0c\u7b80\u5355\u7684\u9a8c\u8bc1\u7801\u8bc6\u522b\u7387\u80fd\u6709 95%\u4ee5\u4e0a\uff0c\u590d\u6742\u7684\u4e5f\u6709 50%-70%\u5de6\u53f3\uff0c\u53ea\u9700\u8981\u7ed3\u5408\u5b98\u7f51\u6821\u9a8c\uff0c\u8f7b\u677e\u4e0b\u8f7d\u51e0\u4e07\u6807\u6ce8\u6837\u672c\u3002<\/p>\n

    \u9664\u6b64\u4e4b\u5916\uff0c\u5b83\u53ef\u4ee5\u652f\u6301\u8c03\u7528\u4f7f\u7528\u672c\u6587\u6846\u67b6\uff08 captcha_trainer \uff09\u8bad\u7ec3\u7684\u6a21\u578b\u3002\u8c03\u7528\u53ea\u9700\u8981\u4e09\u884c\u6838\u5fc3\u4ee3\u7801\uff1a<\/p>\n

    # \u6253\u5f00\u4e00\u5f20\u9a8c\u8bc1\u7801\u56fe\u7247 with open(r\"1.png\", \"rb\") as f:     img_bytes = f.read()      # \u6b65\u9aa4 1 import muggle_ocr  # \u6b65\u9aa4 2 sdk = muggle_ocr.SDK(model_type=muggle_ocr.ModelType.OCR)  # \u6b65\u9aa4 3 text = sdk.predict(image_bytes=img_bytes) print(text) <\/code><\/pre>\n
    \u662f\u4e0d\u662f\u5f88\u7b80\u5355\uff0c\u7528\u5b83\u5e94\u4ed8\u4e00\u822c\u7684\u9a8c\u8bc1\u7801\u8bc6\u522b\u8db3\u77e3<\/p>\n
    \u672c\u9879\u76ee\u65e8\u5728\u964d\u4f4e\u56fe\u50cf\u8bc6\u522b\u7684\u95e8\u69db\uff0c\u8ba9\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u80fd\u591f\u8fdb\u5165\u66f4\u591a\u4eba\u7684\u89c6\u7ebf\u3002\u4efb\u4f55\u4eba\u7ecf\u8fc7\u7b80\u5355\u7684\u4ecb\u7ecd\uff0c\u90fd\u53ef\u4ee5\u8f7b\u6613\u4f7f\u7528\u8fd9\u9879\u6280\u672f\u8bad\u7ec3\u4e00\u4e2a\u5546\u4e1a\u5316\u7684\u6210\u54c1\u3002<\/p>\n
    \u7b14\u8005\u9009\u7528\u7684\u65f6\u4e0b\u6700\u4e3a\u6d41\u884c\u7684 CNN Backbone+RNN+CTC \uff08 CRNN \uff09\u8fdb\u884c\u7aef\u5230\u7aef\u7684\u4e0d\u5b9a\u957f\u9a8c\u8bc1\u7801\u8bc6\u522b\uff0c\u4ee3\u7801\u4e2d\u9884\u7559\u4e86 CNNX\/MobileNet\/DenseNet121\/ResNet50 \u7b49\u3002\u5176\u4e2d\u53ef\u80fd\u4f60\u4eec\u641c\u4e0d\u5230 CNN5 \u548c CNNX\uff0c\u56e0\u4e3a\u662f\u5c0f\u7f16\u81ea\u5df1\u62fc\u51d1\u7684\u7f51\u7edc\u9009\u9879\uff0c\u4e13\u95e8\u4e3a\u9a8c\u8bc1\u7801\u4f18\u5316\u5b9a\u5236\u7684\uff0c\u5728\u914d\u7f6e\u754c\u9762\u4e2d\u53ef\u4ee5\u968f\u610f\u5207\u6362\u7f51\u7edc\u7ec4\u5408\u3002<\/p>\n
    \u524d\u9762\u4ecb\u7ecd\u8fd9\u4e48\u591a\u8fd8\u6ca1\u8fdb\u5165\u6b63\u9898\uff0c\u5404\u4f4d\u662f\u4e0d\u662f\u597d\u5947\u5b83\u5230\u5e95\u662f\u4ec0\u4e48\u6a21\u6837\u5462\uff1f<\/p>\n
    \u8fd0\u884c\u65b9\u6cd5\uff1a<\/p>\n
      \n
    1. \u53ef\u901a\u8fc7\u7f16\u8bd1\u7248\u7684\u53ef\u6267\u884c\u6587\u4ef6\u8fd0\u884c<\/li>\n
    2. \u5728\u9879\u76ee\u4e2d\u8fd0\u884c app.py \u6765\u542f\u52a8 GUI \u7684\u754c\u9762 \"[\u798f\u5229]<\/li>\n<\/ol>\n
      \u8bad\u7ec3\u9879\u76ee\u6e90\u7801\uff1a https:\/\/github.com\/kerlomz\/captcha_trainer<\/strong><\/p>\n
      \u7f16\u8bd1\u7248\u4e0b\u8f7d\u5730\u5740\uff1a https:\/\/github.com\/kerlomz\/captcha_trainer\/releases<\/strong><\/p>\n
      \u90e8\u7f72\u9879\u76ee\u6e90\u7801\uff1a https:\/\/github.com\/kerlomz\/captcha_platform<\/strong><\/p>\n
      \u7f16\u8bd1\u7248\u4e0b\u8f7d\u5730\u5740\uff1a https:\/\/github.com\/kerlomz\/captcha_platform\/releases<\/strong><\/p>\n
      \u6ce8\u610f\uff1a\u5728 Windows \u670d\u52a1\u5668\u7248\u4e2d\u4f7f\u7528\u7f16\u8bd1\u7248\u5982\u679c\u51fa\u73b0\u95ea\u9000\uff0c\u53ef\u4ee5\u7528 CMD \u6267\u884c\u53ef\u6267\u884c\u6587\u4ef6\u6765\u67e5\u770b\u62a5\u9519\uff0c\u5982\u679c\u62a5\u9519\u4e3acv2 ImportError: Dll load failed<\/code> \u8bf7\u6309\u7167\u6b65\u9aa4\uff1a\u6211\u7684\u7535\u8111\u2014\u2014\u5c5e\u6027\u2014\u2014\u7ba1\u7406\u2014\u2014\u6dfb\u52a0\u89d2\u8272\u548c\u529f\u80fd\u2014\u2014\u52fe\u9009<\/strong>\u684c\u9762\u4f53\u9a8c<\/strong>\uff0c\u70b9\u51fb\u5b89\u88c5\uff0c\u5b89\u88c5\u4e4b\u540e\u91cd\u542f\u5373\u53ef\u3002<\/p>\n
      2.\u73af\u5883\u4f9d\u8d56\uff1a<\/h2>\n
      \u73af\u5883\u4f9d\u8d56\u82b1\u4e86\u8d85\u957f\u7bc7\u5e45\uff0c\u4e3b\u8981\u662f\u5199\u7ed9\u96f6\u5f00\u53d1\u57fa\u7840\u7684\u4f7f\u7528\u8005\uff0c\u6709\u57fa\u7840\u7684\u53ef\u4ee5\u968f\u4fbf\u8df3\u8fc7\uff0c\u4e5f\u6b22\u8fce\u4f7f\u7528\u7f16\u8bd1\u7248\uff0c\u53ef\u5728\u4e0a\u4e00\u7ae0\u672b\u5c3e\u627e\u5230\u4e0b\u8f7d\u5730\u5740\u3002<\/strong><\/p>\n
      \u5173\u4e8e CUDA \u548c cuDNN \u7248\u672c\u7684\u95ee\u9898\uff0c\u5c31\u8ba9\u4e0d\u5c11\u4eba\u671b\u800c\u5374\u6b65\uff0c\u5176\u5b9e\u5f88\u7b80\u5355\uff0c\u5982\u679c\u4f7f\u7528 pypi \u4ed3\u5e93\u5b89\u88c5\u7684 TensorFlow\uff0c\u90a3\u4e48 Linux \u7cfb\u7edf\u4f7f\u7528 CUDA 9.0\uff0cWindows \u4f7f\u7528 CUDA 10.0\uff0c\u56e0\u4e3a\u4ed3\u5e93\u4e2d\u7684 whl \u5b89\u88c5\u6587\u4ef6\u90fd\u662f\u6839\u636e\u5bf9\u5e94\u7684 CUDA \u7248\u672c\u7f16\u8bd1\u7684\u3002\u4e5f\u5c31\u662f\u7248\u672c\u7ed1\u5b9a\u6b7b\u4e86\uff0c\u5982\u679c\u6709\u9700\u8981\u53ef\u4ee5\u53bb\u641c\u7d22TensorFlow Wheel<\/code>\u627e\u7b2c\u4e09\u65b9\u7f16\u8bd1\u7684\u7248\u672c\uff0c\u5982\u679c\u5984\u56fe\u81ea\u884c\u7f16\u8bd1\u6211\u8fd9\u91cc\u529d\u9000\u4e00\u4e0b\uff0c\u5751\u5f88\u591a\u3002<\/p>\n
      2.1 \u9879\u76ee\u4f7f\u7528\u73af\u5883<\/h3>\n
      \u5728\u9879\u76ee\u4e2d\u7684 requirements.txt<\/code> \u5df2\u7ecf\u6574\u7406\u597d\u6240\u6709\u4f9d\u8d56\u6a21\u5757\u3002\u4e00\u952epip install -r requirements.txt<\/code>\u5b89\u88c5\u5373\u53ef<\/p>\n
      1 \uff09\u5b89\u88c5\u76f8\u5173\u4f9d\u8d56<\/strong> \u4e0d\u7528\u7406\u4f1a\u4e0a\u9762\u7684\u6e05\u5355\uff0c\u5728\u9879\u76ee\u4e2d\u7684 requirements.txt<\/code> \u5df2\u7ecf\u6574\u7406\u597d\u6240\u6709\u4f9d\u8d56\u6a21\u5757\u3002\u53ef\u4ee5\u76f4\u63a5\u5728\u9879\u76ee\u8def\u5f84\u4e0b\u6267\u884cpip3 install -r requirements.txt<\/code>\u5b89\u88c5\u6240\u6709\u4f9d\u8d56\u3002<\/p>\n
      \u6ce8\u610f\u9ed8\u8ba4\u60c5\u51b5\u4f1a\u5b89\u88c5\u5230\u5168\u5c40\u7684 Python \u73af\u5883\u4e0b\uff0c\u7b14\u8005\u5f3a\u70c8\u5efa\u8bae\u5728\u865a\u62df\u73af\u5883\u8fdb\u884c\uff0c\u505a\u597d\u9879\u76ee\u95f4\u7684\u73af\u5883\u9694\u79bb\uff0c\u53ef\u4ee5\u501f\u52a9Virtualenv<\/strong>\u6216Anaconda<\/strong>\u7b49\u7b49\u5b9e\u73b0\u3002 \u7b14\u8005\u4e2a\u4eba\u4f7f\u7528\u7684\u662f Virtualenv\uff0c\u5982\u679c\u6709\u4fee\u6539\u4ee3\u7801\u9700\u6c42\u7684\uff0c\u53ef\u76f4\u63a5\u5728 PyCharm \u4e0a\u64cd\u4f5c\u3002<\/p>\n
