{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524\n",
      "特啦同爱手使清弟时还睡近线却文政完么展便工在不办入强路安起理斥队何听农验它哦呢座口刚叔沙信內笑装往用更站社亲房跑啊章屋马直众立前都其造该瓜片论水夜习白感离下神送常当只认干发到思席研画领极指吗讲九越仗过受这已乐穿原才包丛满岸第千限今色种头被要活或转紧没后向气咯者跟想你吧学如问忙界解坚科一确非经张快顶由争目雪仔拿分小照般表牛命上北各他部之新孩令黑爬无级友睛脸关忽给动嘴南事写应光将情八晚成紫步所呀些飞息似车开边敢匆阵伯志禾四着够底处道衣说难七音伟仪儿通术面形停胜尤二谁题您赶热万深历导帮反收行传少生度侯员位斗会流五岁村因决但轿歌菜地找古再作物力总她样字围性准苦和怎人咱六候冲叶空加付提望外而熟共坐战连读吃点把是让心打于丘那间河整劳老建倒数治女本十接每高竺世身单山土城个敌明类士己失乎觉合门区轻究子定中我雨记体从至必甩半任告就得册民草姑产什报现青算钱太比大压见师国运石取们出怕像句唱知话家旧主眼自野去变化哪重花急火然哥并星别乡多书法月阶回相早意系以天很破的件业带跳仙机了林印声先代旁风渐长进许名晴块阳船也放几实年团此果最军史际脚革又公树呼婆切饭群两做全里平有庄等改未拉来根亮叫次百刻响海结三落走场识教且为义内条慢方寻真观看东日利答兴服枪掉量住好对可背细\n",
      "524\n"
     ]
    }
   ],
   "source": [
    "from captcha.image import ImageCaptcha\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "import glob\n",
    "import re\n",
    "from pylab import mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['SimHei']  #中文显示问题\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import string\n",
    "# characters = string.digits + string.ascii_uppercase # 验证码字符集合数字+英文\n",
    "with open('/data/captcha/chinese_characters.txt', encoding='utf-8') as f:\n",
    "    characters = f.read().strip()\n",
    "print(len(characters))\n",
    "# characters = '四生乐句付仗斥令仔乎白仙甩他瓜们用丘仪失丛代印册匆禾' # 中文字符集合\n",
    "print(characters)\n",
    "print(len(set(characters)))\n",
    "width, height, n_len, n_class = 120, 40, 4, len(characters) + 1  #图片宽、高，验证码最大长度，分类类别：字符集+1个空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    " # 防止 tensorflow 占用所有显存\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth=True  #True \n",
    "sess = tf.Session(config=config)\n",
    "K.set_session(sess)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义 CTC Loss\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "def ctc_lambda_func(args):\n",
    "    '''\n",
    "        定义ctc损失函数\n",
    "        参数：y_pred:预测值,labels:标签，input_length:lstm tiemstep,label_length:标签长度\n",
    "    '''    \n",
    "    y_pred, labels, input_length, label_length = args\n",
    "    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义网络\n",
    "from tensorflow.keras.models import *\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "input_tensor = Input((height, width, 3))\n",
    "x = input_tensor\n",
    "\n",
    "for i, n_cnn in enumerate([2, 2, 2, 2]):    \n",
    "    for j in range(n_cnn):\n",
    "        x = Conv2D(32*2**min(i, 3), kernel_size=3, padding='same', kernel_initializer='he_uniform')(x)  # 32*2**min(i, 3)\n",
    "        x = BatchNormalization()(x)\n",
    "        x = Activation('relu')(x)\n",
    "    x = MaxPooling2D(2 if i < 3 else (2, 1))(x)\n",
    "\n",
    "x = Permute((2, 1, 3))(x)\n",
    "x = TimeDistributed(Flatten())(x)\n",
    "\n",
    "rnn_size = 64 # 128\n",
    "# x = Bidirectional(CuDNNGRU(rnn_size, return_sequences=True))(x)\n",
    "# x = Bidirectional(CuDNNGRU(rnn_size, return_sequences=True))(x)\n",
    "x = Bidirectional(GRU(rnn_size, return_sequences=True))(x)\n",
    "x = Bidirectional(GRU(rnn_size, return_sequences=True))(x)\n",
    "x = Dense(n_class, activation='softmax')(x)\n",
    "\n",
    "base_model = Model(inputs=input_tensor, outputs=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = Input(name='the_labels', shape=[n_len], dtype='float32')\n",
    "input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
    "label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
    "loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([x, labels, input_length, label_length])\n",
    "\n",
    "model = Model(inputs=[input_tensor, labels, input_length, label_length], outputs=loss_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 网络结构可视化\n",
    "# from tensorflow.keras.utils import plot_model\n",
    "# from IPython.display import Image\n",
    "\n",
    "# plot_model(model, to_file='ctc.png', show_shapes=True)\n",
    "# Image('ctc.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 40, 120, 3)        0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 40, 120, 32)       896       \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 40, 120, 32)       128       \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 40, 120, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 40, 120, 32)       9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 40, 120, 32)       128       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 40, 120, 32)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 20, 60, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 20, 60, 64)        18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 20, 60, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 20, 60, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 20, 60, 64)        36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 20, 60, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 20, 60, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 10, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 10, 30, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 10, 30, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 10, 30, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 10, 30, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 10, 30, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 10, 30, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 5, 15, 128)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 5, 15, 256)        295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 5, 15, 256)        1024      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 5, 15, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 5, 15, 256)        590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 5, 15, 256)        1024      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 5, 15, 256)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 2, 15, 256)        0         \n",
      "_________________________________________________________________\n",
      "permute (Permute)            (None, 15, 2, 256)        0         \n",
      "_________________________________________________________________\n",
      "time_distributed (TimeDistri (None, 15, 512)           0         \n",
      "_________________________________________________________________\n",
      "bidirectional (Bidirectional (None, 15, 128)           221568    \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 15, 128)           74112     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 15, 525)           67725     \n",
      "=================================================================\n",
      "Total params: 1,539,501\n",
      "Trainable params: 1,537,581\n",
      "Non-trainable params: 1,920\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "等伯今立 [478, 207, 114, 62] 等伯今立\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 201,
       "width": 1151
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image, ImageFont, ImageDraw\n",
    "\n",
    "def random_color(start, end, opacity=None):\n",
    "    '''\n",
    "    随机颜色函数，返回指定范围随机颜色值\n",
    "    参数：start:颜色最低值，end:颜色最高值\n",
    "    '''\n",
    "    red = random.randint(start, end)\n",
    "    green = random.randint(start, end)\n",
    "    blue = random.randint(start, end)\n",
    "    if opacity is None:\n",
    "        return (red, green, blue)\n",
    "    return (red, green, blue, opacity)\n",
    "def random_xy(width,height):   \n",
    "    '''\n",
    "    随机位置函数，返回指定范围随机位置坐标\n",
    "    参数：width:图片宽，height:图片高\n",
    "    '''\n",
    "    x = random.randint(0, width)\n",
    "    y = random.randint(0, height)\n",
    "    return x, y\n",
    "\n",
    "table  =  []\n",
    "for  i  in  range( 256 ):\n",
