update readme and sample.json

README.md

@@ -108,8 +108,8 @@ use CNN recognize captcha by tensorflow.
 | 序号 | 文件名称 | 说明 |
 | ------ | ------ | ------ |
 | 1 | verify_and_split_data.py | 验证数据集和拆分数据为训练集和测试集 |
-| 2 | train_model.py | 训练模型 |
-| 3 | train_model_v2.py | 训练模型，训练过程中同时输出训练集和验证集的准确率，推荐使用此种方式训练 |
+| 2 | network.py | cnn网络基类 |
+| 3 | train_model.py | 训练模型，训练过程中同时输出训练集和验证集的准确率，推荐使用此种方式训练 |
 | 4 | test_batch.py | 批量验证 |
 | 5 | gen_image/gen_sample_by_captcha.py | 生成验证码的脚本 |
 | 6 | gen_image/collect_labels.py | 用于统计验证码标签（常用于中文验证码） |
@@ -359,4 +359,5 @@ tensorflow.python.framework.errors_impl.InvalidArgumentError: Unsuccessful Tenso
 原因是两个Recognizer对象都使用了默认的Graph。
 解决办法是在创建对象的时候不使用默认Graph，新建graph，这样每个Recognizer都使用不同的graph，就不会冲突了。
 
-5. Flask程序的并发运行
+5. Flask程序用于生产
+可以参考官方文档：[Flask的生产配置](http://docs.jinkan.org/docs/flask/config.html)

sample.json

@@ -0,0 +1,57 @@
+{
+    "origin_image_dir": "sample/origin/",
+    "train_image_dir": "sample/train/",
+    "test_image_dir": "sample/test/",
+    "api_image_dir": "sample/api/",
+    "online_image_dir": "sample/online/",
+    "local_image_dir": "sample/local/",
+    "model_save_dir": "model/",
+    "image_width": 100,
+    "image_height": 60,
+    "max_captcha": 4,
+    "image_suffix": "png",
+    "char_set": [
+        "0",
+        "1",
+        "2",
+        "3",
+        "4",
+        "5",
+        "6",
+        "7",
+        "8",
+        "9",
+        "a",
+        "b",
+        "c",
+        "d",
+        "e",
+        "f",
+        "g",
+        "h",
+        "i",
+        "j",
+        "k",
+        "l",
+        "m",
+        "n",
+        "o",
+        "p",
+        "q",
+        "r",
+        "s",
+        "t",
+        "u",
+        "v",
+        "w",
+        "x",
+        "y",
+        "z"
+    ],
+    "use_labels_json_file": false,
+    "remote_url": "https://www.xxxxx.com/getImg",
+    "cycle_stop": 3000,
+    "acc_stop": 0.99,
+    "cycle_save": 500,
+    "enable_gpu": 0
+}