      virtualenv -p \/usr\/bin\/python3 venv # venv \u662f\u865a\u62df\u73af\u5883\u7684\u540d\u79f0\uff0c\u4e5f\u662f\u8def\u5f84\u540d. cd venv\/ # \u8fdb\u5165\u73af\u5883. source bin\/activate # \u6fc0\u6d3b\u5f53\u524d\u73af\u5883. cd captcha_trainer # captcha_trainer \u662f\u9879\u76ee\u540d. pip3 install -r requirements.txt # \u5728\u521a\u521a\u521b\u5efa\u7684\u73af\u5883\u4e0b\u5b89\u88c5\u5f53\u524d\u9879\u76ee\u7684\u4f9d\u8d56 <\/code><\/pre>\n
      2.1.2 Ubuntu 16.04 \u4e0b\u7684 CUDA\/cuDNN<\/h4>\n
      \u7f51\u4e0a\u5f88\u591a\u6559\u7a0b\uff0c\u4f46\u662f\u9760\u8c31\u7684\u4e0d\u591a\uff0c\u81ea\u5df1\u5728\u4e0d\u540c\u7684\u673a\u5668\u4e0a\u90e8\u7f72\u8fc7\u51e0\u6b21\uff0c\u4ee5\u8eab\u8bf4\u6cd5\uff0c14.04 \u684c\u9762\u7248\u652f\u6301\u4e0d\u597d\uff0c\u9700\u8981\u4e3b\u677f\u652f\u6301\u5173\u95ed SecureBoot\uff0cUbuntu 16.04 \u7684\u5751\u5c11\u4e00\u70b9\uff0c\u5927\u591a\u7684\u5751\u90fd\u53d1\u751f\u5728\u5b89\u88c5\u597d\u4e4b\u540e\uff0c\u5728\u767b\u9646\u754c\u9762\u65e0\u9650\u5faa\u73af\u65e0\u6cd5\u8fdb\u5165\u684c\u9762\u3002\u7f51\u4e0a\u5f88\u591a\u6559\u7a0b\u63d0\u793a\u8981\u52a0\u9a71\u52a8\u9ed1\u540d\u5355\u4ec0\u4e48\u7684\uff0c\u7b14\u8005\u4eb2\u6d4b\u6ca1\u90a3\u4e2a\u5fc5\u8981\u3002\u5c31\u7b80\u5355\u7684\u51e0\u6b65\uff1a 1. \u4e0b\u8f7d\u597d\u5b89\u88c5\u5305<\/strong> \u5fc5\u987b\u4e0b\u8f7d runfile \u7c7b\u578b\u7684\u5b89\u88c5\u5305\uff0c\u5373\u540e\u7f00\u540d\u4e3a.run \u7684\u5b89\u88c5\u5305\uff0c\u56e0\u4e3a deb \u5b89\u88c5\u5305\u9ed8\u8ba4\u5b89\u88c5\u81ea\u5e26\u9a71\u52a8\uff0c\u8fd9\u662f\u5bfc\u81f4\u767b\u9646\u5faa\u73af\u7684\u7f6a\u9b41\u7978\u9996\u3002 NVIDIA \u9a71\u52a8\u4e0b\u8f7d\uff1a https:\/\/www.geforce.cn\/drivers CUDA \u4e0b\u8f7d\u5730\u5740\uff1a https:\/\/developer.nvidia.com\/cuda-10.0-download-archive cuDNN \u4e0b\u8f7d\u5730\u5740\uff1a https:\/\/developer.nvidia.com\/cudnn \uff08\u9700\u8981\u6ce8\u518c NVIDIA \u8d26\u53f7\u4e14\u767b\u9646\uff0c\u4e0b\u8f7d deb \u5b89\u88c5\u5305\uff09<\/p>\n
      2. \u5173\u95ed\u56fe\u5f62\u754c\u9762<\/strong> \u8fdb\u5165\u5b57\u7b26\u754c\u9762\uff0c\u5feb\u6377\u952e Ctrl+alt+F1\uff0c\u5c06 GUI \u670d\u52a1\u5173\u95ed<\/p>\n
      sudo service lightdm stop <\/code><\/pre>\n
      3. \u5b89\u88c5 Nvidia Driver<\/strong><\/p>\n
      \u547d\u4ee4\u4e2d\u7684\u7248\u672c\u81ea\u5df1\u5bf9\u5e94\u4e0b\u8f7d\u7684\u7248\u672c\u6539\uff0c\u5728\u4e0a\u9762\u7684\u4e0b\u8f7d\u5730\u5740\u6839\u636e\u81ea\u5df1\u7684\u663e\u5361\u578b\u53f7\u4e0b\u8f7d\u6700\u65b0\u7248\uff0c\u5207\u8bb0\u662f runfile \u683c\u5f0f\u7684\u5b89\u88c5\u5305\u3002\u4ee5\u4e0b 3xx.xx \u4e3a\u7248\u672c\u53f7\uff0c\u8bf7\u4e0b\u8f7d\u6700\u65b0\u9a71\u52a8\u3002<\/p>\n
      sudo chmod a+x NVIDIA-Linux-x86_64-3xx.xx.run \/\/\u83b7\u53d6\u6267\u884c\u6743\u9650 sudo .\/NVIDIA-Linux-x86_64-3xx.xx.run \u2013no-x-check \u2013no-nouveau-check \u2013no-opengl-files \/\/\u5b89\u88c5\u9a71\u52a8 <\/code><\/pre>\n
      \u5b89\u88c5\u540e\u4f7f\u7528 nvidia-smi<\/code> \u547d\u4ee4\u9a8c\u8bc1\uff0c\u82e5\u51fa\u73b0\u663e\u5361\u4fe1\u606f\uff0c\u5219\u8868\u793a\u5b89\u88c5\u6210\u529f<\/p>\n
      4. \u5b89\u88c5 CUDA<\/strong><\/p>\n
      1 \uff09\u5148\u5b89\u88c5\u4e00\u4e9b\u7cfb\u7edf\u4f9d\u8d56\u5e93<\/p>\n
      sudo apt-get install build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa-dev libgl1-mesa-glx libglu1-mesa freeglut3-dev <\/code><\/pre>\n
        \n
      1. \u6267\u884c\u5b89\u88c5\u7a0b\u5e8f\uff0c\u6309\u63d0\u793a\u7ee7\u7eed\u5c31\u597d\u4e86\uff0c\u76f4\u5230\u51fa\u73b0\u662f\u5426\u5b89\u88c5\u9a71\u52a8\u9009\u9879\uff0c\u9009\u62e9\u4e0d\u5b89\u88c5\u5373\u53ef\u3002<\/strong><\/li>\n<\/ol>\n
        sudo sh cuda_9.0.176_384.81_linux.run <\/code><\/pre>\n
        \u5b89\u88c5\u5b8c\u6210\u8fd8\u9700\u8981\u914d\u7f6e\u73af\u5883\u53d8\u91cf\uff0c\u5c06\u4ee5\u4e0b\u5185\u5bb9\u5c31\u8ffd\u52a0\u5230 ~\/.bashrc \u6587\u4ef6\u7684\u5c3e\u90e8<\/p>\n
        export PATH=\/usr\/local\/cuda-9.0\/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=\/usr\/local\/cuda-9.0\/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} <\/code><\/pre>\n
        \u6700\u540e\u5728\u7ec8\u7aef\u6267\u884c sudo ldconfig<\/code>\u547d\u4ee4\u66f4\u65b0\u73af\u5883\u53d8\u91cf\uff0c\u91cd\u542f\u673a\u5668\uff0c\u91cd\u65b0\u542f\u7528 GUI \u5373\u53ef\u3002<\/p>\n
        sudo service lightdm start <\/code><\/pre>\n
        2.1.3 Windows \u7cfb\u7edf<\/h4>\n
        \u4e00\u76f4\u6709\u4eba\u8bf4 Windows \u4e0d\u9002\u5408\u505a\u6df1\u5ea6\u5b66\u4e60\uff0c\u5176\u5b9e\u7b14\u8005\u89c9\u5f97\u8fd8\u662f\u86ee\u53cb\u597d\u7684\u3002\u5de8\u786c\u7684\u7cfb\u7edf\u5b89\u88c5\u73af\u5883\u7b80\u5355\u4e00\u767e\u500d\uff0c\u53ea\u8981\u5230\u5b98\u7f51\u4e0b\u8f7d\u5bf9\u5e94\u7684\u5b89\u88c5\u5305\uff0c\u672c\u9879\u76ee\u5efa\u8bae CUDA 10.0\uff0cWindows 2019 \u7684\u8bdd\u53ef\u4ee5\u4f7f\u7528 Win10 \u7248\u66ff\u4ee3\uff0cCUDA \u5b89\u88c5\u7684\u65f6\u5019\u540c\u6837\u4e0d\u5b89\u88c5\u9a71\u52a8\uff0c\u5305\u62ec\u4e00\u4e2a VS \u7684\u9009\u9879\u4e5f\u53bb\u6389\uff08\u4e0d\u53d6\u6d88\u5b89\u88c5\u4f1a\u5f88\u6162\u5e76\u53ef\u80fd\u5b89\u88c5\u5931\u8d25\uff09\uff0c\u7136\u540e\u4e0b\u8f7d\u5bf9\u5e94\u7684 cuDNN \u66ff\u6362\u5230 CUDA \u5b89\u88c5\u8def\u5f84\u5373\u53ef\uff0c\u4e00\u822c\u4e3a\uff1aC:Program FilesNVIDIA GPU Computing ToolkitCUDAv10.0<\/code>\u3002<\/p>\n
        3 \u4f7f\u7528<\/h2>\n
        \u5f00\u59cb\u4e4b\u524d\uff0c\u5148\u89e3\u51b3\u4e00\u4e2a\u4e16\u7eaa\u7591\u60d1\uff0c\u6709\u4e0d\u5c11\u670b\u53cb\u5e38\u5e38\u79c1\u4fe1\u6211\u201c\u8bad\u7ec3\u4e00\u4e2a x \u4f4d\u6570\u82f1\u6587\u6570\u5b57\u9a8c\u8bc1\u7801\u9700\u8981\u591a\u5c11\u6837\u672c\uff1f\u201d\u8bf8\u5982\u6b64\u7c7b\u7684\u95ee\u9898\uff0c\u7b14\u8005\u5728\u6b64\u7edf\u4e00\u56de\u590d\uff0c\u6837\u672c\u9700\u8981\u591a\u5c11\u6570\u91cf\u9700\u8981\u6839\u636e\u6837\u672c\u7684\u7279\u5f81\u590d\u6742\u7a0b\u5ea6\u6765\u51b3\u5b9a\u3002<\/strong><\/p>\n
        \u7279\u5f81\u590d\u6742\u5ea6\u8bc4\u4ef7\u6307\u6807\uff1a<\/strong><\/p>\n
          \n
        1. \u53d8\u5f62<\/li>\n
        2. \u65cb\u8f6c<\/li>\n
        3. \u6a21\u7cca<\/li>\n
        4. \u80cc\u666f\u5e72\u6270<\/li>\n
        5. \u524d\u666f\u5e72\u6270<\/li>\n
        6. \u5b57\u4f53\u79cd\u7c7b<\/li>\n
        7. \u6807\u7b7e\u6570\u76ee \/\u9a8c\u8bc1\u7801\u4f4d\u6570<\/li>\n