    "    table.append( i * 1.97 )\n",
    "    \n",
    "def create_captcha_image(chars, background, width=120, height=40):\n",
    "    '''\n",
    "    生成验证码图片\n",
    "    chars：要生成的字符串\n",
    "    background:背景颜色\n",
    "    '''\n",
    "    image = Image.new('RGB', (width, height), color=background)\n",
    "    draw = ImageDraw.Draw(image)\n",
    "    def get_char_img(char,font,color,angle):\n",
    "        '''\n",
    "        生成单个字符图片，随机颜色加随机旋转\n",
    "        \n",
    "        '''\n",
    "        w, h = draw.textsize(char, font=font)\n",
    "        im = Image.new('RGBA',(w,h), color=background)\n",
    "        ImageDraw.Draw(im).text((0,0), char, font=font, fill=color)\n",
    "        im = im.crop(im.getbbox())\n",
    "        rot = im.rotate(angle,Image.BILINEAR,expand=1)\n",
    "        bg = Image.new('RGBA',rot.size,background)\n",
    "        im = Image.composite(rot, bg, rot)\n",
    "        return im\n",
    "    w_all = 0\n",
    "    im_list = []\n",
    "    w_list = []\n",
    "    for c in chars:\n",
    "        fonts = ['/usr/share/fonts/WindowsFonts/fonts/STXINGKA.TTF','/usr/share/fonts/WindowsFonts/fonts/simhei.ttf']\n",
    "        font = ImageFont.truetype(font=random.choice(fonts), size=random.randint(18,20))\n",
    "        char_img = get_char_img(char=c, font=font, color=random_color(0,90), angle=random.randint(0,0))\n",
    "        w, h = char_img.size\n",
    "        w_all += random.randint(0,5)  \n",
    "        w_list.append(w_all)\n",
    "        im_list.append(char_img)\n",
    "#         image.paste(char_img, (w_all,random.randint(0,image.size[1]-h)))\n",
    "        w_all += w\n",
    "    if w_all > width:\n",
    "        image = Image.new('RGB', (w_all, height), color=background)\n",
    "\n",
    "    for i in range(len(w_list)):\n",
    "        image.paste(im_list[i], (w_list[i],random.randint(0,height-im_list[i].size[1])))\n",
    "    return image.resize((width, height))\n",
    "\n",
    "def generate_image(random_str, width=120, height=40):\n",
    "    '''\n",
    "    随机生成验证码，从四种字体随机抽取一种生成文字，加随机线干扰和点干扰\n",
    "    参数：random_str:要生成验证码的文字\n",
    "    返回：验证码图片\n",
    "    '''    \n",
    "#     image = Image.new(mode='RGB', size=(width, height), color=(255,255,255))\n",
    "#     fonts = ['/usr/share/fonts/WindowsFonts/fonts/simsunb.ttf']\n",
    "    fonts = ['/usr/share/fonts/WindowsFonts/fonts/ariali.ttf', '/usr/share/fonts/WindowsFonts/fonts/simhei.ttf',\n",
    "            '/usr/share/fonts/WindowsFonts/fonts/simsunb.ttf', '/usr/share/fonts/WindowsFonts/fonts/calibri.ttf']\n",
    "    font = ImageFont.truetype(font=random.choice(fonts), size=20)\n",
    "    chars = random_str\n",
    "    color = random_color(120,250)\n",
    "    background = random_color(110,255)\n",
    "    image = create_captcha_image(chars, background, width=width, height=height)\n",
    "    draw = ImageDraw.Draw(image) \n",
    "#     for _ in range(random.randint(5, 10)):\n",
    "#         draw.line(xy=(random_xy(width,height),random_xy(width,height)),fill=random_color(80, 255))\n",
    "    for _ in range(random.randint(100,150)):\n",
    "        draw.point(xy=(random_xy(width,height)),fill=random_color(20, 250))\n",
    "#     for _ in range(random.randint(50,80)):\n",
    "#         x,y = random_xy(width,height)\n",
    "#         draw.line(xy=(x,y,x+random.randint(-10,15),y+random.randint(-10,15)),fill=random_color(120, 255))\n",
    "       \n",
    "#     draw.text(xy=(random.randint(0,10),random.randint(0,2)),text= random_str, fill=text_fill, font=font)\n",
    "#     len_t = len(random_str)\n",
    "#     for i in range(len_t):\n",
    "#         draw.text(xy=(random.randint(0,2)+i*int(width/len_t),random.randint(1,6)),text= random_str[i], \n",
    "#                   fill=random_color(0,180,opacity=80), font=font)\n",
    "    \n",
    "    return image.resize((120,40), Image.BILINEAR)\n",
    "\n",
    "random_str = ''.join([random.choice(characters) for j in range(random.randint(4,4))])\n",
    "label = [characters.find(x) for x in random_str]\n",
    "label_str = ''.join([characters[x] for x in label if x < len(characters)])\n",
    "print(random_str, label, label_str)\n",
    "img = generate_image(random_str, 100, 25)  \n",
    "# img = generate_image('瓜用匆生')\n",
    "img2 = Image.open('Chinese/1da7be39-2189-11ea-b304-408d5cd36814_瓜用匆生.jpg')\n",
    "img2 = img2.resize((120,40), Image.BILINEAR)\n",
    "im = [img, img2]\n",
    "plt.figure(figsize=(20,10))\n",
    "for i in range(1,3):    \n",
    "    plt.subplot(2,2,i)\n",
    "    plt.imshow(im[i-1])\n",
    "plt.show()\n",
    "\n",
    "# plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "用四匆匆\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 144,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 重组验证码\n",
    "def rebuild_img(path):\n",
    "    '''\n",
    "    读取本地4-5位验证码图片进行裁剪分割为单个数字，从分割的字符随机抽取n个重组为新图片\n",
    "    参数：path：图片路径\n",
    "    返回：重组后图片\n",
    "    '''\n",
    "    label = path.split('_')[-1][:-4]\n",
    "    if label.isalpha() and len(label) > 3:\n",
    "        crop_n = len(label) \n",
    "        img = Image.open(path)\n",
    "        w, h = img.size\n",
    "        fig_size = int(w/crop_n)\n",
    "        fig_list = []\n",
    "        new_label = []\n",
    "        if crop_n == 4:\n",
    "            for i in range(crop_n):\n",
    "                fig_list.append(img.crop((i*fig_size+2, 2, (i+1)*fig_size-2, h-2 )))\n",
    "            for i in range(crop_n):\n",
    "                idx = random.randint(0,crop_n-1) # 修改为打乱顺序\n",
    "                img.paste(fig_list[idx], (i*fig_size+2, 2, (i+1)*fig_size-2, h-2 ))\n",
    "                new_label.append(label[idx])\n",
    "        elif crop_n == 5:        \n",
    "            for i in range(crop_n):\n",
    "                fig_list.append(img.crop((i*fig_size, 0, (i+1)*fig_size, h )))\n",
    "            for i in range(crop_n):\n",
    "                idx = random.randint(0,crop_n-1)\n",
    "                img.paste(fig_list[idx], (i*fig_size, 0, (i+1)*fig_size, h ))\n",
    "                new_label.append(label[idx])\n",
    "                \n",
    "        draw = ImageDraw.Draw(img) \n",
    "        for _ in range(random.randint(0,50)):  # 在重组的验证码图片上加噪声\n",
    "            draw.point(xy=(random_xy(width,height)),fill=random_color(20, 250))\n",
    "            \n",
    "        return img.resize((120,40), Image.BILINEAR), ''.join(new_label)\n",
    "image, random_str = rebuild_img(random.choice(glob.glob('Chinese/*.jpg')))  \n",
    "print(random_str)\n",
    "plt.imshow(image)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文件名：标签 字典大小 4092\n",
      "标签： 究旧当物\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 144,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_label_dic(label_file):\n",
    "    '''通过文件名=标签 文件重构 名字：标签 字典'''\n",
    "    from collections import defaultdict\n",
    "    label_dic = defaultdict(str)\n",
    "    with open(label_file, encoding='utf-8') as f:\n",
    "        lines = f.readlines()\n",
    "        for line in lines:\n",
    "            result = line.split('=')[-1].strip()\n",
    "            name = line.split('=')[0].strip()\n",
    "            if len(result) != 4:\n",
    "                continue\n",
    "            else:            \n",
    "                label_dic[name] = result\n",
    "    return label_dic\n",
    "\n",
    "label_dic = get_label_dic('/data/captcha/total_chinese/total_chinese.txt')\n",
    "print('文件名：标签 字典大小',len(label_dic))\n",
    "\n",
    "def smartSliceImg(img_path, label_dic, count=4, p_w=3):\n",
