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captcha_check
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CNN卷积神经网络训练识别验证码模型的一次尝试！
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# captcha_check # author: DYBOY ### 项目简介： 利用 `captcha` 库生成的图形验证码，并对其进行内容识别。首先通过对验证码图片进行一个“`灰度`”处理，使之变为灰度图，灰度图有利于去除杂色，便于提高模型精度，提升训练速度。训练过程中，使用了`三层CNN`，最终的准确率最高可达到`98.75%` ### 运行环境： - python3.6 - tensorflow 1.10 (运行时，缺少什么就安装对应包 例如安装tensorflow：pip3 install tensorflow -i https://pypi.tuna.tsinghua.edu.cn/simple ) - windows 10 家庭版 ### USAGE: - 训练模型：python3 model_train.py - 测试模型：python3 model_test.py ### 效果： [](https://upload-images.jianshu.io/upload_images/6661013-0740ed1f4da0e286.png "预测") ### 项目介绍文章地址： - https://blog.dyboy.cn/program/100.html ### 训练过程记录： ``` shell [root@VM_96_17_centos captcha]# /usr/local/bin/python3.6 model_train.py 2018-11-26 09:29:57.038117: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2018-11-26 09:29:57.408852: W tensorflow/core/framework/allocator.cc:122] Allocation of 41943040 exceeds 10% of system memory. 2018-11-26 09:29:57.451278: W tensorflow/core/framework/allocator.cc:122] Allocation of 41943040 exceeds 10% of system memory. 2018-11-26 09:29:57.498523: W tensorflow/core/framework/allocator.cc:122] Allocation of 41943040 exceeds 10% of system memory. 2018-11-26 09:29:57.525206: W tensorflow/core/framework/allocator.cc:122] Allocation of 41943040 exceeds 10% of system memory. 2018-11-26 09:29:57.566340: W tensorflow/core/framework/allocator.cc:122] Allocation of 41943040 exceeds 10% of system memory. Mon Nov 26 09:30:03 2018 step: 0 accuracy: 0.1 Mon Nov 26 09:33:47 2018 step: 100 accuracy: 0.1175 Mon Nov 26 09:37:29 2018 step: 200 accuracy: 0.1 Mon Nov 26 09:41:11 2018 step: 300 accuracy: 0.095 Mon Nov 26 09:44:53 2018 step: 400 accuracy: 0.1 Mon Nov 26 09:48:32 2018 step: 500 accuracy: 0.105 Mon Nov 26 09:52:11 2018 step: 600 accuracy: 0.0575 Mon Nov 26 09:55:50 2018 step: 700 accuracy: 0.09 Mon Nov 26 09:59:30 2018 step: 800 accuracy: 0.105 Mon Nov 26 10:03:10 2018 step: 900 accuracy: 0.085 Mon Nov 26 10:06:50 2018 step: 1000 accuracy: 0.0875 Mon Nov 26 10:10:31 2018 step: 1100 accuracy: 0.09 Mon Nov 26 10:14:11 2018 step: 1200 accuracy: 0.08 Mon Nov 26 10:17:51 2018 step: 1300 accuracy: 0.0825 Mon Nov 26 10:21:31 2018 step: 1400 accuracy: 0.135 Mon Nov 26 10:25:11 2018 step: 1500 accuracy: 0.18 Mon Nov 26 10:28:51 2018 step: 1600 accuracy: 0.31 Mon Nov 26 10:32:31 2018 step: 1700 accuracy: 0.4225 Mon Nov 26 10:36:09 2018 step: 1800 accuracy: 0.4625 Mon Nov 26 10:39:49 2018 step: 1900 accuracy: 0.4925 Mon Nov 26 10:43:28 2018 step: 2000 accuracy: 0.5475 Mon Nov 26 10:47:07 2018 step: 2100 accuracy: 0.58 Mon Nov 26 10:50:46 2018 step: 2200 accuracy: 0.6725 Mon Nov 26 10:54:25 