        8. \u5206\u7c7b\u6570\u76ee \/\u5b57\u7b26\u96c6\u5927\u5c0f<\/li>\n<\/ol>\n
          \u4e00\u822c\u53ea\u5305\u542b\u4ee5\u4e0a 1-2 \u79cd\u7684\u4e3a\u7b80\u5355\uff0c2-3 \u79cd\u4e3a\u590d\u6742\uff0c3 \u79cd\u4ee5\u4e0a\u5c5e\u4e8e\u7279\u522b\u590d\u6742\u3002\u6837\u672c\u91cf\u4f9d\u6b21\u9012\u589e\uff0c\u4ece\u51e0\u767e\uff0c\u51e0\u5343\uff0c\u51e0\u4e07\uff0c\u5230\u51e0\u5341\u4e07\u4e0d\u7b49\uff0c\u5176\u4e2d\uff0c\u5206\u7c7b\u6570\u76ee\uff08\u5b57\u7b26\u96c6\u5e26\uff09\u591a\u5be1\u5bf9\u6570\u91cf\u7ea7\u5f71\u54cd\u8f83\u5927\uff0c\u4f8b\u5982\u4e2d\u6587\u51e0\u5343\u5b57\u7b26\u96c6\u7684\u9a8c\u8bc1\u7801\u4e00\u822c 10w \u8d77\u6b65\uff0c\u7b14\u8005\u6587\u4e2d\u672b\u5c3e\u7684\u9a8c\u8bc1\u7801\u7528\u4e86 100w \u6837\u672c\u3002<\/p>\n
          PS\uff1a\u4eb2\u4eec\u4e0d\u8981\u518d\u8003\u9a8c\u6846\u67b6\u7684\u5065\u58ee\u6027\u4e86\uff0c\u6837\u672c\u91cf\u8fde\u4e00\u4e2a Batch Size \u90fd\u8fbe\u4e0d\u5230\u7684\uff0c\u5343\u4e07\u4e0d\u8981\u5c1d\u8bd5\uff0c\u6839\u672c\u8dd1\u4e0d\u8d77\u6765\u3002<\/strong><\/p>\n
          \u76ee\u524d\u4e3a\u6b62\uff0c\u5165\u5751\u51c6\u5907\u5de5\u4f5c\u8fd8\u5dee\u4e00\u6b65\uff0c\u5de7\u5987\u96be\u4e3a\u65e0\u7c73\u4e4b\u708a\uff0c\u9996\u5148\uff0c\u65e2\u7136\u662f\u8bad\u7ec3\uff0c\u5f97\u8981\u5148\u6709\u6570\u636e\uff0c\u7b14\u8005\u8fd9\u91cc\u63d0\u4f9b\u4e00\u4efd\u8def\u4eba\u7686\u77e5\u7684 mnist \u624b\u5199\u8bc6\u522b\u7684\u6570\u636e\u96c6\u3002<\/p>\n
          \u53ef\u4ee5\u5728\u817e\u8baf\u4e91\u4e0b\u8f7d\uff1a https:\/\/share.weiyun.com\/5pzGF4V\uff0c\u73b0\u5728\u4e07\u4e8b\u4ff1\u5907\uff0c\u53ea\u6b20\u4e1c\u98ce\u3002<\/p>\n
          3.1 \u5b9a\u4e49\u4e00\u4e2a\u6a21\u578b<\/h3>\n
          \u672c\u9879\u76ee\u6240\u6709\u914d\u7f6e\u90fd\u662f\u53c2\u6570\u5316\u7684\uff0c\u4e0d\u9700\u8981\u6539\u52a8\u4efb\u4f55\u4ee3\u7801\uff0c\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7\u53ef\u89c6\u5316\u754c\u9762\u64cd\u4f5c\uff0c\u8bad\u7ec3\u51e0\u4e4e\u56fe\u7247\u9a8c\u8bc1\u7801\u3002\u8bad\u7ec3\u6846\u67b6\u754c\u9762\u53ef\u4ee5\u5927\u81f4\u5212\u5206\u4e3a\u51e0\u4e2a\u90e8\u5206\uff1a<\/p>\n
            \n
          1. Neural Network – \u795e\u7ecf\u7f51\u7edc\u533a \"[\u798f\u5229]<\/li>\n
          2. Project Configuration – \u9879\u76ee\u914d\u7f6e\u533a \"[\u798f\u5229]<\/li>\n
          3. Sample Source – \u6837\u672c\u6e90\u914d\u7f6e\u533a \"[\u798f\u5229]<\/li>\n
          4. Training Configuration – \u8bad\u7ec3\u914d\u7f6e\u533a \"[\u798f\u5229]<\/li>\n
          5. Buttons – \u529f\u80fd\u63a7\u5236\u533a \"[\u798f\u5229]<\/li>\n<\/ol>\n
            \u4f9d\u6b64\u7c7b\u63a8\u7684\u8bad\u7ec3\u914d\u7f6e\u7684\u6b65\u9aa4\u5982\u4e0b\uff1a<\/strong><\/p>\n
              \n
            1. \u795e\u7ecf\u7f51\u7edc\u533a<\/strong> \u7684\u914d\u7f6e\u9879\u770b\u8d77\u6765\u5f88\u591a\uff0c\u5bf9\u4e8e\u65b0\u624b\u6765\u8bf4\uff0c\u53ea\u9700\u5148\u9009\u62e9\u597d\u4f7f\u7528\u7684\u7f51\u7edc\uff0c\u5728\u6837\u672c\u914d\u7f6e\u533a\u9009\u62e9\u6837\u672c\u8def\u5f84\u4e4b\u540e\uff0c\u4f1a\u81ea\u52a8\u914d\u7f6e\u56fe\u7247\u6709\u5173\u7684\u53c2\u6570\uff0c\u4fdd\u6301\u9ed8\u8ba4\u63a8\u8350\u53c2\u6570\u5373\u53ef\u3002\u7b14\u8005\u4e00\u822c\u4f7f\u7528 CNNX+GRU+CTC \u7f51\u7edc\u8fdb\u884c\u4e0d\u5b9a\u957f\u9a8c\u8bc1\u7801\u7684\u8bad\u7ec3\u3002<\/li>\n
            2. \u9879\u76ee\u914d\u7f6e\u533a<\/strong> \u7684\u914d\u7f6e\u9879\u5728\u7f51\u7edc\u9009\u597d\u4e4b\u540e\u914d\u7f6e\u9879\u76ee\u540d\uff0c\u6309\u56de\u8f66\u6216\u8005\u70b9\u51fb\u7a7a\u767d\u5904\u786e\u8ba4\u3002<\/li>\n
            3. \u6837\u672c\u6e90\u914d\u7f6e\u533a<\/strong> \u7684\u914d\u7f6e\u9879\u7528\u6765\u914d\u7f6e\u6837\u672c\u6e90\u7684\u8def\u5f84\uff0c\u8bad\u7ec3\u6837\u672c\u662f\u6839\u636e\u6b64\u8def\u5f84\u8fdb\u884c\u6253\u5305\u6210 TFRecords \u683c\u5f0f\uff0c\u9a8c\u8bc1\u6837\u672c\u53ef\u4ee5\u4e0d\u6307\u5b9a\uff0c\u4f7f\u7528[Validation Set Num]\u53c2\u6570\u968f\u673a\u4ece\u8bad\u7ec3\u96c6\u603b\u62bd\u6837\u6210\u9a8c\u8bc1\u96c6\uff0c\u8fd9\u91cc\u9ed8\u8ba4\u968f\u673a\u62bd\u53d6\u6570\u76ee\u4e3a 300 \u4e2a\uff0c\u53ef\u4ee5\u5728\u754c\u9762\u4e0a\u81ea\u884c\u4fee\u6539\u3002<\/li>\n
            4. \u8bad\u7ec3\u914d\u7f6e\u533a<\/strong> \u7684\u914d\u7f6e\u9879\u8d1f\u8d23\u5b9a\u4e49\u8bad\u7ec3\u5b8c\u6210\u7684\u6761\u4ef6\u5982\uff1a\u7ed3\u675f\u51c6\u786e\u7387\uff0c\u7ed3\u675f COST\uff0c\u7ed3\u675f Epochs\uff0c\u6279\u6b21\u5927\u5c0f\u3002\u5982\u679c\u6700\u540e\u65e0\u6cd5\u6ee1\u8db3\u53ef\u4ee5\u624b\u52a8\u505c\u6b62\uff0c\u7136\u540e\u70b9\u51fb[Compile]\u7f16\u8bd1\u5bfc\u51fa\u6700\u65b0\u7684\u8bad\u7ec3\u6a21\u578b\u3002<\/li>\n
            5. \u529f\u80fd\u63a7\u5236\u533a<\/strong> \u7684\u914d\u7f6e\u9879\uff0c\u8bbe\u7f6e\u5b8c\u4e0a\u9762\u6b65\u9aa4\uff0c\u5148\u70b9\u51fb[Make Dataset] \u6253\u5305\u6837\u672c\uff0c\u518d\u70b9\u51fb[Start Training]\u5f00\u59cb\u8bad\u7ec3\u3002<\/li>\n<\/ol>\n
              \u4ee5\u4e0b\u90e8\u5206\u6709\u57fa\u7840\u7684\u8bfb\u8005\u4eec\u53ef\u4ee5\u4e86\u89e3\u4e00\u4e0b\uff1a<\/h4>\n
              \u5982\u82e5\u4f7f\u7528 CrossEntropy \u4f5c\u4e3a\u89e3\u7801\u5668\u9700\u8981\u6ce8\u610f\u6807\u7b7e\u6570 LabelNum \u548c\u56fe\u7247\u5c3a\u5bf8\u9700\u8981\u6ee1\u8db3\u7684\u5173\u7cfb\uff0c\u56e0\u4e3a\u7f51\u7edc\u4e3a\u591a\u6807\u7b7e\u800c\u8bbe\u8ba1\uff08\u4e00\u822c\u7684\u591a\u6807\u7b7e\u91c7\u7528\u76f4\u63a5\u8fde\u63a5\u591a\u4e2a\u5206\u7c7b\u5668\uff0c\u8fd9\u4e5f\u662f\u6709\u4e00\u90e8\u5206\u7f51\u4e0a\u7684\u5f00\u6e90\u4ee3\u7801\u4f60\u4eec\u4fee\u6539\u4e86\u56fe\u7247\u5c31\u65e0\u6cd5\u8fd0\u884c\u7684\u539f\u56e0\u4e4b\u4e00\uff09\uff0c\u5377\u79ef\u5c42\u7684\u8f93\u51fa outputs \u7ecf\u8fc7\u4e86\u4ee5\u4e0b\u53d8\u6362\uff1a<\/p>\n
              Reshape([label_num, int(outputs_shape[1] \/ label_num)]) <\/code><\/pre>\n
              \u4e3a\u4e86\u4fdd\u8bc1 int(outputs_shape[1] \/ label_num) \u8fd0\u7b97\u80fd\u591f\u5f97\u5230\u6b63\u6574\u6570\u7ef4\u5ea6\uff0c\u8fd9\u610f\u5473\u7740\u4ed6\u4eec\u4e4b\u95f4\u5b58\u5728\u67d0\u79cd\u6570\u5b66\u5173\u7cfb\uff0c\u5bf9 CNN5+Cross Entropy \u7f51\u7edc\u7ed3\u6784\u800c\u8a00\uff0cConv2D \u5c42\u7684\u6b65\u957f\u7686\u4e3a 1\uff0c\u90a3\u4e48\u9700\u8981\u4fdd\u8bc1\u4ee5\u4e0b\u7b49\u5f0f\u6210\u7acb\uff1a<\/p>\n