    "    '''\n",
    "    :param img:\n",
    "    :param outDir:\n",
    "    :param count: 图片中有多少个图片\n",
    "    :param p_w: 对切割地方多少像素内进行判断\n",
    "    :return:\n",
    "    '''\n",
    "    img = Image.open(img_path)\n",
    "    w, h = img.size\n",
    "    pixdata = img.load()\n",
    "    eachWidth = int(w / count)\n",
    "    beforeX = 1\n",
    "    name = img_path.split('/')[-1][:-4]\n",
    "    label = label_dic.get(name, None)\n",
    "    if not label or re.search('[\\u4e00-\\u9fa5]', label)==None:\n",
    "        print(label)\n",
    "        return\n",
    "\n",
    "    imgs = []\n",
    "    for i in range(count):\n",
    "        allBCount = []\n",
    "        nextXOri = (i + 1) * eachWidth\n",
    "\n",
    "        for x in range(nextXOri - p_w, nextXOri + p_w):\n",
    "            if x >= w:\n",
    "                x = w - 1\n",
    "            if x < 0:\n",
    "                x = 0\n",
    "            b_count = 1\n",
    "            for y in range(h):\n",
    "#                 if pixdata[x,y]==pixdata[x,2]:\n",
    "                if pixdata[x,y][0]==pixdata[x,y][1]==pixdata[x,y][2]:\n",
    "                    b_count += 1\n",
    "            allBCount.append({'x_pos': x, 'count': b_count})\n",
    "        sort = sorted(allBCount, key=lambda e: e.get('count'), reverse=True)\n",
    "#         print(allBCount)\n",
    "#         print(sort)\n",
    "        nextX = sort[0]['x_pos'] if sort[0]['x_pos']<w-5 else w-1\n",
    "        box = (beforeX, 1, nextX, h-1)\n",
    "#         crop_img = label[i]+\"_\"+name+\".jpg\"\n",
    "        imgs.append(img.crop(box))\n",
    "\n",
    "        beforeX = nextX\n",
    "    x = 1\n",
    "    idx = [0,1,2,3]\n",
    "    random.shuffle(idx)\n",
    "    new_label = []\n",
    "    for i in range(count):\n",
    "        w,h = imgs[idx[i]].size\n",
    "        img.paste(imgs[idx[i]], (x,1))\n",
    "        x += w\n",
    "        new_label.append(label[idx[i]])\n",
    "    draw = ImageDraw.Draw(img) \n",
    "    for _ in range(random.randint(0,50)):  # 在重组的验证码图片上加噪声\n",
    "        draw.point(xy=(random_xy(width,height)),fill=random_color(195, 255))    \n",
    "        \n",
    "    return img.resize((120,40)), \"\".join(new_label)\n",
    "\n",
    "'''构造标签字典，切分验证码'''   \n",
    "\n",
    "\n",
    "\n",
    "path = random.choice(glob.glob('/data/captcha/total_chinese/*.jpg'))\n",
    "img, label = smartSliceImg(path, label_dic, count=4, p_w=2)\n",
    "\n",
    "print('标签：',label)\n",
    "img = img.resize((120,40))\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "\n",
    "# for img_path in glob.glob('/data/captcha/total_chinese/*.jpg'):\n",
    "#     name = img_path.split('/')[-1][:-4]\n",
    "#     label = label_dic.get(name, None)\n",
    "#     if not label or re.search('[\\u4e00-\\u9fa5]', label)==None:\n",
    "#         print(label, name)\n",
    "#         print(img_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义数据生成器\n",
    "from tensorflow.keras.utils import Sequence\n",
    "\n",
    "class CaptchaSequence(Sequence):\n",
    "    '''\n",
    "    继承Sequence的数据生成类，方便调用多CPU，加快生成训练及测试数据\n",
    "    参数：self.characters:验证码字符集合，self.batch_size：每批次样本数，self.steps：生成多少批数据，self.n_len：验证码长度，\n",
    "    self.width：图片宽度，self.height:图片高度，self.input_length：lstm time step长度，self.label_length：标签长度\n",
    "    返回：array类型训练或测试数据 \n",
    "    \n",
    "    '''\n",
    "    def __init__(self, characters, batch_size, steps, n_len=4, width=120, height=40, \n",
    "                 input_length=15, label_length=4, chars_len=(4, 4)): #  width=128, height=64, input_length=16, label_length=4\n",
    "        self.characters = characters\n",
    "        self.batch_size = batch_size\n",
    "        self.steps = steps\n",
    "        self.n_len = n_len\n",
    "        self.width = width\n",
    "        self.height = height\n",
    "        self.input_length = input_length\n",
    "        self.label_length = label_length\n",
    "        self.chars_len = chars_len\n",
    "#         self.label_length = self.n_len\n",
    "        self.n_class = len(characters)\n",
    "        self.generator = ImageCaptcha(width=width, height=height, font_sizes=(12,20,18,25))\n",
    "            \n",
    "    def __len__(self):\n",
    "        return self.steps\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        X = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n",
    "        y = np.zeros((self.batch_size, self.n_len), dtype=np.int32) #np.uint8\n",
    "        input_length = np.ones(self.batch_size)*self.input_length\n",
    "        label_length = np.ones(self.batch_size)*self.label_length        \n",
    "        for i in range(self.batch_size):            \n",
    "            if i % 6 == 0:\n",
    "                image, random_str = rebuild_img(random.choice(glob.glob('Chinese/*.jpg')))    \n",
    "            elif i % 6 == 1: \n",
    "                image, random_str = smartSliceImg(random.choice(glob.glob('/data/captcha/total_chinese/*.jpg')), label_dic, count=4, p_w=2)\n",
    "            elif i % 6==2: \n",
    "                random_str = ''.join([random.choice(self.characters) for j in range(random.randint(self.chars_len[0],self.chars_len[1]))])\n",
    "                image = generate_image(random_str, 100, 25)    \n",
    "            else:                \n",
    "                random_str = ''.join([random.choice(self.characters) for j in range(random.randint(self.chars_len[0],self.chars_len[1]))])\n",
    "                image = generate_image(random_str, 120, 40)    \n",
    "            \n",
    "#             elif i % 2 == 0:\n",
    "#                 random_str = ''.join([random.choice(self.characters) for j in range(random.randint(self.chars_len[0],self.chars_len[1]))])\n",
    "#                 image = self.generator.generate_image(random_str)                           \n",
    "#             elif i % 4 == 3:\n",
    "#                 image, random_str = rebuild_img(random.choice(glob.glob('Digit5/*.jpg')))   \n",
    "            X[i] = np.array(image)/255.0\n",
    "            label = [self.characters.find(x) for x in random_str]\n",
    "#             print(\"生成器\", random_str, label)\n",
    "#             if len(random_str) < self.n_len:\n",
    "#                 label += [self.n_class]*(self.n_len-len(random_str)) \n",
    "            y[i] = label\n",
    "#             print(\"生成器\", random_str, label, y, y.shape)\n",
    "\n",
    "        return [X, y, input_length, label_length], np.ones(self.batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "定忽同争\n",
      "[347 179   2 150]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 144,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试生成器\n",
    "# import matplotlib\n",
    "# # matplotlib.use('qt4agg')\n",
    "# #指定默认字体\n",
    "# matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# matplotlib.rcParams['font.family']='sans-serif'\n",
    "# #解决负号'-'显示为方块的问题\n",
    "# matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "data = CaptchaSequence(characters, batch_size=10, steps=1)\n",
    "[X_test, y_test, input_length, label_length], _  = data[0]\n",
    "idx =3\n",
    "plt.imshow(X_test[idx])\n",
    "print(''.join([characters[x] for x in y_test[idx]]))\n",
    "print(y_test[idx])\n",
    "# print(''.join([characters[x] for x in y_test[idx] if x < len(characters)]))\n",
    "# plt.title(''.join([characters[x] for x in y_test[idx] if x < len(characters)]))\n",
    "# print(input_length, label_length)\n",
    "# print(y_test)\n",
    "# print(X_test.shape)\n",