2018 step: 2300 accuracy: 0.69 Mon Nov 26 10:58:06 2018 step: 2400 accuracy: 0.735 Mon Nov 26 11:01:46 2018 step: 2500 accuracy: 0.735 Mon Nov 26 11:05:27 2018 step: 2600 accuracy: 0.745 Mon Nov 26 11:09:07 2018 step: 2700 accuracy: 0.825 Mon Nov 26 11:12:46 2018 step: 2800 accuracy: 0.7675 Mon Nov 26 11:16:26 2018 step: 2900 accuracy: 0.8425 Mon Nov 26 11:20:05 2018 step: 3000 accuracy: 0.825 Mon Nov 26 11:23:43 2018 step: 3100 accuracy: 0.855 Mon Nov 26 11:27:23 2018 step: 3200 accuracy: 0.8425 Mon Nov 26 11:31:02 2018 step: 3300 accuracy: 0.8375 Mon Nov 26 11:34:41 2018 step: 3400 accuracy: 0.8575 Mon Nov 26 11:38:20 2018 step: 3500 accuracy: 0.8475 Mon Nov 26 11:41:59 2018 step: 3600 accuracy: 0.8775 Mon Nov 26 11:45:38 2018 step: 3700 accuracy: 0.8825 Mon Nov 26 11:49:17 2018 step: 3800 accuracy: 0.9025 Mon Nov 26 11:52:57 2018 step: 3900 accuracy: 0.8625 Mon Nov 26 11:56:37 2018 step: 4000 accuracy: 0.92 Mon Nov 26 12:00:17 2018 step: 4100 accuracy: 0.8925 Mon Nov 26 12:03:56 2018 step: 4200 accuracy: 0.9075 Mon Nov 26 12:07:35 2018 step: 4300 accuracy: 0.91 Mon Nov 26 12:11:15 2018 step: 4400 accuracy: 0.92 Mon Nov 26 12:14:54 2018 step: 4500 accuracy: 0.915 Mon Nov 26 12:18:36 2018 step: 4600 accuracy: 0.9275 Mon Nov 26 12:22:16 2018 step: 4700 accuracy: 0.9375 Mon Nov 26 12:25:56 2018 step: 4800 accuracy: 0.905 Mon Nov 26 12:29:36 2018 step: 4900 accuracy: 0.915 Mon Nov 26 12:33:17 2018 step: 5000 accuracy: 0.945 Mon Nov 26 12:36:56 2018 step: 5100 accuracy: 0.9475 Mon Nov 26 12:40:35 2018 step: 5200 accuracy: 0.9375 Mon Nov 26 12:44:15 2018 step: 5300 accuracy: 0.9675 Mon Nov 26 12:47:57 2018 step: 5400 accuracy: 0.945 Mon Nov 26 12:51:37 2018 step: 5500 accuracy: 0.96 Mon Nov 26 12:55:16 2018 step: 5600 accuracy: 0.93 Mon Nov 26 12:58:56 2018 step: 5700 accuracy: 0.9 Mon Nov 26 13:02:36 2018 step: 5800 accuracy: 0.9425 Mon Nov 26 13:06:15 2018 step: 5900 accuracy: 0.9275 Mon Nov 26 13:09:54 2018 step: 6000 accuracy: 0.9225 Mon Nov 26 13:13:34 2018 step: 6100 accuracy: 0.945 Mon Nov 26 13:17:13 2018 step: 6200 accuracy: 0.945 Mon Nov 26 13:20:52 2018 step: 6300 accuracy: 0.9475 Mon Nov 26 13:24:32 2018 step: 6400 accuracy: 0.9675 Mon Nov 26 13:28:13 2018 step: 6500 accuracy: 0.95 Mon Nov 26 13:31:51 2018 step: 6600 accuracy: 0.9375 Mon Nov 26 13:35:30 2018 step: 6700 accuracy: 0.9475 Mon Nov 26 13:39:09 2018 step: 6800 accuracy: 0.95 Mon Nov 26 13:42:49 2018 step: 6900 accuracy: 0.96 Mon Nov 26 13:46:29 2018 step: 7000 accuracy: 0.955 Mon Nov 26 13:50:09 2018 step: 7100 accuracy: 0.9425 Mon Nov 26 13:53:48 2018 step: 7200 accuracy: 0.9625 Mon Nov 26 13:57:27 2018 step: 7300 accuracy: 0.94 Mon Nov 26 14:01:10 2018 step: 7400 accuracy: 0.97 Mon Nov 26 14:04:52 2018 step: 7500 accuracy: 0.9675 Mon