{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz\n"
     ]
    }
   ],
   "source": [
    "from captcha.image import ImageCaptcha\n",
    "from PIL import Image, ImageFont, ImageDraw\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "import glob\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",
    "characters = string.digits + string.ascii_uppercase + string.ascii_lowercase # 验证码字符集合数字+英文\n",
    "# characters = string.digits # 验证码字符集合\n",
    "print(characters)\n",
    "\n",
    "fonts = ['/usr/share/fonts/WindowsFonts/fonts/ANTQUAB.TTF',\n",
    "#          '/usr/share/fonts/WindowsFonts/fonts/AGENCYR.TTF',\n",
    "         '/usr/share/fonts/WindowsFonts/fonts/ANTQUABI.TTF',\n",
    "         '/usr/share/fonts/WindowsFonts/fonts/ARIALNI.TTF',\n",
    "         '/usr/share/fonts/WindowsFonts/fonts/Candara.ttf',\n",
    "         '/usr/share/fonts/WindowsFonts/fonts/cambriab.ttf',\n",
    "        '/usr/share/fonts/WindowsFonts/fonts/Candarai.ttf',\n",
    "         '/usr/share/fonts/WindowsFonts/fonts/calibri.ttf',\n",
    "        '/usr/share/fonts/WindowsFonts/fonts/constan.ttf',\n",
    "         '/usr/share/fonts/WindowsFonts/fonts/constanz.ttf',\n",
    "         '/usr/share/fonts/WindowsFonts/fonts/kaiu.ttf',\n",
    "         '/usr/share/fonts/WindowsFonts/fonts/simhei.ttf',\n",
    "#         '/usr/share/fonts/WindowsFonts/fonts/STHUPO.TTF', # 粗体不要\n",
    "#         '/usr/share/fonts/WindowsFonts/fonts/STKAITI.TTF',\n",
    "        '/usr/share/fonts/WindowsFonts/fonts/STZHONGS.TTF']\n",
    "width, height, n_len, n_class = 200, 70, 6, 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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "labels = []\n",
    "for path in glob.glob('/data/captcha/shensexiansandian/*.jpg')[-3000:]:\n",
    "    label = path.split('_')[0].split('/')[-1].lower()\n",
    "    if len(label) ==4:\n",
    "        labels.append(label)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "font: /usr/share/fonts/WindowsFonts/fonts/ARIALNI.TTF\n",
      "字体颜色 (75, 45, 204)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2327
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# base_model.summary()\n",
    "def get_wavy_line(w = (0, 100),h = (30, 50)):\n",
    "    '''产生波浪线坐标'''\n",
    "    import random\n",
    "    n = 50\n",
    "    x = 0\n",
    "    y = random.randint(h[0],h[1])\n",
    "    flag = random.randint(0,2)\n",
    "    xy = [(x, y)]\n",
    "    while x < w[1]:\n",
    "        temp_y = random.randint(1, 3)\n",
    "        temp_x = random.randint(5, 10)\n",
    "        if flag == 0:\n",
    "            if y + temp_y > h[1]:\n",
    "                y -= temp_y\n",
    "                flag = 1\n",
    "            else:\n",
    "                y += temp_y\n",
    "        else:\n",
    "            if y - temp_y < h[0]:\n",
    "                y += temp_y\n",
    "                flag = 0\n",
    "            else:\n",
    "                y -= temp_y\n",
    "        x = x+temp_x if x+temp_x < w[1] else w[1]\n",
    "        xy.append((x, y))\n",
    "    return xy\n",
    "\n",
    "def gen_captcha(text, fig_size=(200,70), fonts=['/usr/share/fonts/WindowsFonts/fonts/ANTQUAB.TTF'],font_color=[(0,0,0)],same_color=1, font_size=(25, 35),offset_hor=5,offset_ver=5, rotate=0,\n",
    "                font_noise=0, line=(0,5), line_width=(1,2), point=(0,500),wavy=(0,0), noise_color=[(200,200,255)], bg=[(255,255,255)]):\n",
    "    '''\n",
    "    text:验证码文本\n",