    "# print(n_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "于话重胜\n"
     ]
    }
   ],
   "source": [
    "# # 从现有图片生成测试数据\n",
    "# def get_data(img_path):\n",
    "#     img = Image.open(img_path)\n",
    "# #     img = img.crop((0, height-25, width, height))\n",
    "#     w, h = img.size\n",
    "#     data = np.zeros((1,h, w, 3))\n",
    "#     data[0] = np.array(img)/255.0\n",
    "#     return data\n",
    "# img_path = '../FileInfo/ffc510f4-f977-11e9-b970-408d5cd36814_5802.jpg'\n",
    "\n",
    "# data = get_data(img_path)\n",
    "# print(data.shape)\n",
    "# plt.imshow(data[0])\n",
    "print(''.join([characters[x] for x in [307, 390, 401, 229]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准确率回调函数\n",
    "from tqdm import tqdm\n",
    "\n",
    "def evaluate(model, batch_size=128, steps=1):\n",
    "    '''\n",
    "    准确率验证函数，每批次的验证码长度必须一致\n",
    "    '''    \n",
    "    batch_acc = 0\n",
    "    valid_data = CaptchaSequence(characters, batch_size, steps)\n",
    "    for i in range(len(valid_data)):\n",
    "        [X_test, y_test, _, _], _ = valid_data[i]\n",
    "        y_pred = base_model.predict(X_test)\n",
    "        shape = y_pred.shape\n",
    "    #         out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(shape[0])*shape[1],)[0][0])[:, :4]\n",
    "        out = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(shape[0])*shape[1],)[0][0])[:, :]\n",
    "    #     print(y_test)\n",
    "    #     print(type(y_test))\n",
    "    #     print(y_test[y_test<10, axis=1])\n",
    "    #     print(out)\n",
    "        if out.shape[1] >= 4:\n",
    "            batch_acc += (y_test[:,:out.shape[1]] == out).all(axis=1).mean()\n",
    "    return batch_acc / steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27000000000000002"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model.load_weights('digit4to6_ctc_best2.h5')\n",
    "evaluate(base_model,batch_size=1, steps=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import Callback\n",
    "\n",
    "class Evaluate(Callback):\n",
    "    '''\n",
    "    准确率验证的类，每批次的验证码长度必须一致\n",
    "    '''    \n",
    "    def __init__(self):\n",
    "        self.accs = []\n",
    "    \n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        logs = logs or {}\n",
    "        acc = evaluate(base_model, batch_size=128)  # evaluate(base_model)\n",
    "        logs['val_acc'] = acc\n",
    "        self.accs.append(acc)\n",
    "        print('\\nacc%.4f'%acc)\n",
    "#         print(f'\\nacc: {acc*100:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "1000/1000 [==============================] - 262s 262ms/step - loss: 23.9300 - val_loss: 22.1291\n",
      "Epoch 2/200\n",
      "1000/1000 [==============================] - 266s 266ms/step - loss: 20.2433 - val_loss: 19.9841\n",
      "Epoch 3/200\n",
      "1000/1000 [==============================] - 264s 264ms/step - loss: 19.5101 - val_loss: 18.1271\n",
      "Epoch 4/200\n",
      "1000/1000 [==============================] - 270s 270ms/step - loss: 15.8576 - val_loss: 13.6020\n",
      "Epoch 5/200\n",
      "1000/1000 [==============================] - 251s 251ms/step - loss: 11.2304 - val_loss: 10.5907\n",
      "Epoch 6/200\n",
      "1000/1000 [==============================] - 253s 253ms/step - loss: 7.7945 - val_loss: 6.8276\n",
      "Epoch 7/200\n",
      "1000/1000 [==============================] - 249s 249ms/step - loss: 5.0638 - val_loss: 4.8995\n",
      "Epoch 8/200\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 3.2676 - val_loss: 3.1209\n",
      "Epoch 9/200\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 2.2798 - val_loss: 2.4022\n",
      "Epoch 10/200\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 1.6577 - val_loss: 1.5855\n",
      "Epoch 11/200\n",
      "1000/1000 [==============================] - 258s 258ms/step - loss: 1.3260 - val_loss: 1.3435\n",
      "Epoch 12/200\n",
      "1000/1000 [==============================] - 263s 263ms/step - loss: 1.0780 - val_loss: 1.1741\n",
      "Epoch 13/200\n",
      "1000/1000 [==============================] - 262s 262ms/step - loss: 0.9467 - val_loss: 0.9290\n",
      "Epoch 14/200\n",
      "1000/1000 [==============================] - 262s 262ms/step - loss: 0.8385 - val_loss: 0.7924\n",
      "Epoch 15/200\n",
      "1000/1000 [==============================] - 248s 248ms/step - loss: 0.7515 - val_loss: 0.7596\n",
      "Epoch 16/200\n",
      "1000/1000 [==============================] - 222s 222ms/step - loss: 0.6868 - val_loss: 0.6856\n",
      "Epoch 17/200\n",
      "1000/1000 [==============================] - 219s 219ms/step - loss: 0.6275 - val_loss: 0.6722\n",
      "Epoch 18/200\n",
      "1000/1000 [==============================] - 250s 250ms/step - loss: 0.6068 - val_loss: 0.6041\n",
      "Epoch 19/200\n",
      "1000/1000 [==============================] - 256s 256ms/step - loss: 0.5443 - val_loss: 0.7427\n",
      "Epoch 20/200\n",
      "1000/1000 [==============================] - 247s 247ms/step - loss: 0.5192 - val_loss: 0.6104\n",
      "Epoch 21/200\n",
      "1000/1000 [==============================] - 192s 192ms/step - loss: 0.5181 - val_loss: 0.5642\n",
      "Epoch 22/200\n",
      "1000/1000 [==============================] - 175s 175ms/step - loss: 0.4950 - val_loss: 0.4901\n",
      "Epoch 23/200\n",
      "1000/1000 [==============================] - 179s 179ms/step - loss: 0.4738 - val_loss: 0.8496\n",
      "Epoch 24/200\n",
      "1000/1000 [==============================] - 179s 179ms/step - loss: 0.4547 - val_loss: 0.4348\n",
      "Epoch 25/200\n",
      "1000/1000 [==============================] - 178s 178ms/step - loss: 0.4420 - val_loss: 0.5450\n",
      "Epoch 26/200\n",
      "1000/1000 [==============================] - 197s 197ms/step - loss: 0.4077 - val_loss: 0.5357\n",
      "Epoch 27/200\n",
      "1000/1000 [==============================] - 179s 179ms/step - loss: 0.4126 - val_loss: 0.3803\n",
      "Epoch 28/200\n",
      "1000/1000 [==============================] - 194s 194ms/step - loss: 0.3915 - val_loss: 0.4115\n",
      "Epoch 29/200\n",
      "1000/1000 [==============================] - 187s 187ms/step - loss: 0.3893 - val_loss: 0.4076\n",
      "Epoch 30/200\n",
      "1000/1000 [==============================] - 171s 171ms/step - loss: 0.3695 - val_loss: 0.4259\n",
      "Epoch 31/200\n",
      "1000/1000 [==============================] - 183s 183ms/step - loss: 0.3677 - val_loss: 0.4213\n",
      "Epoch 32/200\n",
      "1000/1000 [==============================] - 176s 176ms/step - loss: 0.3579 - val_loss: 1.5800\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7ff3304bf4e0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate()\n",
    "# 模型训练\n",
    "from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n",
    "from tensorflow.keras.optimizers import *\n",
    "# model.load_weights('digit4to6_ctc_best.h5')\n",
    "\n",
    "train_data = CaptchaSequence(characters, batch_size=128, steps=1000) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=100)  # (characters, batch_size=128, steps=100)\n",
    "# callbacks = [EarlyStopping(patience=5), Evaluate(), \n",
    "#              CSVLogger('ctc.csv'), ModelCheckpoint('ctc_best.h5', save_best_only=True)]\n",
    "callbacks = [EarlyStopping(patience=5),ModelCheckpoint('gru_chinese524char_ctc_best.h5', save_best_only=True)]\n",
    "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-3, amsgrad=True))\n",
    "# model.fit_generator(train_data, epochs=100, validation_data=valid_data,\n",
    "#                     callbacks=callbacks)\n",