Nov 26 14:08:31 2018 step: 7600 accuracy: 0.9575 Mon Nov 26 14:12:11 2018 step: 7700 accuracy: 0.96 Mon Nov 26 14:15:51 2018 step: 7800 accuracy: 0.9425 Mon Nov 26 14:19:31 2018 step: 7900 accuracy: 0.965 Mon Nov 26 14:23:11 2018 step: 8000 accuracy: 0.9725 Mon Nov 26 14:26:51 2018 step: 8100 accuracy: 0.9825 Mon Nov 26 14:30:32 2018 step: 8200 accuracy: 0.975 Mon Nov 26 14:34:11 2018 step: 8300 accuracy: 0.9625 Mon Nov 26 14:37:49 2018 step: 8400 accuracy: 0.9775 Mon Nov 26 14:41:29 2018 step: 8500 accuracy: 0.955 Mon Nov 26 14:45:09 2018 step: 8600 accuracy: 0.9675 Mon Nov 26 14:48:47 2018 step: 8700 accuracy: 0.96 Mon Nov 26 14:52:27 2018 step: 8800 accuracy: 0.9625 Mon Nov 26 14:56:06 2018 step: 8900 accuracy: 0.9525 Mon Nov 26 14:59:45 2018 step: 9000 accuracy: 0.9575 Mon Nov 26 15:03:24 2018 step: 9100 accuracy: 0.9675 Mon Nov 26 15:07:02 2018 step: 9200 accuracy: 0.9725 Mon Nov 26 15:10:41 2018 step: 9300 accuracy: 0.9575 Mon Nov 26 15:14:19 2018 step: 9400 accuracy: 0.97 Mon Nov 26 15:17:57 2018 step: 9500 accuracy: 0.9775 Mon Nov 26 15:21:36 2018 step: 9600 accuracy: 0.9775 Mon Nov 26 15:25:14 2018 step: 9700 accuracy: 0.97 Mon Nov 26 15:28:53 2018 step: 9800 accuracy: 0.98 Mon Nov 26 15:32:32 2018 step: 9900 accuracy: 0.9825 Mon Nov 26 15:36:10 2018 step: 10000 accuracy: 0.9775 Mon Nov 26 15:39:48 2018 step: 10100 accuracy: 0.97 Mon Nov 26 15:43:26 2018 step: 10200 accuracy: 0.9575 Mon Nov 26 15:47:05 2018 step: 10300 accuracy: 0.9625 Mon Nov 26 15:50:42 2018 step: 10400 accuracy: 0.9675 Mon Nov 26 15:54:21 2018 step: 10500 accuracy: 0.9725 Mon Nov 26 15:57:59 2018 step: 10600 accuracy: 0.9825 Mon Nov 26 16:01:39 2018 step: 10700 accuracy: 0.9825 Mon Nov 26 16:05:18 2018 step: 10800 accuracy: 0.985 Mon Nov 26 16:08:57 2018 step: 10900 accuracy: 0.9625 Mon Nov 26 16:12:38 2018 step: 11000 accuracy: 0.965 Mon Nov 26 16:16:18 2018 step: 11100 accuracy: 0.97 Mon Nov 26 16:19:58 2018 step: 11200 accuracy: 0.9875 Mon Nov 26 16:23:37 2018 step: 11300 accuracy: 0.98 Mon Nov 26 16:27:17 2018 step: 11400 accuracy: 0.9625 Mon Nov 26 16:30:58 2018 step: 11500 accuracy: 0.975 Mon Nov 26 16:34:39 2018 step: 11600 accuracy: 0.985 Mon Nov 26 16:38:18 2018 step: 11700 accuracy: 0.9775 Mon Nov 26 16:41:58 2018 step: 11800 accuracy: 0.9825 Mon Nov 26 16:45:38 2018 step: 11900 accuracy: 0.98 Mon Nov 26 16:49:18 2018 step: 12000 accuracy: 0.9825 Mon Nov 26 16:52:57 2018 step: 12100 accuracy: 0.985 Mon Nov 26 16:56:36 2018 step: 12200 accuracy: 0.98 Mon Nov 26 17:00:15 2018 step: 12300 accuracy: 0.9725 Mon Nov 26 17:03:54 2018 step: 12400 accuracy: 0.955 Mon Nov 26 17:07:35 2018 step: 12500 accuracy: 0.97 Mon Nov 26 17:11:14 2018 step: 12600 accuracy: 0.9775 Mon Nov 26 17:14:53 2018 step: 12700 accuracy: 0.985 ... ```