    "    size:验证码图片宽高\n",
    "    fonts:字体列表，随机选择一个\n",
    "    font_noise: 字体散点干扰，0不加干扰，1加干扰\n",
    "    fill:字体颜色范围\n",
    "    rotate:字体旋转角度\n",
    "    line:干扰线条数范围\n",
    "    point:干扰点数范围\n",
    "    wavy:波浪线数范围\n",
    "    color:干扰线、点 颜色\n",
    "    bg:背景色范围\n",
    "    '''\n",
    "    bg = random.choice(bg)\n",
    "    img = Image.new(mode='RGB', size=fig_size, color=bg) #\n",
    "    draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None\n",
    "    font_path = random.choice(fonts)\n",
    "    font = ImageFont.truetype(font_path, size=random.randint(font_size[0], font_size[1])) # font=None, size=10, index=0, encoding=\"\"\n",
    "    print('font:', font_path)\n",
    "    def get_char_img(char,font,font_color,rotate,bg, font_noise=0):\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('RGB',(w,h), color=bg)\n",
    "        ImageDraw.Draw(im).text((0,0), char, font=font, fill=font_color)   \n",
    "        if rotate:\n",
    "            im = im.rotate(random.randint(-rotate, rotate),Image.BILINEAR,expand=1)\n",
    "            im = im.crop(im.getbbox())\n",
    "        if font_noise: \n",
    "            im_draw = ImageDraw.Draw(im)\n",
    "#             for i in range(random.randint(10,100)):\n",
    "            for i in range(random.randint(int(w*h*0.01),int(w*h*0.05))):\n",
    "                im_draw.point(xy=(random.randint(0, w), random.randint(0, h)),fill=bg)\n",
    "        table = []\n",
    "        for i in range(256):\n",
    "            table.append(i * 97) # 5.97\n",
    "        mask = im.convert('L').point(table)         \n",
    "        return (im, mask)\n",
    "    \n",
    "    char_color = random.choice(font_color)\n",
    "    print('字体颜色',char_color)\n",
    "    if same_color:        \n",
    "        char_imgs = [get_char_img(char, font, font_color=char_color, rotate=rotate, bg=bg, font_noise=font_noise) for char in text]\n",
    "    else:\n",
    "        char_imgs = [get_char_img(char, font, font_color=random.choice(font_color), rotate=rotate, bg=bg, font_noise=font_noise) for char in text]       \n",
    "    ws = [img[0].size[0] for img in char_imgs]\n",
    "    hs = [img[0].size[1] for img in char_imgs]\n",
    "    w = max(sum(ws), fig_size[0])\n",
    "    h = max(max(hs), fig_size[1])\n",
    "    if w>fig_size[0] or h>fig_size[1]:\n",
    "        img = img.resize((w+6, h+6), Image.BILINEAR)\n",
    "        draw = ImageDraw.Draw(im=img, mode='RGB')  # im, mode=None\n",
    "        fig_size = img.size\n",
    "\n",
    "    if rotate:\n",
    "        temp_x = random.randint(int((fig_size[0]-sum(ws))/5), int((fig_size[0]-sum(ws))/2+1))\n",
    "        temp_y = random.randint(int((fig_size[1]-hs[0])/8), int((fig_size[1]-hs[0])/2+1))\n",
    "        for i in range(len(char_imgs)):\n",
    "            img.paste(char_imgs[i][0], box=(temp_x, temp_y), mask=char_imgs[i][1]) \n",
    "            new_x = temp_x+ws[i]+random.randint(0, offset_hor)\n",
    "            temp_x = new_x if new_x<fig_size[0] else temp_x+ws[i]+random.randint(-offset_hor,0)\n",
    "            new_y = temp_y+random.randint(-offset_ver, offset_ver)\n",
    "            temp_y = new_y if 0<new_y and new_y+hs[i]<fig_size[1] else temp_y     \n",
    "        \n",
    "    # 直线\n",
    "    for i in range(random.randint(line[0], line[1])):\n",
    "        x0 = random.randint(0, fig_size[0])\n",
    "        x1 = random.randint(0, fig_size[0])\n",
    "        y0 = random.randint(0, fig_size[1])\n",