    "model.fit_generator(train_data, epochs=200, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                   callbacks=callbacks)\n",
    "\n",
    "# 20200721 gru_chinese524char_ctc_best.h5 训练32个epoch从 loss: 23.9300 - val_loss: 22.1291 下降到 loss: 0.3677 - val_loss: 0.4213"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "1000/1000 [==============================] - 367s 367ms/step - loss: 0.0241 - val_loss: 0.0232\n",
      "Epoch 2/200\n",
      "1000/1000 [==============================] - 349s 349ms/step - loss: 0.0222 - val_loss: 0.0208\n",
      "Epoch 3/200\n",
      "1000/1000 [==============================] - 348s 348ms/step - loss: 0.0197 - val_loss: 0.0258\n",
      "Epoch 4/200\n",
      "1000/1000 [==============================] - 350s 350ms/step - loss: 0.0215 - val_loss: 0.0204\n",
      "Epoch 5/200\n",
      "1000/1000 [==============================] - 350s 350ms/step - loss: 0.0184 - val_loss: 0.0273\n",
      "Epoch 6/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0192 - val_loss: 0.0197\n",
      "Epoch 7/200\n",
      "1000/1000 [==============================] - 355s 355ms/step - loss: 0.0204 - val_loss: 0.0209\n",
      "Epoch 8/200\n",
      "1000/1000 [==============================] - 350s 350ms/step - loss: 0.0208 - val_loss: 0.0158\n",
      "Epoch 9/200\n",
      "1000/1000 [==============================] - 351s 351ms/step - loss: 0.0190 - val_loss: 0.0159\n",
      "Epoch 10/200\n",
      "1000/1000 [==============================] - 352s 352ms/step - loss: 0.0178 - val_loss: 0.0205\n",
      "Epoch 11/200\n",
      "1000/1000 [==============================] - 354s 354ms/step - loss: 0.0187 - val_loss: 0.0192\n",
      "Epoch 12/200\n",
      "1000/1000 [==============================] - 351s 351ms/step - loss: 0.0188 - val_loss: 0.0145\n",
      "Epoch 13/200\n",
      "1000/1000 [==============================] - 347s 347ms/step - loss: 0.0188 - val_loss: 0.0184\n",
      "Epoch 14/200\n",
      "1000/1000 [==============================] - 350s 350ms/step - loss: 0.0197 - val_loss: 0.0283\n",
      "Epoch 15/200\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0198 - val_loss: 0.0207\n",
      "Epoch 16/200\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0157 - val_loss: 0.0175\n",
      "Epoch 17/200\n",
      "1000/1000 [==============================] - 361s 361ms/step - loss: 0.0160 - val_loss: 0.0199\n",
      "Epoch 18/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0165 - val_loss: 0.0142\n",
      "Epoch 19/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0156 - val_loss: 0.0164\n",
      "Epoch 20/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0164 - val_loss: 0.0162\n",
      "Epoch 21/200\n",
      "1000/1000 [==============================] - 347s 347ms/step - loss: 0.0164 - val_loss: 0.0176\n",
      "Epoch 22/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0162 - val_loss: 0.0165\n",
      "Epoch 23/200\n",
      "1000/1000 [==============================] - 348s 348ms/step - loss: 0.0167 - val_loss: 0.0145\n",
      "Epoch 24/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0163 - val_loss: 0.0156\n",
      "Epoch 25/200\n",
      "1000/1000 [==============================] - 335s 335ms/step - loss: 0.0148 - val_loss: 0.0147\n",
      "Epoch 26/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0149 - val_loss: 0.0138\n",
      "Epoch 27/200\n",
      "1000/1000 [==============================] - 437s 437ms/step - loss: 0.0149 - val_loss: 0.0140\n",
      "Epoch 28/200\n",
      "1000/1000 [==============================] - 440s 440ms/step - loss: 0.0147 - val_loss: 0.0223\n",
      "Epoch 29/200\n",
      "1000/1000 [==============================] - 368s 368ms/step - loss: 0.0155 - val_loss: 0.0136\n",
      "Epoch 30/200\n",
      "1000/1000 [==============================] - 375s 375ms/step - loss: 0.0161 - val_loss: 0.0120\n",
      "Epoch 31/200\n",
      "1000/1000 [==============================] - 351s 351ms/step - loss: 0.0133 - val_loss: 0.0126\n",
      "Epoch 32/200\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0146 - val_loss: 0.0219\n",
      "Epoch 33/200\n",
      "1000/1000 [==============================] - 359s 359ms/step - loss: 0.0155 - val_loss: 0.0174\n",
      "Epoch 34/200\n",
      "1000/1000 [==============================] - 359s 359ms/step - loss: 0.0143 - val_loss: 0.0145\n",
      "Epoch 35/200\n",
      "1000/1000 [==============================] - 372s 372ms/step - loss: 0.0154 - val_loss: 0.0161\n",
      "Epoch 36/200\n",
      "1000/1000 [==============================] - 351s 351ms/step - loss: 0.0152 - val_loss: 0.0128\n",
      "Epoch 37/200\n",
      "1000/1000 [==============================] - 359s 359ms/step - loss: 0.0155 - val_loss: 0.0141\n",
      "Epoch 38/200\n",
      "1000/1000 [==============================] - 335s 335ms/step - loss: 0.0143 - val_loss: 0.0132\n",
      "Epoch 39/200\n",
      "1000/1000 [==============================] - 403s 403ms/step - loss: 0.0125 - val_loss: 0.0118\n",
      "Epoch 40/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0135 - val_loss: 0.0131\n",
      "Epoch 41/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0125 - val_loss: 0.0145\n",
      "Epoch 42/200\n",
      "1000/1000 [==============================] - 330s 330ms/step - loss: 0.0134 - val_loss: 0.0134\n",
      "Epoch 43/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0126 - val_loss: 0.0117\n",
      "Epoch 44/200\n",
      "1000/1000 [==============================] - 330s 330ms/step - loss: 0.0122 - val_loss: 0.0117\n",
      "Epoch 45/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0121 - val_loss: 0.0093\n",
      "Epoch 46/200\n",
      "1000/1000 [==============================] - 332s 332ms/step - loss: 0.0120 - val_loss: 0.0099\n",
      "Epoch 47/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0118 - val_loss: 0.0118\n",
      "Epoch 48/200\n",
      "1000/1000 [==============================] - 336s 336ms/step - loss: 0.0112 - val_loss: 0.0145\n",
      "Epoch 49/200\n",
      "1000/1000 [==============================] - 333s 333ms/step - loss: 0.0134 - val_loss: 0.0146\n",
      "Epoch 50/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0129 - val_loss: 0.0110\n",
      "Epoch 51/200\n",
      "1000/1000 [==============================] - 329s 329ms/step - loss: 0.0119 - val_loss: 0.0147\n",
      "Epoch 52/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0108 - val_loss: 0.0152\n",
      "Epoch 53/200\n",
      "1000/1000 [==============================] - 357s 357ms/step - loss: 0.0137 - val_loss: 0.0138\n",
      "Epoch 54/200\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0111 - val_loss: 0.0095\n",
      "Epoch 55/200\n",
      "1000/1000 [==============================] - 332s 332ms/step - loss: 0.0112 - val_loss: 0.0168\n",
      "Epoch 56/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0119 - val_loss: 0.0178\n",
      "Epoch 57/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0129 - val_loss: 0.0117\n",
      "Epoch 58/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0140 - val_loss: 0.0115\n",
      "Epoch 59/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0125 - val_loss: 0.0108\n",
      "Epoch 60/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0118 - val_loss: 0.0134\n",
      "Epoch 61/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0117 - val_loss: 0.0089\n",
      "Epoch 62/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0108 - val_loss: 0.0093\n",
      "Epoch 63/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0141 - val_loss: 0.0121\n",