              $$ mod(frac{\u8f93\u5165\u5bbd\u5ea6times \u8f93\u5165\u9ad8\u5ea6times \u8f93\u51fa\u5c42\u53c2\u6570}{\u6c60\u5316\u6b65\u957f^{\u6c60\u5316\u5c42\u6570}times \u6807\u7b7e\u6570})= 0 $$<\/p>\n
              \u6240\u4ee5\u6709\u65f6\u5019\u9700\u8981\u5bf9\u8f93\u5165\u7684\u56fe\u7247 Resize\uff0c\u4e00\u822c 4 \u4f4d\u9a8c\u8bc1\u7801\u4e0d\u5bb9\u6613\u51fa\u73b0\u8fd9\u79cd\u95ee\u9898\uff0c\u4f4d\u6570\u4e3a 3\uff0c5\uff0c6\uff0c7 \u5bb9\u6613\u51fa\u73b0\u4e0d\u6ee1\u8db3\u7b49\u5f0f\u7684\u95ee\u9898\uff0c\u8fd9\u4e2a\u7b49\u4ef7\u5173\u7cfb\u5982\u679c\u4e0d\u597d\u8ba1\u7b97\u7684\u8bdd\uff0c\u5efa\u8bae\u4f7f\u7528 CTC Loss \u3002<\/p>\n
              \u4f8b\u5982\u4f7f\u7528 CNN5+CrossEntropy \u7ec4\u5408\uff0c\u5219\u8f93\u5165\u5bbd\u5ea6\u4e0e\u8f93\u5165\u9ad8\u5ea6\u9700\u8981\u6ee1\u8db3\uff1a $$ mod(frac{\u8f93\u5165\u5bbd\u5ea6times \u8f93\u5165\u9ad8\u5ea6times64}{16times \u6807\u7b7e\u6570})= 0 $$ \u540c\u7406\u5982\u679c CNN5+RNN+CTC\uff0c\u5377\u79ef\u5c42\u4e4b\u540e\u7684\u8f93\u51fa\u7ecf\u8fc7\u4ee5\u4e0b\u53d8\u6362\uff1a<\/p>\n
              Reshape([-1, outputs_shape[2] * outputs_shape[3]]) <\/code><\/pre>\n
              \u539f\u8f93\u51fa(batch_size, outputs_shape[1], outputs_shape[2], outputs_shape[3])\uff0cRNN \u5c42\u7684\u8f93\u5165\u8f93\u51fa\u8981\u6c42\u4e3a(batch, timesteps, num_classes)\uff0c\u4e3a\u4e86\u63a5\u5165 RNN \u5c42\uff0c\u7ecf\u8fc7\u4ee5\u4e0a\u7684\u64cd\u4f5c\uff0c\u53c8\u5f15\u51fa\u4e00\u4e2a Time Step \uff08\u65f6\u95f4\u6b65\u957f\uff09\u7684\u6982\u5ff5\u3002<\/p>\n
              \u53ef\u4ee5\u628a timesteps \u53ef\u4ee5\u7406\u89e3\u4e3a\u56fe\u7247\u5207\u7247\uff0c\u6bcf\u4e2a\u5207\u7247\u9700\u8981\u548c\u6807\u7b7e\u5bf9\u5e94\u3002\u8fdb\u5165 RNN \u5c42\u4e4b\u540e timesteps \u7684\u503c\u4e5f\u662f\u7ecf\u8fc7\u5377\u79ef\u6c60\u5316\u53d8\u6362\u4e4b\u540e outputs_shape[1]\uff0c\u800c CTC Loss \u7684\u8f93\u5165\u8981\u6c42\u4e3a [batch_size, frames, num_labels]\uff0c\u82e5 timesteps \u5c0f\u4e8e\u6807\u7b7e\u6570\u76ee\uff0c\u53ef\u4ee5\u7406\u89e3\u4e3a\u56fe\u7247\u5207\u7247\u6570\u5c0f\u4e8e\u6807\u7b7e\u6570\uff0c\u4e00\u4e2a\u5207\u7247\u5bf9\u5e94\u4e86\u591a\u4e2a\u6807\u7b7e\uff0c\u90a3\u4e48\u80af\u5b9a\u662f\u65e0\u6cd5\u8ba1\u7b97\u635f\u5931\u7684\uff0c\u4e5f\u5c31\u662f\u65e0\u6cd5\u4ece\u635f\u5931\u51fd\u6570\u4e2d\u627e\u5230\u6781\u5c0f\u503c\uff0c\u68af\u5ea6\u65e0\u6cd5\u4e0b\u964d\u3002<\/p>\n
              timesteps \u6700\u5408\u7406\u7684\u503c\u4e00\u822c\u662f\u6807\u7b7e\u6570\u7684 2 \u500d\uff0c\u4e3a\u4e86\u8fbe\u5230\u76ee\u7684\uff0c\u4e5f\u53ef\u4ee5\u901a\u8fc7\u5bf9\u8f93\u5165 Resize \u6765\u95f4\u63a5\u8c03\u6574\u5377\u79ef\u6c60\u5316\u4e4b\u540e\u7684 outputs_shape[1]\uff0c\u4e00\u822c\u60c5\u51b5\u4e0b timesteps \u76f4\u63a5\u5173\u8054\u4e8e\u56fe\u7247\u5bbd\u5ea6\uff0c\u5927\u591a\u60c5\u51b5\u53ea\u9700\u6309\u6bd4\u4f8b Resize \u5bbd\u5ea6\u5373\u53ef\u3002<\/p>\n
              ExtractRegex \u53c2\u6570\uff1a<\/h4>\n
              \u6ce8\u610f\uff1a\u5982\u679c\u8bad\u7ec3\u96c6\u7684\u547d\u540d\u65b9\u5f0f\u548c\u6211\u63d0\u4f9b\u7684\u65b0\u624b\u8bad\u7ec3\u96c6\u4e0d\u4e00\u6837\uff0c\u53ef\u4ee5\u6839\u636e\u5b9e\u9645\u60c5\u51b5\u4fee\u6539 ExtractRegex \u7684\u6b63\u5219\u8868\u8fbe\u5f0f\u3002\u5f3a\u70c8\u5efa\u8bae\u4e0d\u77e5\u9053\u5982\u4f55\u5199\u6b63\u5219\u8868\u8fbe\u5f0f\u7684\u670b\u53cb\u6309\u7167\u7b14\u8005\u7684\u5b9a\u4e49\u89c4\u8303\u547d\u540d\u3002\u76ee\u524d\u8fd9\u4e2a\u529f\u80fd\u53ea\u652f\u6301\u5728 yaml \u914d\u7f6e\u6587\u4ef6\u4e2d\u4fee\u6539\uff0cGUI \u754c\u9762\u5c1a\u4e0d\u652f\u6301\u4fee\u6539\u8be5\u53c2\u6570\u3002<\/strong> DatasetPath \u548c SourcePath \u53c2\u6570\u5141\u8bb8\u914d\u7f6e\u591a\u4e2a\u8def\u5f84\uff0c\u5982\u679c\u9700\u8981\u628a\u591a\u79cd\u6837\u5f0f\u7684\u56fe\u7247\u6df7\u5408\u4e00\u8d77\u8bad\u7ec3\uff0c\u6216\u8005\u6253\u7b97\u8bad\u7ec3\u4e00\u5957\u901a\u7528\u8bc6\u522b\u6a21\u578b\u7684\u7528\u6237\uff0c\u8fd9\u975e\u5e38\u65b9\u4fbf\u3002 \u5206\u7c7b\u6570\u76ee \/\u5b57\u7b26\u96c6\uff08 Category \uff09\u5df2\u7ecf\u5305\u62ec\u4e86\u5927\u591a\u6570\u9a8c\u8bc1\u7801\u548c OCR \u7684\u60c5\u51b5\uff0c\u5927\u591a\u6570\u60c5\u51b5\u4e0b\u4e0d\u9700\u8981\u81ea\u5b9a\u4e49\uff0c\u4e00\u822c\u7684\u56fe\u5f62\u9a8c\u8bc1\u7801\u662f\u5927\u5c0f\u5199\u4e0d\u654f\u611f\u7684\uff0c\u4e00\u822c\u4e0d\u8981\u8f7b\u6613\u9009\u62e9\u533a\u5206\u5927\u5c0f\u5199\u7684\u5206\u7c7b\uff0c\u63a8\u8350\u9ed8\u8ba4\u7684 ALPHANUMERIC_LOWER\uff0c\u4f1a\u81ea\u52a8\u5c06\u5927\u5199\u7684\u8f6c\u4e3a\u5c0f\u5199\uff0c\u5b57\u7b26\u96c6\u5b9a\u4e49\u5f88\u7075\u6d3b\uff0c\u9664\u4e86\u914d\u7f6e\u5907\u6ce8\u4e0a\u63d0\u4f9b\u7684\u51e0\u79cd\u8303\u5f0f\uff0c\u8fd8\u652f\u6301\u8bad\u7ec3\u4e2d\u6587\uff0c\u81ea\u5b9a\u4e49\u5b57\u7b26\u96c6\u7528 list \u8868\u793a\uff0c\u53c2\u8003\u5982\u4e0b\uff1a<\/p>\n
              Category: ['\u4f60', '\u597d', '\u4e16', '\u754c', '\u5317', '\u4eac', '\u5927', '\u5b66'] <\/code><\/pre>\n
              \u5982\u679c\u662f\u5355\u6807\u7b7e\u5206\u7c7b\uff0c\u53ef\u4ee5\u914d\u5408 LabelNum=1\uff0c\u4f8b\u5982\uff1a<\/p>\n
              Category: [\"\u98de\u673a\", \"\u978b\u5b50\", \"\u6c34\u676f\", \"\u9762\u5305\", \"\u6a2a\u5e45\", \"\u8ba2\u4e66\u673a\", \"\u58c1\u753b\", \"\u732b\u7802\", ......] <\/code><\/pre>\n
              \u5176\u6587\u4ef6\u540d\u793a\u4f8b\uff1a\u98de\u673a_0123456789012.png<\/p>\n
              \u5982\u679c\u662f\u591a\u6807\u7b7e\u5206\u7c7b\uff0c\u53ef\u4ee5\u914d\u5408 LabelSplit=&\uff0c\u4f8b\u5982\uff1a<\/p>\n
              Category: [\"\u98de\u673a\", \"\u978b\u5b50\", \"\u6c34\u676f\", \"\u9762\u5305\", \"\u6a2a\u5e45\", \"\u8ba2\u4e66\u673a\", \"\u58c1\u753b\", \"\u732b\u7802\", ......] <\/code><\/pre>\n
              \u5176\u6587\u4ef6\u540d\u793a\u4f8b\uff1a\u98de\u673a&\u978b\u5b50&\u6c34\u676f_1231290424123.png<\/p>\n
              \u6ce8\u610f\uff1a\u4e2d\u6587\u5b57\u7b26\u96c6\u4e00\u822c\u6bd4\u6570\u5b57\u82f1\u6587\u5927\u5f88\u591a\uff0c\u6536\u655b\u65f6\u95f4\u8f83\u957f\uff0c\u540c\u6837\u4e5f\u9700\u8981\u66f4\u591a\u7684\u6837\u672c\u91cf\uff0c\u5343\u4e07\u4e0d\u8981\u60f3\u7740\u51e0\u5343\u5f20\u56fe\u7247\u8bad\u7ec3\u51e0\u5343\u5b57\u7b26\u96c6\u7684\u9a8c\u8bc1\u7801\uff0c\u6bd5\u7adf\u673a\u5668\u4e5f\u4e0d\u662f\u795e<\/strong> \"[\u798f\u5229] \u5f62\u5982\u4e0a\u56fe\u7684\u56fe\u7247\u80fd\u8f7b\u677e\u8bad\u7ec3\u5230 98%\u4ee5\u4e0a\u7684\u8bc6\u522b\u7387\u3002<\/strong><\/p>\n