      "Epoch 64/200\n",
      "1000/1000 [==============================] - 329s 329ms/step - loss: 0.0122 - val_loss: 0.0137\n",
      "Epoch 65/200\n",
      "1000/1000 [==============================] - 331s 331ms/step - loss: 0.0139 - val_loss: 0.0141\n",
      "Epoch 66/200\n",
      "1000/1000 [==============================] - 329s 329ms/step - loss: 0.0118 - val_loss: 0.0100\n",
      "Epoch 67/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0120 - val_loss: 0.0118\n",
      "Epoch 68/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0107 - val_loss: 0.0092\n",
      "Epoch 69/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0094 - val_loss: 0.0097\n",
      "Epoch 70/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0110 - val_loss: 0.0095\n",
      "Epoch 71/200\n",
      "1000/1000 [==============================] - 331s 331ms/step - loss: 0.0105 - val_loss: 0.0088\n",
      "Epoch 72/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0102 - val_loss: 0.0100\n",
      "Epoch 73/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0103 - val_loss: 0.0082\n",
      "Epoch 74/200\n",
      "1000/1000 [==============================] - 323s 323ms/step - loss: 0.0103 - val_loss: 0.0082\n",
      "Epoch 75/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0104 - val_loss: 0.0080\n",
      "Epoch 76/200\n",
      "1000/1000 [==============================] - 324s 324ms/step - loss: 0.0095 - val_loss: 0.0068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 77/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0121 - val_loss: 0.0098\n",
      "Epoch 78/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0100 - val_loss: 0.0085\n",
      "Epoch 79/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0100 - val_loss: 0.0104\n",
      "Epoch 80/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0123 - val_loss: 0.0102\n",
      "Epoch 81/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0108 - val_loss: 0.0097\n",
      "Epoch 82/200\n",
      "1000/1000 [==============================] - 324s 324ms/step - loss: 0.0093 - val_loss: 0.0071\n",
      "Epoch 83/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0096 - val_loss: 0.0076\n",
      "Epoch 84/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0086 - val_loss: 0.0095\n",
      "Epoch 85/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0101 - val_loss: 0.0119\n",
      "Epoch 86/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0107 - val_loss: 0.0099\n",
      "Epoch 87/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0101 - val_loss: 0.0078\n",
      "Epoch 88/200\n",
      "1000/1000 [==============================] - 349s 349ms/step - loss: 0.0106 - val_loss: 0.0085\n",
      "Epoch 89/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0103 - val_loss: 0.0109\n",
      "Epoch 90/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0094 - val_loss: 0.0071\n",
      "Epoch 91/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0111 - val_loss: 0.0082\n",
      "Epoch 92/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0085 - val_loss: 0.0118\n",
      "Epoch 93/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0092 - val_loss: 0.0092\n",
      "Epoch 94/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0114 - val_loss: 0.0092\n",
      "Epoch 95/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0089 - val_loss: 0.0089\n",
      "Epoch 96/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0096 - val_loss: 0.0139\n",
      "Epoch 97/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0095 - val_loss: 0.0089\n",
      "Epoch 98/200\n",
      "1000/1000 [==============================] - 324s 324ms/step - loss: 0.0110 - val_loss: 0.0092\n",
      "Epoch 99/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0104 - val_loss: 0.0091\n",
      "Epoch 100/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0089 - val_loss: 0.0097\n",
      "Epoch 101/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0090 - val_loss: 0.0089\n",
      "Epoch 102/200\n",
      "1000/1000 [==============================] - 324s 324ms/step - loss: 0.0097 - val_loss: 0.0104\n",
      "Epoch 103/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0091 - val_loss: 0.0066\n",
      "Epoch 104/200\n",
      "1000/1000 [==============================] - 336s 336ms/step - loss: 0.0088 - val_loss: 0.0087\n",
      "Epoch 105/200\n",
      "1000/1000 [==============================] - 335s 335ms/step - loss: 0.0088 - val_loss: 0.0094\n",
      "Epoch 106/200\n",
      "1000/1000 [==============================] - 333s 333ms/step - loss: 0.0085 - val_loss: 0.0110\n",
      "Epoch 107/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0096 - val_loss: 0.0090\n",
      "Epoch 108/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0088 - val_loss: 0.0088\n",
      "Epoch 109/200\n",
      "1000/1000 [==============================] - 324s 324ms/step - loss: 0.0104 - val_loss: 0.0098\n",
      "Epoch 110/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0094 - val_loss: 0.0092\n",
      "Epoch 111/200\n",
      "1000/1000 [==============================] - 324s 324ms/step - loss: 0.0081 - val_loss: 0.0071\n",
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      "1000/1000 [==============================] - 323s 323ms/step - loss: 0.0094 - val_loss: 0.0087\n",
      "Epoch 113/200\n",
      "1000/1000 [==============================] - 326s 326ms/step - loss: 0.0089 - val_loss: 0.0106\n",
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      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0104 - val_loss: 0.0125\n",
      "Epoch 115/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0113 - val_loss: 0.0077\n",
      "Epoch 116/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0099 - val_loss: 0.0071\n",
      "Epoch 117/200\n",
      "1000/1000 [==============================] - 325s 325ms/step - loss: 0.0111 - val_loss: 0.0104\n",
      "Epoch 118/200\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0113 - val_loss: 0.0069\n",
      "Epoch 119/200\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0085 - val_loss: 0.0073\n",
      "Epoch 120/200\n",
      "1000/1000 [==============================] - 331s 331ms/step - loss: 0.0092 - val_loss: 0.0101\n",
      "Epoch 121/200\n",
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      "Epoch 122/200\n",
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      "Epoch 124/200\n",
      "1000/1000 [==============================] - 349s 349ms/step - loss: 0.0085 - val_loss: 0.0083\n",
      "Epoch 125/200\n",
      "1000/1000 [==============================] - 386s 386ms/step - loss: 0.0098 - val_loss: 0.0081\n",
      "Epoch 126/200\n",
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      "Epoch 127/200\n",
      "1000/1000 [==============================] - 376s 376ms/step - loss: 0.0095 - val_loss: 0.0107\n",
      "Epoch 128/200\n",
      "1000/1000 [==============================] - 374s 374ms/step - loss: 0.0097 - val_loss: 0.0110\n",
      "Epoch 129/200\n",
      "1000/1000 [==============================] - 396s 396ms/step - loss: 0.0080 - val_loss: 0.0081\n",
      "Epoch 130/200\n",
      "1000/1000 [==============================] - 389s 389ms/step - loss: 0.0076 - val_loss: 0.0093\n",
      "Epoch 131/200\n",
      "1000/1000 [==============================] - 417s 417ms/step - loss: 0.0082 - val_loss: 0.0121\n",
      "Epoch 132/200\n",
      "1000/1000 [==============================] - 436s 436ms/step - loss: 0.0081 - val_loss: 0.0079\n",
      "Epoch 133/200\n",