              ImageWidth \u3001ImageHeight \u53c2\u6570\u53ea\u8981\u548c\u5f53\u524d\u56fe\u7247\u5c3a\u5bf8\u5339\u914d\u5373\u53ef\uff0c\u5176\u5b9e\u8fd9\u91cc\u7684\u914d\u7f6e\u4e3b\u8981\u662f\u4e3a\u4e86\u65b9\u4fbf\u540e\u9762\u7684\u90e8\u7f72\u667a\u80fd\u7b56\u7565\u3002<\/p>\n
              Pretreatment \u53c2\u6570\uff1a<\/h4>\n
              \u8fd9\u4e2a Pretreatment \u53c2\u6570\u4e3b\u8981\u662f\u56fe\u7247\u9884\u5904\u7406\u7528\u7684\uff0c\u4f8b\u5982\u4e0b\u9762\u8fd9\u4e2a\u6709\u8da3\u7684 GIF \u52a8\u56fe\uff0c \"[\u798f\u5229]<\/p>\n
              \u901a\u8fc7\u89c2\u5bdf\uff0c\u6eda\u52a8\u5300\u901f\uff0c\u4f4d\u6570\u56fa\u5b9a\uff0c\u90a3\u4e48\u4e00\u5b9a\u5b58\u5728\u67d0\u4e24\u4e2a\u56fa\u5b9a\u7684\u5e27\uff0c\u5b8c\u5168\u5305\u542b\u524d\u4e09\u548c\u540e\u4e09\u4f4d\u7684\u5185\u5bb9\u3002\u8fd9\u79cd\u5c31\u53ef\u4ee5\u91c7\u7528\u62fc\u63a5\u7684\u5f62\u5f0f\uff0c\u5c06\u5305\u542b\u5b8c\u6574 6 \u4f4d\u7684\u5185\u5bb9\u7684\u56fe\u7247\u62fc\u63a5\u4e3a\u4e00\u5f20\uff0c\u4f7f\u7528 Pretreatment\/ConcatFrames \u53c2\u6570\uff0c\u9009\u53d6\u524d\u540e\u4e24\u4e2a\u5e27\u8fdb\u884c\u6c34\u5e73\u62fc\u63a5\uff0c\u9002\u7528\u4e8e\u5904\u7406\u6eda\u52a8\u578b GIF\uff0c\u800c\u95ea\u70c1\u578b GIF \u53ef\u4ee5\u4f7f\u7528 BlendFrames \u53c2\u6570\u8fdb\u884c\u56fe\u5c42\u878d\u5408\u3002<\/p>\n
              3.2 \u5f00\u59cb\u8bad\u7ec3<\/h3>\n
                \n
              1. \u7ecf\u8fc7 \u91c7\u96c6\u6807\u6ce8\u6837\u672c\u5f62\u5982 xxx_\u968f\u673a\u6570.png \"[\u798f\u5229]<\/li>\n
              2. \u6837\u672c\u6253\u5305 \u53ef\u4ee5\u901a\u8fc7 GUI \u754c\u9762\u7684 [Make Dataset]\uff0c\u6216\u8005\u4f7f\u7528 make_dataset.py \u624b\u52a8\u914d\u7f6e\u6253\u5305\u6837\u672c\uff0c\u6253\u5305\u7684\u76ee\u7684\u4e3b\u8981\u662f\u4e3a\u4e86\u51cf\u5c11\u786c\u76d8\u7684 IO \u8bfb\u5199\u3002\u6709\u65f6\u5019\u51c6\u5907\u7684\u6837\u672c\u6bd4\u8f83\u5c11\uff0c\u8bad\u7ec3\u7ed3\u679c\u4e0d\u6ee1\u610f\uff0c\u91cd\u65b0\u91c7\u96c6\u4e86\u4e00\u90e8\u5206\u6837\u672c\u600e\u4e48\u52a0\u5165\u8bad\u7ec3\u5462\uff1f\u5bf9\u4e8e\u589e\u91cf\u7684\u6837\u672c\u6253\u5305\u53ef\u4ee5\u4f7f\u7528[Attach Dataset]\uff0c\u65e0\u9700\u91cd\u65b0\u6253\u5305\u3002 PS\uff1a\u4f7f\u7528\u6e90\u7801\u7684\u540c\u5b66\u9700\u8981\u5177\u5907\u4e00\u5b9a\u7684\u7f16\u7a0b\u57fa\u7840\uff0c\u5c3d\u91cf\u4e0d\u53bb\u4fee\u6539\u6838\u5fc3\u51fd\u6570\u548c\u9759\u6001\u5b9a\u4e49\u4ee5\u514d\u51fa\u73b0\u9519\u8bef\uff0c\u4fee\u6539\u4ee3\u7801\u7684\u65f6\u5019\u8bf7\u786e\u4fdd\u914d\u5957\u7684\u90e8\u7f72\u9879\u76ee\u5bf9\u5e94\u7684\u5730\u65b9\u4e5f\u4e00\u5e76\u4fee\u6539\u4e86\u3002<\/strong><\/li>\n<\/ol>\n
                \u6309\u7167\u4e0a\u9762\u7684\u4ecb\u7ecd\uff0c\u8bb2\u89e3\u867d\u591a\uff0c\u4f46\u5b9e\u9645\u4e0a\u53ea\u9700\u8981\u914d\u7f6e\u6781\u5c11\u6570\u7684\u53c2\u6570\uff0c\u5c31\u53ef\u4ee5\u5f00\u59cb\u8bad\u7ec3\u4e86\uff0c\u9ad8\u7ea7\u73a9\u5bb6\u4e00\u822c\u914d\u7f6e\u4e0d\u8d85\u8fc7 10 \u79d2\u3002<\/p>\n
                \u5f00\u59cb\u8bad\u7ec3\uff1a<\/p>\n
                  \n
                1. \u521b\u5efa\u597d\u9879\u76ee\u540e\uff0c\u5728 PyCharm \u4e2d\u8fd0\u884c trains.py \uff0c\u4e5f\u53ef\u4ee5\u5728\u6fc0\u6d3b Virtualenv \u4e0b\u4f7f\u7528\u7ec8\u7aef\u4ea6\u6216\u5728\u5b89\u88c5\u4f9d\u8d56\u7684\u5168\u5c40\u73af\u5883\u4e0b\u6267\u884c<\/li>\n
                2. \u672c\u6587\u5efa\u8bae\u5168\u7a0b\u4f7f\u7528 GUI \u754c\u9762\u8fdb\u884c\u64cd\u4f5c\uff0c\u6e90\u7801\u4f7f\u7528 GUI \u4ec5\u9700\u542f\u52a8 app.py \u5373\u53ef\u3002<\/li>\n<\/ol>\n
                  python3 trains.py <\/code><\/pre>\n
                  \u4e0b\u56fe\u4e3a\u8bad\u7ec3\u901a\u7528\u6a21\u578b\u7684\u8fc7\u7a0b\u622a\u56fe\uff0c\u8010\u5fc3\u7b49\u5f85\u8bad\u7ec3\u7ed3\u675f\u5373\u53ef\u3002<\/p>\n
                  \"[\u798f\u5229]<\/p>\n
                  \u8bad\u7ec3\u7ed3\u675f\u4f1a\u5728\u9879\u76ee\u8def\u5f84\u7684 out \u4e0b\u770b\u5230\u4ee5\u4e0b\u7ed3\u6784\u7684\u6587\u4ef6\uff0cpb \u4e3a\u6a21\u578b\uff0cyaml \u4e3a\u6a21\u578b\u914d\u7f6e\u6587\u4ef6\uff0c\u4e0b\u9762\u8be5\u5230\u90e8\u7f72\u73af\u8282\u4e86\u3002<\/p>\n
                  \"[\u798f\u5229]<\/p>\n
                  3.3 \u90e8\u7f72<\/h3>\n
                  \u4e00\u822c\u9a8c\u8bc1\u7801\u8bc6\u522b\u5728\u4f01\u4e1a\u4e2d\u5f88\u5c11\u4ee5 SDK \u7684\u5f62\u5f0f\u88ab\u4f7f\u7528\uff0c\u5927\u591a\u662f\u4ee5\u5fae\u670d\u52a1\u51fa\u73b0\u7684\uff0c\u72ec\u7acb\u4e8e\u5176\u4ed6\u7684\u4e1a\u52a1\uff0c\u72ec\u7acb\u8fd0\u8425\u548c\u7ef4\u62a4\uff0c\u90a3\u4e48\u4f01\u4e1a\u7ea7\u7684\u90e8\u7f72\u670d\u52a1\u53c8\u662f\u600e\u6837\u7684\u5462\uff1f<\/p>\n
                  \u9879\u76ee\u5730\u5740\uff1a https:\/\/github.com\/kerlomz\/captcha_platform\uff0c\u53ef\u4ee5\u4e3a\u5404\u4f4d\u63d0\u4f9b\u4e00\u4e2a\u53c2\u8003\uff0cTornado \u670d\u52a1\u4ec5\u4f5c\u4e3a\u4e00\u4e2a\u4f8b\u5b50\uff0c\u4f01\u4e1a\u4e00\u822c\u91c7\u7528 gRPC \u96c6\u7fa4\u8fdc\u7a0b\u8c03\u7528\u3002<\/p>\n
                  \u5982\u9700\u8981\u96c6\u6210\u5230\u9879\u76ee\u91cc\u901a\u8fc7 sdk \u8c03\u7528\u7684\uff0c\u53ef\u4ee5\u53c2\u8003 MuggleOCR \u7684\u505a\u6cd5\uff0c\u5b83\u7684\u6838\u5fc3\u7ee7\u627f\u4e86 captcha_platform\/sdk\/pb\/sdk.py\uff1a<\/p>\n
                  https:\/\/pypi.org\/project\/muggle-ocr\/<\/p>\n
                  \u6a21\u578b\u7684\u8c03\u7528\u65b9\u6cd5\uff1a<\/p>\n
                    \n
                  1. \n
                    \u53ef\u4ee5\u901a\u8fc7 muggle-ocr \u8c03\u7528\u8bad\u7ec3\u6846\u67b6\u751f\u4ea7\u7684\u6a21\u578b\uff08 pypi \u6587\u6863\u6709\u4ecb\u7ecd\uff09\uff0c<\/p>\n<\/li>\n
                  2. \n
                    \u4e5f\u53ef\u4ee5\u63d0\u53d6 sdk.py \u6839\u636e\u9700\u8981\u81ea\u884c\u4fee\u6539\u3002<\/p>\n<\/li>\n
                  3. \n
                    \u8fd8\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u7f16\u8bd1\u7248\uff08\u514d\u53bb\u5b89\u88c5 Python \u548c TensorFlow \u73af\u5883\uff0c\u76ee\u524d\u540c\u65f6\u652f\u6301 Ubuntu\/MacOS\/Windows \u4e09\u5927\u5e73\u53f0\uff09\uff0c\u7b2c\u4e00\u7ae0\u672b\u5c3e\u6709\u94fe\u63a5\u3002<\/p>\n<\/li>\n<\/ol>\n
                    \u90e8\u7f72\u670d\u52a1\u7684\u7279\u6027\uff1a<\/h4>\n
                      \n
                    1. \u652f\u6301\u591a\u6a21\u578b\u90e8\u7f72<\/li>\n
                    2. \u652f\u6301\u6a21\u578b\u70ed\u62d4\u63d2<\/li>\n
                    3. \u7248\u672c\u63a7\u5236\u7075\u6d3b<\/li>\n
                    4. \u652f\u6301\u6279\u91cf\u8bc6\u522b<\/li>\n
                    5. \u667a\u80fd\u6a21\u578b\u5206\u53d1<\/li>\n<\/ol>\n
                      \u7b14\u8005\u5c01\u88c5\u4e86 Graph \u4f1a\u8bdd\u7ba1\u7406\uff0c\u8bbe\u8ba1\u4f1a\u8bdd\u6c60\uff0c\u5141\u8bb8\u540c\u65f6\u7ba1\u7406\u591a\u6a21\u578b\uff0c\u5b9e\u73b0\u591a\u6a21\u578b\u52a8\u6001\u90e8\u7f72\u65b9\u6848\u3002<\/strong><\/p>\n
                      1 \uff09<\/strong> \u8bad\u7ec3\u597d\u7684 pb \u6a21\u578b\u53ea\u8981\u653e\u5728 graph \u8def\u5f84\u4e0b\uff0cyaml \u6587\u4ef6\u653e\u5728 model \u8def\u5f84\u4e0b<\/strong>\uff08\u64cd\u4f5c\u987a\u5e8f\u5f88\u91cd\u8981<\/strong>\uff0cyaml \u4e3b\u8981\u7528\u4e8e\u670d\u52a1\u53d1\u73b0\uff0c\u901a\u8fc7 ModelName \u53c2\u6570\u5b9a\u4f4d\u5bf9\u5e94\u7684 pb \u6a21\u578b\uff0c\u5982\u679c\u987a\u5e8f\u98a0\u5012\uff0c\u670d\u52a1\u662f\u65e0\u6cd5\u52a0\u8f7d\u5c1a\u672a\u653e\u7f6e\u8fdb\u6765\u7684\u6a21\u578b\u7684\uff09\u3002<\/p>\n
                      \"[\u798f\u5229]<\/p>\n
                      \u4f7f\u7528 SDK \u8c03\u7528\u65f6\uff0cyaml \u548c pb \u6a21\u578b\u5fc5\u987b\u5728\u540c\u4e00\u8def\u5f84\u4e0b\u3002<\/p>\n
                      \"[\u798f\u5229]<\/p>\n
                      2 \uff09<\/strong> \u5378\u8f7d\u4e00\u4e2a\u6b63\u5728\u670d\u52a1\u7684\u6a21\u578b\uff0c\u53ea\u9700\u8981\u5220\u9664 yaml \u548c\u5bf9\u5e94\u7684 pb \u6a21\u578b\u5373\u53ef\u3002\uff08\u6a21\u578b\u5df2\u52a0\u8f7d\u4e8e\u5185\u5b58\u6240\u4ee5\u65e0\u6240\u8c13\u987a\u5e8f\uff09<\/p>\n
                      3 \uff09<\/strong> \u66f4\u65b0\u4e00\u4e2a\u5df2\u7ecf\u90e8\u7f72\u52a0\u8f7d\u7684\u6a21\u578b\uff0c\u53ea\u9700\u6309\u5148\u540e\u987a\u5e8f\u653e\u7f6e pb \u6a21\u578b\u548c\u9ad8\u7248\u672c\u7684 yaml \u6587\u4ef6\uff0c\u670d\u52a1\u4f1a\u81ea\u52a8\u53d1\u73b0\u5e76\u52a0\u8f7d\uff0c\u65e7\u6a21\u578b\u4f18\u5148\u7ea7\u88ab\u53d6\u4ee3\uff0c\u4e0d\u4f1a\u518d\u88ab\u8c03\u7528\uff0c\u4fbf\u53ef\u6309\u4e0a\u8ff0\u65b9\u6cd5\u5378\u8f7d\u5f03\u7528\u7684\u6a21\u578b\u91ca\u653e\u5185\u5b58\u3002 \u4e00\u5207\u7ba1\u7406\u64cd\u4f5c\u5747\u65e0\u9700\u91cd\u542f\u670d\u52a1\uff0c\u53ef\u4ee5\u65e0\u611f\u77e5\u5207\u6362\uff0c\u65b9\u4fbf\u7ef4\u62a4\u63d0\u9ad8\u4e86\u53ef\u7528\u6027\u3002<\/p>\n
                      \u5176\u6b21\uff0c\u5982\u679c\u8bfb\u8005\u6709\u5f88\u591a\u9a8c\u8bc1\u7801\u9700\u6c42\u9700\u8981\u9010\u4e2a\u5b9a\u5236\uff0c\u8bad\u7ec3\u65f6\u5c06\u6240\u6709\u5c3a\u5bf8\u4e00\u6837\u7684\u56fe\u7247\u8bad\u7ec3\u6210\u4e00\u4e2a\u6a21\u578b\uff0c\u670d\u52a1\u6839\u636e\u56fe\u7247\u5c3a\u5bf8\u4f1a\u81ea\u52a8\u5b9a\u4f4d\u5bf9\u5e94\u7684\u6a21\u578b\u3002\u5f53\u7136\u4e5f\u53ef\u4ee5\u901a\u8fc7\u4f20\u9012 model_name \u53c2\u6570\u7cbe\u786e\u63a7\u5236\u591a\u6a21\u578b\u8c03\u7528\uff0c\u8fd9\u6837\u7684\u8bbe\u8ba1\u5141\u8bb8\u5b9a\u5236\u5316\u548c\u901a\u7528\u6027\u5171\u5b58\uff0c\u5f53\u8bfb\u8005\u4eec\u79ef\u7d2f\u5230\u4e00\u5b9a\u91cf\u7684\u6837\u672c\u96c6\u65f6\u53ef\u4ee5\u50cf MuggleOCR \u4e00\u6837\u8bad\u7ec3\u4e00\u5957\u901a\u7528\u8bc6\u522b\u6a21\u578b\u4f5c\u4e3a\u5907\u7528\u6a21\u578b\u3002\u6a21\u578b\u4e4b\u95f4\u4ea6\u5f7c\u6b64\u72ec\u7acb\uff0c\u6bcf\u589e\u52a0\u90e8\u7f72\u4e00\u4e2a\u6a21\u578b\uff0c\u4ec5\u4ec5\u589e\u52a0\u4e86\u5c11\u91cf\u7684\u5185\u5b58\u6216\u663e\u5b58\u5360\u7528\uff0c\u4e0d\u5c11\u5c0f\u4f01\u4e1a\u4e5f\u5403\u8fc7\u5b9a\u5236\u6a21\u578b\u7684\u4e8f\uff0c\u627e\u4e2a\u4eba\u5b9a\u5236\u6a21\u578b\uff0c\u6bcf\u4e2a\u6a21\u578b\u90fd\u8981\u72ec\u7acb\u542f\u7528\u4e00\u4e2a\u670d\u52a1\uff0c\u65e0\u5f62\u589e\u52a0\u4e86\u6210\u672c\uff0c\u6bcf\u4e2a\u8fdb\u7a0b\u82e5\u91cd\u590d\u52a0\u8f7d\u4e00\u904d\u6574\u4e2a\u6846\u67b6\u65e0\u7591\u662f\u6781\u5927\u7684\u8d44\u6e90\u6d6a\u8d39\u3002<\/strong><\/p>\n
                      \u524d\u9762\u6709\u63d0\u5230\u6279\u91cf\u8bc6\u522b<\/strong>\uff0c\u6709\u8fd9\u79cd\u9700\u6c42\u7684\u7528\u6237\u76f8\u5bf9\u8f83\u5c11\uff0c\u8fd9\u91cc\u53ea\u505a\u7b80\u5355\u4ecb\u7ecd\uff0c\u7ed9\u4e00\u4e2a 12306 \u7684\u4f8b\u5b50\uff0c\u5982\u56fe\u6240\u793a\uff1a<\/p>\n
                      \"[\u798f\u5229]<\/p>\n
                      \u4e00\u5f20\u56fe\u4e2d\u5305\u542b\u4e86\u591a\u4e2a\u9700\u8981\u8bc6\u522b\u7684\u90e8\u5206\uff0c\u800c\u6846\u67b6\u4e2d\u7684 CorpParams \u652f\u6301\u5c06\u5927\u56fe\u5207\u5272\u4e3a\u5c0f\u56fe\u4e00\u5e76\u4f20\u5165\uff0c\u539f\u672c\u4e00\u4e2a\u8bf7\u6c42\u5bf9\u4e8e\u670d\u52a1\u53ea\u80fd\u4f20\u4e00\u5f20\u56fe\uff0c\u73b0\u5728\u53ef\u4ee5\u901a\u8fc7\u88c1\u526a\u529f\u80fd\u4e00\u6b21\u4f20\u5165 9 \u5f20\u56fe\u3002\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
                      FieldParam:   CorpParams: [     {       \"start_pos\": [118, 0],       \"interval_size\": [0, 0],       \"corp_num\": [1, 1],       \"corp_size\": [60, 30]     },     {       \"start_pos\": [5, 40],       \"interval_size\": [5, 5],       \"corp_num\": [4, 2],       \"corp_size\": [66, 66]     }   ]   OutputCoord: True <\/code><\/pre>\n
                      FieldParam\/CorpParams \u53c2\u6570\u53ef\u4ee5\u88c1\u526a\u5408\u5e76\u6279\u6b21\uff0c\u8be5\u7528\u6cd5\u53ef\u907f\u514d\u591a\u6b21\u8c03\u7528\u3002<\/p>\n
                      \u4f46\u662f\u8bc6\u522b\u9879\u76ee\u63d0\u4f9b\u591a\u79cd\u540e\u7aef\u5b9e\u73b0\u7248\u672c\uff1aTornado\/Flask\/gRPC\/Sanic\uff0c\u5176\u4e2d Flask \u548c Tornado \u642d\u8f7d\u4e86\u52a0\u5bc6\u63a5\u53e3\/captcha\/auth\/v2<\/code>\uff0c\u7c7b\u4f3c\u4e8e\u5fae\u4fe1\u516c\u4f17\u53f7\u5f00\u53d1\u63a5\u53e3\u7684 SecretKey \u548c AccessKey \u63a5\u53e3\uff0c\u6709\u5174\u8da3\u7684\u53ef\u4ee5\u5728 demo.py \u4e2d\u9605\u8bfb\u8c03\u7528\u6e90\u7801\u4e86\u89e3\u3002<\/p>\n