      "1000/1000 [==============================] - 448s 448ms/step - loss: 0.0116 - val_loss: 0.0092\n",
      "Epoch 134/200\n",
      "1000/1000 [==============================] - 412s 412ms/step - loss: 0.0083 - val_loss: 0.0074\n",
      "Epoch 135/200\n",
      "1000/1000 [==============================] - 439s 439ms/step - loss: 0.0089 - val_loss: 0.0102\n",
      "Epoch 136/200\n",
      "1000/1000 [==============================] - 265s 265ms/step - loss: 0.0082 - val_loss: 0.0087\n",
      "Epoch 137/200\n",
      "1000/1000 [==============================] - 222s 222ms/step - loss: 0.0076 - val_loss: 0.0088\n",
      "Epoch 138/200\n",
      "1000/1000 [==============================] - 236s 236ms/step - loss: 0.0088 - val_loss: 0.0069\n",
      "Epoch 139/200\n",
      "1000/1000 [==============================] - 219s 219ms/step - loss: 0.0078 - val_loss: 0.0076\n",
      "Epoch 140/200\n",
      "1000/1000 [==============================] - 212s 212ms/step - loss: 0.0104 - val_loss: 0.0080\n",
      "Epoch 141/200\n",
      "1000/1000 [==============================] - 203s 203ms/step - loss: 0.0083 - val_loss: 0.0087\n",
      "Epoch 142/200\n",
      "1000/1000 [==============================] - 189s 189ms/step - loss: 0.0105 - val_loss: 0.0109\n",
      "Epoch 143/200\n",
      "1000/1000 [==============================] - 217s 217ms/step - loss: 0.0093 - val_loss: 0.0066\n",
      "Epoch 144/200\n",
      "1000/1000 [==============================] - 274s 274ms/step - loss: 0.0093 - val_loss: 0.0085\n",
      "Epoch 145/200\n",
      "1000/1000 [==============================] - 280s 280ms/step - loss: 0.0085 - val_loss: 0.0069\n",
      "Epoch 146/200\n",
      "1000/1000 [==============================] - 254s 254ms/step - loss: 0.0074 - val_loss: 0.0075\n",
      "Epoch 147/200\n",
      "1000/1000 [==============================] - 268s 268ms/step - loss: 0.0086 - val_loss: 0.0139\n",
      "Epoch 148/200\n",
      "1000/1000 [==============================] - 273s 273ms/step - loss: 0.0078 - val_loss: 0.0109\n",
      "Epoch 149/200\n",
      "1000/1000 [==============================] - 274s 274ms/step - loss: 0.0084 - val_loss: 0.0079\n",
      "Epoch 150/200\n",
      "1000/1000 [==============================] - 284s 284ms/step - loss: 0.0086 - val_loss: 0.0075\n",
      "Epoch 151/200\n",
      "1000/1000 [==============================] - 280s 280ms/step - loss: 0.0089 - val_loss: 0.0062\n",
      "Epoch 152/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000/1000 [==============================] - 284s 284ms/step - loss: 0.0085 - val_loss: 0.0123\n",
      "Epoch 153/200\n",
      "1000/1000 [==============================] - 249s 249ms/step - loss: 0.0082 - val_loss: 0.0068\n",
      "Epoch 154/200\n",
      "1000/1000 [==============================] - 263s 263ms/step - loss: 0.0085 - val_loss: 0.0081\n",
      "Epoch 155/200\n",
      "1000/1000 [==============================] - 270s 270ms/step - loss: 0.0082 - val_loss: 0.0083\n",
      "Epoch 156/200\n",
      "1000/1000 [==============================] - 255s 255ms/step - loss: 0.0066 - val_loss: 0.0068\n",
      "Epoch 157/200\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.0070 - val_loss: 0.0060\n",
      "Epoch 158/200\n",
      "1000/1000 [==============================] - 273s 273ms/step - loss: 0.0065 - val_loss: 0.0132\n",
      "Epoch 159/200\n",
      "1000/1000 [==============================] - 255s 255ms/step - loss: 0.0069 - val_loss: 0.0054\n",
      "Epoch 160/200\n",
      "1000/1000 [==============================] - 270s 270ms/step - loss: 0.0074 - val_loss: 0.0068\n",
      "Epoch 161/200\n",
      "1000/1000 [==============================] - 257s 257ms/step - loss: 0.0069 - val_loss: 0.0086\n",
      "Epoch 162/200\n",
      "1000/1000 [==============================] - 264s 264ms/step - loss: 0.0076 - val_loss: 0.0062\n",
      "Epoch 163/200\n",
      "1000/1000 [==============================] - 260s 260ms/step - loss: 0.0076 - val_loss: 0.0072\n",
      "Epoch 164/200\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0078 - val_loss: 0.0089\n",
      "Epoch 165/200\n",
      "1000/1000 [==============================] - 247s 247ms/step - loss: 0.0080 - val_loss: 0.0064\n",
      "Epoch 166/200\n",
      "1000/1000 [==============================] - 272s 272ms/step - loss: 0.0069 - val_loss: 0.0081\n",
      "Epoch 167/200\n",
      "1000/1000 [==============================] - 271s 271ms/step - loss: 0.0069 - val_loss: 0.0067\n",
      "Epoch 168/200\n",
      "1000/1000 [==============================] - 269s 269ms/step - loss: 0.0071 - val_loss: 0.0056\n",
      "Epoch 169/200\n",
      "1000/1000 [==============================] - 271s 271ms/step - loss: 0.0073 - val_loss: 0.0048\n",
      "Epoch 170/200\n",
      "1000/1000 [==============================] - 259s 259ms/step - loss: 0.0071 - val_loss: 0.0080\n",
      "Epoch 171/200\n",
      "1000/1000 [==============================] - 274s 274ms/step - loss: 0.0083 - val_loss: 0.0073\n",
      "Epoch 172/200\n",
      "1000/1000 [==============================] - 276s 276ms/step - loss: 0.0068 - val_loss: 0.0064\n",
      "Epoch 173/200\n",
      "1000/1000 [==============================] - 265s 265ms/step - loss: 0.0069 - val_loss: 0.0068\n",
      "Epoch 174/200\n",
      "1000/1000 [==============================] - 262s 262ms/step - loss: 0.0064 - val_loss: 0.0076\n",
      "Epoch 175/200\n",
      "1000/1000 [==============================] - 271s 271ms/step - loss: 0.0083 - val_loss: 0.0070\n",
      "Epoch 176/200\n",
      "1000/1000 [==============================] - 278s 278ms/step - loss: 0.0080 - val_loss: 0.0113\n",
      "Epoch 177/200\n",
      "1000/1000 [==============================] - 275s 275ms/step - loss: 0.0067 - val_loss: 0.0098\n",
      "Epoch 178/200\n",
      "1000/1000 [==============================] - 230s 230ms/step - loss: 0.0064 - val_loss: 0.0053\n",
      "Epoch 179/200\n",
      "1000/1000 [==============================] - 182s 182ms/step - loss: 0.0063 - val_loss: 0.0063\n",
      "Epoch 180/200\n",
      "1000/1000 [==============================] - 175s 175ms/step - loss: 0.0066 - val_loss: 0.0067\n",
      "Epoch 181/200\n",
      "1000/1000 [==============================] - 182s 182ms/step - loss: 0.0068 - val_loss: 0.0082\n",
      "Epoch 182/200\n",
      "1000/1000 [==============================] - 172s 172ms/step - loss: 0.0078 - val_loss: 0.0068\n",
      "Epoch 183/200\n",
      "1000/1000 [==============================] - 188s 188ms/step - loss: 0.0074 - val_loss: 0.0063\n",
      "Epoch 184/200\n",
      "1000/1000 [==============================] - 182s 182ms/step - loss: 0.0066 - val_loss: 0.0084\n",
      "Epoch 185/200\n",
      "1000/1000 [==============================] - 172s 172ms/step - loss: 0.0066 - val_loss: 0.0067\n",
      "Epoch 186/200\n",
      "1000/1000 [==============================] - 193s 193ms/step - loss: 0.0066 - val_loss: 0.0054\n",
      "Epoch 187/200\n",
      "1000/1000 [==============================] - 179s 179ms/step - loss: 0.0083 - val_loss: 0.0054\n",
      "Epoch 188/200\n",
      "1000/1000 [==============================] - 194s 194ms/step - loss: 0.0071 - val_loss: 0.0089\n",
      "Epoch 189/200\n",
      "1000/1000 [==============================] - 194s 194ms/step - loss: 0.0064 - val_loss: 0.0085\n",
      "Epoch 190/200\n",
      "1000/1000 [==============================] - 180s 180ms/step - loss: 0.0072 - val_loss: 0.0077\n",
      "Epoch 191/200\n",
      "1000/1000 [==============================] - 203s 203ms/step - loss: 0.0077 - val_loss: 0.0059\n",
      "Epoch 192/200\n",