                      \u90e8\u7f72\u670d\u52a1\u53ef\u4ee5\u4f7f\u7528 package.py \u7f16\u8bd1\u4e3a\u53ef\u6267\u884c\u6587\u4ef6\uff0c\u672c\u6587\u4e2d\u63d0\u4f9b\u7684\u7f16\u8bd1\u7248\u4e5f\u662f\u57fa\u4e8e Pyinstaller \u6253\u5305\u7f16\u8bd1\u7684\uff0c\u7f16\u8bd1\u7248\u4e0d\u9700\u8981\u8003\u8651\u66f4\u6362\u673a\u5668\u9700\u8981\u91cd\u65b0\u5b89\u88c5\u73af\u5883\uff0c\u82e5\u4f7f\u7528\u6e90\u7801\u90e8\u7f72\u7684\u8bdd\uff0c\u73af\u5883\u914d\u7f6e\u540c\u8bad\u7ec3\u9879\u76ee\u4e00\u6837\uff0c\u4f7f\u7528\u9879\u76ee\u4e2d\u63d0\u4f9b\u7684 requirements.txt \u4e00\u952e\u5b89\u88c5\u5168\u90e8\u4f9d\u8d56\uff0c\u90e8\u7f72\u670d\u52a1\u9ed8\u8ba4\u63a8\u8350\u7684\u662f CPU \u7248\u7684 TensorFlow \u3002<\/p>\n
                      \u90e8\u7f72\u670d\u52a1\u63a8\u8350 Tornado \u540e\u7aef\uff0c\u76ee\u524d\u6700\u7a33\u5b9a\u7684\u7248\u672c\u3002<\/strong><\/p>\n
                      Linux\uff1a<\/strong><\/p>\n
                        \n
                      1. Tornado\uff1a<\/li>\n<\/ol>\n
                        # \u7aef\u53e3 19952 python3 tornado_server.py <\/code><\/pre>\n
                          \n
                        1. Flask<\/li>\n<\/ol>\n
                          # \u65b9\u6848 1\uff0c\u88f8\u542f\u52a8\uff0c \u7aef\u53e3 19951 python flask_server.py  # \u65b9\u6848 2\uff0c\u4f7f\u7528 gunicorn\uff0c\u7aef\u53e3 5000 pip install gunicorn  gunicorn -c deploy.conf.py flask_server:app <\/code><\/pre>\n
                            \n
                          1. Sanic\uff1a<\/li>\n<\/ol>\n
                            # \u7aef\u53e3 19953 python3 sanic_server.py <\/code><\/pre>\n
                              \n
                            1. gRPC:<\/li>\n<\/ol>\n
                              # \u7aef\u53e3 50054 python3 grpc_server.py <\/code><\/pre>\n
                                \n
                              1. \u7f16\u8bd1\u7248(\u57fa\u4e8e Tornado)<\/li>\n<\/ol>\n
                                # \u524d\u53f0\u8fd0\u884c .\/captcha_platform_tornado #\u540e\u53f0\u8fd0\u884c nohup .\/captcha_platform_tornado & <\/code><\/pre>\n
                                Windows\uff1a<\/strong> Windows \u5e73\u53f0\u4e0b\u90fd\u662f\u901a\u8fc7python3 xxx_server.py<\/code>\u542f\u52a8\u5bf9\u5e94\u7684\u670d\u52a1\uff0c\u6ce8\u610f\uff0cTornado \u3001Flask \u3001Sanic \u7684\u6027\u80fd\u5728 Windows \u5e73\u53f0\u90fd\u5927\u6253\u6298\u6263\uff0cgRPC \u662f Google \u5f00\u6e90\u7684 RPC \u670d\u52a1\uff0c\u6709\u8f83\u4e3a\u4f18\u8d8a\u7684\u6027\u80fd\u3002 \u7f16\u8bd1\u7248\u76f4\u63a5\u8fd0\u884c\u7f16\u8bd1\u540e\u7684 exe \u53ef\u6267\u884c\u6587\u4ef6\u5373\u53ef\u3002<\/p>\n
                                3.4 \u8c03\u7528 \/\u6d4b\u8bd5<\/h3>\n
                                1. Tornado \u670d\u52a1\uff1a<\/strong><\/p>\n
                                \u8bf7\u6c42\u4e3a JSON+POST \u683c\u5f0f\uff0cURIhttp:\/\/localhost:19952\/captcha\/v1<\/code> \u5f62\u5982\uff1a{\"image\": \"iVBORw0KGgoAAAANSUhEUgAAAFoAAAAjCAIAAA...base64 \u7f16\u7801\u540e\u7684\u56fe\u50cf\u4e8c\u8fdb\u5236\u6d41\"}<\/code><\/p>\n
                                \u8fd4\u56de\u7ed3\u679c\uff1a<\/strong><\/p>\n
                                \u8be5\u8fd4\u56de\u4e3a JSON \u683c\u5f0f\uff0c\u5f62\u5982\uff1a{'uid': \"9b5a6a34-9693-11ea-b6f9-525400a21e62\", \"message\": \"xxxx\", \"code\": 0, \"success\": true}<\/code><\/p>\n
                                4. gRPC \u670d\u52a1\uff1a<\/strong> \u9700\u8981\u5b89\u88c5\u4f9d\u8d56\uff0cgrpcio \u3001grpcio_tools \u548c\u5bf9\u5e94\u7684 grpc.proto \u6587\u4ef6\uff0c\u53ef\u4ee5\u76f4\u63a5\u4ece\u9879\u76ee\u4e2d\u7684\u793a\u4f8b\u4ee3\u7801 demo.py \u4e2d\u63d0\u53d6\u3002<\/p>\n
                                python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. .\/grpc.proto <\/code><\/pre>\n
                                grpcio \u3001grpcio_tools \u662f\u6839\u636e grpc.proto \u4f7f\u7528\u4e0a\u8ff0\u547d\u4ee4\u751f\u6210\u7684\u3002<\/p>\n
                                class GoogleRPC(object):      def __init__(self, host: str):         self._url = '{}:50054'.format(host)         self.true_count = 0         self.total_count = 0      def request(self, image, model_type=None, model_site=None):          import grpc         import grpc_pb2         import grpc_pb2_grpc         channel = grpc.insecure_channel(self._url)         stub = grpc_pb2_grpc.PredictStub(channel)         response = stub.predict(grpc_pb2.PredictRequest(             image=image, split_char=',', model_type=model_type, model_site=model_site         ))         return {\"message\": response.result, \"code\": response.code, \"success\": response.success}  if __name__ == '__main__':     result = GoogleRPC().request(\"base64 \u7f16\u7801\u540e\u7684\u56fe\u7247\u4e8c\u8fdb\u5236\u6d41\")     print(result) <\/code><\/pre>\n
                                3.5 \u5947\u6280\u6deb\u5de7<\/h3>\n
                                \u4e3e\u4e00\u4e2a\u6bd4\u8f83\u4e0d\u5e38\u89c1\u7684\u4f8b\u5b50\uff0c\u4ee5\u4e0b\u4f8b\u5b50\u4e0d\u4ee3\u8868\u4efb\u4f55\u7f51\u7ad9\u3002<\/strong><\/p>\n
                                \"[\u798f\u5229]<\/p>\n
                                \u6b63\u5e38\u60c5\u51b5\u4e0b\u4f1a\u60f3\u5230\u4ee5\u4e0b 1 \u548c 2.1 \u7684\u65b9\u6cd5\uff1a<\/strong><\/p>\n
                                  \n
                                1. \u989c\u8272\u63d0\u53d6\u7684\u601d\u8def<\/strong>\uff0c\u53ef\u4ee5\u91c7\u7528 HSV\/K-means \u805a\u7c7b\u8fdb\u884c\u989c\u8272\u7684\u5206\u79bb\u63d0\u53d6\uff1a\u6548\u679c\u5982\u4e0b\uff1a \"[\u798f\u5229] \u5f0a\u7aef\u663e\u800c\u6613\u89c1\uff0c\u4f1a\u6709\u8f83\u5927\u7684\u7279\u5f81\u4e22\u5931\uff0c\u8bc6\u522b\u7387\u6709\u8f83\u5927\u7684\u63d0\u5347\u74f6\u9888\uff0c\u7ecf\u8fc7\u6d4b\u8bd5\uff0c\u4e2d\u82f1\u6587+\u6c49\u5b57\u7684\u8bc6\u522b\u7387\u5728 90%\u5de6\u53f3\u3002<\/li>\n
                                2. \u4e0d\u5206\u79bb\u989c\u8272\u7684\u601d\u8def\uff0c\u8be5\u65b9\u6848\u6709\u4e24\u79cd\u5904\u7406\u65b9\u6cd5\uff1a<\/strong> \uff08 1 \uff09\u540c\u65f6\u9884\u6d4b\u989c\u8272\u548c\u5b57\u7b26\u5185\u5bb9<\/strong>\uff0c\u8fd9\u79cd\u65b9\u6cd5\u770b\u8d77\u6765\u6bd4\u8f83\u6b63\u7edf\uff0c\u4f46\u662f\u6210\u672c\u8f83\u9ad8\uff0c\u9700\u8981\u6807\u6ce8\u6bcf\u5f20\u56fe\u7684\u989c\u8272<\/strong>\u548c\u5b57\u7b26\u5185\u5bb9<\/strong>\uff0c\u8fd9\u4e2a\u8981\u6c42\u6709\u591a\u9ad8\u5462\uff0c\u4e00\u822c\u7684\u6253\u7801\u5e73\u53f0\u662f\u65e0\u6cd5<\/strong>\u63d0\u4f9b\u8fd9\u6837\u7684\u7ed3\u679c\u7684\uff0c\u6253\u7801\u5e73\u53f0\u53ea\u8fd4\u56de\u5bf9\u5e94\u989c\u8272\u7684\u5185\u5bb9\uff0c\u53ea\u80fd\u4eba\u5de5\u6807\u6ce8\uff0c\u90a3\u4e48\u9700\u8981\u591a\u5c11\u6837\u672c\u5462\uff1f\u6309\u7167\u7b14\u8005\u8bad\u7ec3\u7684\u8bc6\u522b\u7387 98 \u7684\u6a21\u578b\u7528\u4e86 100w \u5de6\u53f3\u7684\u6837\u672c\u3002\u4e00\u5f20\u8fd9\u6837\u7684\u6837\u672c\u6807\u6ce8\u5047\u8bbe\u9700\u8981 0.1 \u5143\uff0c\u90a3\u4e48 100w \u6837\u672c\u9700\u8981 10w \u6807\u6ce8\u8d39\u7528\uff0c\u5047\u8bbe 0.01 \u5143\uff0c\u4e5f\u8981 1w \u7684\u6807\u6ce8\u8d39\u7528\u3002\u4f46\u662f\u9a8c\u8bc1\u7801\u9ad8\u8d28\u91cf\u7684\u4eba\u5de5\u6807\u6ce8\u51e0\u4e4e\u662f\u4e0d\u5b58\u5728\u7684\uff0c\u56e0\u4e3a\u5f88\u591a\u6837\u672c\uff0c\u4eba\u773c\u7684\u8bc6\u522b\u7387\u662f\u4e0d\u5982\u673a\u5668\u7684\uff0c\u603b\u4f53\u6807\u6ce8\u7684\u51c6\u786e\u7387\u5927\u6982\u4e5f\u53ea\u80fd\u5728 85 \u5de6\u53f3\u3002\u770b\u8d77\u6765\u5e76\u4e0d\u53ef\u53d6\uff0c\u6709\u4e00\u79cd\u8282\u7ea6\u6210\u672c\u7684\u529e\u6cd5\uff0c\u53ef\u4ee5\u901a\u8fc7\u7b97\u6cd5\u751f\u6210\u6837\u672c\uff0c\u4f46\u662f\u5462\uff0c\u751f\u6210\u7684\u8bc6\u522b\u7387\u82f1\u6587\u6570\u5b57\u8fd8\u53ef\u4ee5\uff0c\u4e2d\u6587\u7684\u8bc6\u522b\u7387\u5c31\u4f4e\u7684\u53ef\u601c\u4e86\u3002 \uff08 2 \uff09\u6bcf\u4e2a\u989c\u8272\u5206\u522b\u8bad\u7ec3\u4e00\u4e2a\u6a21\u578b\uff0c<\/strong> \u8fd9\u79cd\u65b9\u6cd5\u770b\u8d77\u6765\u6709\u70b9\u8822\uff0c\u4f46\u662f\u786e\u5b9e\u6bd4\u8f83\u5408\u9002\u6709\u6548\u7684\u529e\u6cd5\u4e86\uff0c\u53ef\u4ee5\u8f7b\u677e\u501f\u52a9\u6253\u7801\u5e73\u53f0\u7684\u8fd4\u56de\u7ed3\u679c\u6807\u6ce8\u6837\u672c\u3002\u9700\u8981\u7684\u989c\u8272\u53ef\u4ee5\u901a\u8fc7\u5b98\u7f51\u63d0\u4f9b\u7684\u5b57\u6bb5\u53d6\u5230\uff0c\u8fd4\u56de\u7ed3\u679c\u901a\u8fc7\u6253\u7801\u5e73\u53f0\u8bc6\u522b\u5f97\u5230\uff0c\u8fd9\u6837\u4e00\u7ec4\u5408\uff0c\u6837\u672c\u5c31\u6709\u4e86\u3002\u8fd9\u79cd\u65b9\u6cd5\u7684\u6210\u672c\u76f8\u5bf9\u8f83\u4f4e\uff0c\u6837\u672c\u6570\u4e0d\u53d8\u7684\u524d\u63d0\u4e0b\uff0c\u6253\u7801\u4ef7\u683c\u4f4e\u4e8e\u4eba\u5de5\u6807\u6ce8\u7684\u6210\u672c\u3002\u4f46\u662f\u7b14\u8005\u8bad\u7ec3\u7684\u662f\u4e00\u79cd\u989c\u8272\u7684\u6837\u672c\u7528\u4e86 100w \u3002\u6bcf\u4e2a\u989c\u8272\u5206\u522b\u8bad\u7ec3\u8fd9\u6837\u6210\u672c\u8fd8\u662f\u4e0b\u4e0d\u6765\u3002\u56db\u79cd\u989c\u8272\u5c31\u662f 500w \u6837\u672c\u3002\u5b98\u7f51\u7684\u6bcf\u6b21\u83b7\u53d6\u56fe\u7247\u7684\u65f6\u5019\u989c\u8272\u968f\u673a\u51fa\u73b0\u7684\u6982\u7387\u4e5f\u4e0d\u4e00\u5b9a\u662f 1\/4 \u3002 \"[\u798f\u5229]<\/li>\n<\/ol>\n
                                  \uff08 3 \uff09\u628a\u6240\u6709\u989c\u8272\u90fd\u901a\u8fc7\u989c\u8272\u53d8\u6362\u4e3a\u4e00\u79cd\u989c\u8272\uff0c\u6574\u4f53\u601d\u8def\u540c\uff08 2 \uff09<\/strong>\u3002\u5982\u4e0b\u56fe\uff0c\u7b14\u8005\u5c06\u9ed1\u8272\u8f6c\u6362\u4e3a\u7ea2\u8272\uff0c\u4f46\u662f\u6837\u672c\u6210\u672c\u53ea\u6709\u91c7\u96c6\u4e00\u79cd\u989c\u8272\u7684\u6210\u672c\u3002\u770b\u8d77\u6765\u662f\u76ee\u524d\u4f4d\u7f6e\u6700\u4f73\u7684\u65b9\u6848\u4e86\uff0c\u4e8b\u5b9e\u4e5f\u662f\u5982\u6b64\u7684\u3002\u4f46\u662f\u5462\uff0c100w \u7684\u6837\u672c\u5bf9\u4e8e\u666e\u901a\u4eba\u6765\u8bf4\u4e5f\u662f\u4e00\u7b14\u4e0d\u5c0f\u7684\u82b1\u9500\uff0c\u5373\u4fbf\u6709\u4e86\u6837\u672c\u80fd\u505a\u51fa\u6765\u4e5f\u9700\u8981\u82b1\u8d39\u4e0d\u5c11\u7684\u65f6\u95f4\u548c\u7cbe\u529b\u3002<\/p>\n
                                  \u4e0d\u8fc7\u91c7\u96c6\u6837\u672c\u4e0d\u662f\u5355\u7eaf\u7684\u63a5\u6253\u7801\u5e73\u53f0\u5c31\u5b8c\u4e8b\u4e86\uff0c\u9700\u8981\u7ecf\u8fc7\u5b98\u7f51\u5224\u65ad\uff0c\u53ea\u6709\u901a\u8fc7\u9a8c\u8bc1\uff0c\u6b63\u786e\u7684\u6837\u672c\u624d\u4fdd\u5b58\u4e0b\u6765\u3002\u8fd9\u6837\u6709\u6548\u7684\u6837\u672c\u5bf9\u63d0\u9ad8\u8bc6\u522b\u7387\u624d\u6709\u5e2e\u52a9\u3002 \"[\u798f\u5229]<\/p>\n
                                  \u7ecf\u8fc7\u7b14\u8005\u5b9e\u9a8c\uff0c2.3 \u7684\u65b9\u6cd5\u6027\u80fd\u6548\u679c\u6700\u4f73<\/strong><\/p>\n
                                  \u7b14\u8005\u5b9e\u65f6\u5bf9\u63a5\u5b98\u7f51\u5bf9\u5b9e\u9a8c\u6a21\u578b\u8fdb\u884c\u68c0\u9a8c\uff0c\u7ed3\u679c\u5982\u4e0a\u56fe\uff0c\u6d4b\u8bd5\u4e86 200+\u6b21\uff0c\u8bc6\u522b\u7387\u8fbe\u5230 98%\u4ee5\u4e0a\uff0c\u8bc6\u522b\u901f\u5ea6\u7684\u8bdd\uff0c\u76f8\u8f83\u4e8e 1.1 \u7684\u65b9\u6cd5\u7701\u53bb\u4e86\u989c\u8272\u63d0\u53d6\uff0c\u5927\u5927\u7f29\u77ed\u4e86\u65f6\u95f4\uff0cCPU \u5927\u6982 5-8 \u6beb\u79d2\u5de6\u53f3\uff0c\u6a21\u578b\u5927\u6982 3mb \u3002<\/p>\n
                                  \"[\u798f\u5229]<\/p>\n
                                  \u6240\u4ee5\u9009\u62e9\u5408\u9002\u7684\u65b9\u6848\u89e3\u51b3\u95ee\u9898\u624d\u662f\u6700\u7ec8\u7684\u76ee\u7684\uff0c\u5e0c\u671b\u8fd9\u4e2a\u9879\u76ee\u548c\u8fd9\u7bc7\u4ecb\u7ecd\u80fd\u5e26\u5927\u5bb6\u5165\u95e8\u4f01\u4e1a\u7ea7\u7684\u9a8c\u8bc1\u7801\u8bc6\u522b\u3002<\/p>\n<\/p><\/div>\n
                                   \u5927\u4f6c\u6709\u8a71\u8aaa<\/b> (1<\/span>)        <\/div>\n
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                                     \u8cc7\u6df1\u5927\u4f6c : loadingimg <\/span>  <\/div>\n
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                                    [\u798f\u5229] \u4f01\u4e1a\u7ea7\u9a8c\u8bc1\u7801\u8bc6\u522b\u65b9\u6848 |…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[],"tags":[],"_links":{"self":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts\/124570"}],"collection":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=124570"}],"version-history":[{"count":0,"href":"http:\/\/4563.org\/index.php?rest_route=\/wp\/v2\/posts\/124570\/revisions"}],"wp:attachment":[{"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=124570"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=124570"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/4563.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=124570"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}