      "1000/1000 [==============================] - 185s 185ms/step - loss: 0.0066 - val_loss: 0.0064\n",
      "Epoch 193/200\n",
      "1000/1000 [==============================] - 163s 163ms/step - loss: 0.0063 - val_loss: 0.0056\n",
      "Epoch 194/200\n",
      "1000/1000 [==============================] - 200s 200ms/step - loss: 0.0072 - val_loss: 0.0068\n",
      "Epoch 195/200\n",
      "1000/1000 [==============================] - 227s 227ms/step - loss: 0.0056 - val_loss: 0.0079\n",
      "Epoch 196/200\n",
      "1000/1000 [==============================] - 183s 183ms/step - loss: 0.0074 - val_loss: 0.0054\n",
      "Epoch 197/200\n",
      "1000/1000 [==============================] - 166s 166ms/step - loss: 0.0062 - val_loss: 0.0061\n",
      "Epoch 198/200\n",
      "1000/1000 [==============================] - 171s 171ms/step - loss: 0.0068 - val_loss: 0.0060\n",
      "Epoch 199/200\n",
      "1000/1000 [==============================] - 190s 190ms/step - loss: 0.0081 - val_loss: 0.0073\n",
      "Epoch 200/200\n",
      "1000/1000 [==============================] - 165s 165ms/step - loss: 0.0080 - val_loss: 0.0068\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f7785a5bda0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 载入最好的模型继续训练一会\n",
    "from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n",
    "from tensorflow.keras.optimizers import *\n",
    "model.load_weights('gru_chinese524char_ctc_best.h5')\n",
    "train_data = CaptchaSequence(characters, batch_size=128, steps=1000) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=100)  # (characters, batch_size=128, steps=100)\n",
    "# callbacks = [EarlyStopping(patience=5),\n",
    "#              CSVLogger('ctc.csv', append=True), ModelCheckpoint('ctc_best.h5', save_best_only=True)]\n",
    "callbacks = [CSVLogger('ctc.csv', append=True), ModelCheckpoint('gru_chinese524char_ctc_best.h5', save_best_only=True)]\n",
    "\n",
    "model.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=Adam(1e-4, amsgrad=True))\n",
    "model.fit_generator(train_data, epochs=200, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "# 继续训练近200个epoch，模型gru_chinese524char_ctc_best.h5最终损失loss: 0.0361 - val_loss: 0.0320  四个字的验证码 调整生成验证码的颜色值\n",
    "#让背景色与与字体颜色有区分度，再跑200epoch从loss: 0.0361 - val_loss: 0.0320 降到loss: 0.0080 - val_loss: 0.0068"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.load_weights('ctc_best.h5')\n",
    "# base_model.save('gru_chinese_base_model.h5')\n",
    "base_model.save('gru_chinese524char_base_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred:得得第穿\n",
      "true:得第穿军\n",
      "pred:枪枪世代\n",
      "true:枪世代儿\n",
      "pred:前会准怕\n",
      "true:前会传怕\n",
      "总耗时： 25.031147241592407\n",
      "2557 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 171,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 测试模型\n",
    "# model.load_weights('gru_chinese524char_ctc_best.h5')\n",
    "characters2 = characters + ' '\n",
    "import time\n",
    "import re\n",
    "def get_test_data():\n",
    "    '''\n",
    "    从本地获取验证码图片并生成测试数据\n",
    "    '''    \n",
    "    X = []\n",
    "    Y = []\n",
    "    for path in glob.glob('/data/captcha/total_chinese/*.jpg'): #    Digit5/*.jpg  #Chinese/*.jpg  /data/captcha/total_chinese/*.jpg\n",
    "#         random_str = path.split('_')[-1][:-4]   # #Chinese/*.jpg\n",
    "        name = path.split('/')[-1].strip()[:-4]\n",
    "        random_str = label_dic.get(name, None)\n",
    "        if not random_str:\n",
    "            continue\n",
    "        if random_str.isalpha() and len(random_str) ==4:\n",
    "            random_str = random_str.upper()\n",
    "#             print(random_str)\n",
    "#         if random_str.isdigit() and len(random_str) ==4:\n",
    "            img = Image.open(path)\n",
    "            X.append(np.array(img.resize((120,40), Image.BILINEAR))/255.0)\n",
    "            label_idx = [characters.find(x) for x in random_str]\n",
    "            if len(random_str) < n_len:\n",
    "                label_idx += [n_class-1]*(n_len-len(random_str)) \n",
    "            Y.append(label_idx)\n",
    "    return [np.array(X), np.array(Y), np.ones(len(X)), np.ones(len(X))],np.ones(len(X))\n",
    "\n",
    "# data = [get_test_data()]\n",
    "\n",
    "data = CaptchaSequence(characters, batch_size=128, steps=20, chars_len=(4,4))\n",
    "\n",
    "pos = neg = 0\n",
    "t1 = time.time()\n",
    "\n",
    "neg_img = []\n",
    "neg_str = []\n",
    "for i in range(len(data)): \n",
    "    flag = False\n",
    "    [X_test, y_test, _, _], _  = data[i]\n",
    "    y_pred = base_model.predict(X_test)\n",
    "#     print(y_pred.shape)\n",
    "    out_pre = K.get_value(K.ctc_decode(y_pred, input_length=np.ones(y_pred.shape[0])*y_pred.shape[1])[0][0])[:, :4]\n",
    "#     print(out_pre.shape)\n",
    "#     print(out)\n",
    "    for j in range(out_pre.shape[0]):\n",
    "        out = ''.join([characters[x] for x in out_pre[j] if x < len(characters)])      \n",
    "        y_true = ''.join([characters[x] for x in y_test[j] if x < len(characters)])\n",
    "#         out = ''.join([characters2[x] for x in out_pre[j] if x < len(characters)])  \n",
    "#         if re.sub(' ','',out) != y_true:\n",
    "        if out != y_true:\n",
    "            neg_img.append(X_test[j])\n",
    "            neg_str.append('pred:' + str(out) + '\\ttrue:' + str(y_true))\n",
    "            plt.imshow(X_test[j])\n",
    "            plt.title('pred:' + str(out) + '\\ntrue: ' + str(y_true))\n",
    "            print('pred:' + str(out) + '\\ntrue:' + str(y_true))\n",
    "            neg += 1\n",
    "            flag = True\n",
    "#             break\n",
    "#             time.sleep(1)\n",
    "#             argmax = np.argmax(y_pred, axis=2)[j]\n",
    "#             print(list(zip(argmax, ''.join([characters2[x] for x in argmax]))))\n",
    "        else:\n",
    "#             print('pred:' + str(out) + '\\ntrue: ' + str(y_true))\n",
    "            pos += 1 \n",
    "\n",
    "#     if flag:\n",
    "#         break\n",
    "t2 = time.time()\n",
    "print('总耗时：',t2-t1)\n",
    "print(pos,neg)\n",
    "# # plt.imshow(X_test[0])\n",
    "# # plt.title('pred:' + str(out) + '\\ntrue: ' + str(y_true))\n",
    "\n",
    "# argmax = np.argmax(y_pred, axis=2)[0]\n",
    "# list(zip(argmax, ''.join([characters2[x] for x in argmax])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pred:得得第穿\ttrue:得第穿军\n",
      "pred:枪枪世代\ttrue:枪世代儿\n",
      "pred:前会准怕\ttrue:前会传怕\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 114,
       "width": 860
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "f = plt.figure(figsize=(20,10))\n",
    "n = math.ceil(len(neg_img)/4)\n",
    "for i in range(len(neg_img)):\n",
    "    plt.subplot(n,4,i+1)\n",
    "    plt.imshow(neg_img[i])\n",
    "    print(neg_str[i])\n",
    "plt.show()\n",
    "\n",
    "# with open('/data/captcha/total_chinese/total_chinese.txt', encoding='utf-8') as f:\n",
    "#     line = f.readlines()\n",
    "#     print(len(line))\n",
    "# print(label_dic['f15d93fc-aa3d-11ea-8951-5254009c362b'])\n",
    "# print(len(label_dic))\n",
    "# 'f15d93fc-aa3d-11ea-8951-5254009c362b' in label_dic\n",
    "# line[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluate(base_model,batch_size=128, steps=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61db44d4e0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示损失下降图\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('ctc.csv')\n",
    "df[['loss', 'val_loss']].plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}