    "        y1 = random.randint(0, fig_size[1])\n",
    "        draw.line(xy=((x0,y0),(x1,y1)),\n",
    "                  fill=random.choice(noise_color),\n",
    "                  width=random.choice(line_width)) # xy, fill=None, width=0\n",
    "    # 散点\n",
    "    for i in range(random.randint(point[0], point[1])):\n",
    "        draw.point(xy=(random.randint(0, fig_size[0]), random.randint(0, fig_size[1])),\n",
    "                   fill=random.choice(noise_color))\n",
    "    # 波浪线\n",
    "    for _ in range(random.randint(wavy[0],wavy[1])):  \n",
    "        draw.line(xy=get_wavy_line(w = (0, 200),h = (min(hs)-5, max(hs)+5)), \n",
    "                  fill=char_color, width=random.choice(line_width))\n",
    "        \n",
    "#     for char in text:\n",
    "#         w, h = draw.textsize(char, font=font)\n",
    "#         draw.text()\n",
    "#         text((0,0), char, font=font, fill=font_color)\n",
    "        \n",
    "    if not rotate:\n",
    "        temp_x = random.randint(int((fig_size[0]-sum(ws))/5), int((fig_size[0]-sum(ws))/2+1))\n",
    "        temp_y = random.randint(int((fig_size[1]-hs[0])/8), int((fig_size[1]-hs[0])/2+1))\n",
    "        for i in range(len(char_imgs)):\n",
    "            draw.text((temp_x, temp_y), text[i], font=font, fill=char_color)\n",
    "            new_x = temp_x+ws[i]+random.randint(0, offset_hor)\n",
    "            temp_x = new_x if new_x<fig_size[0] else temp_x+ws[i]+random.randint(-offset_hor,0)\n",
    "            new_y = temp_y+random.randint(-offset_ver, offset_ver)\n",
    "            temp_y = new_y if 0<new_y and new_y+hs[i]<fig_size[1] else temp_y     \n",
    "        \n",
    "    return img.resize((200,70), Image.BILINEAR)\n",
    "                   \n",
    "\n",
    "# img2 = Image.open('FileInfo0508/31c1f481-912a-11ea-b24d-408d5cd36814_cmftq.jpg') # 波浪线验证码\n",
    "# font_color=[(random.randint(210,255),random.randint(0,40),0),\n",
    "#             (0,random.randint(200,255),random.randint(0,90)),\n",
    "#            (random.randint(30,45),random.randint(90,110),255),\n",
    "#            (255,random.randint(210,255),random.randint(0,10)),\n",
    "#            (0,random.randint(0,5),random.randint(0,5))]\n",
    "# img = gen_captcha('cmftq', fig_size=(200,70), fonts=fonts,font_color=font_color,\n",
    "#                 same_color=1, font_size=(35, 45),offset_hor=8,offset_ver=5, rotate=5,\n",
    "#                 font_noise=1, line=(0,0), line_width=(1,2), point=(0,0),wavy=(1,1),\n",
    "#                 noise_color=[(200,200,255)], bg=[(255,255,255)])\n",
    "\n",
    "# img2 = Image.open('/data/captcha/shensebeijingsandian/pgv4_d58a8328-c425-11ea-be07-ecf4bbc56acd.jpg') # 深色背景验证码\n",
    "# font_color = [(random.randint(200,255),random.randint(0,40),random.randint(30,100)),\n",
    "#            (random.randint(150,200),random.randint(150,200),random.randint(150,200)),\n",
    "#            (random.randint(150,200),random.randint(150,250),random.randint(50,100))]\n",
    "# noise_color = [(random.randint(50,100),random.randint(150,250),random.randint(50,100)),\n",
    "#               (random.randint(200,250),random.randint(200,250),random.randint(50,100))]\n",
    "\n",
    "# img = gen_captcha('mtbd', fig_size=(100,25), fonts=fonts,font_color=font_color,same_color=1,\n",
    "#                   font_size=(15, 20),offset_hor=8,offset_ver=5, rotate=10,\n",
    "#                 font_noise=0, line=(0,0), line_width=(1,2), point=(30,100),wavy=(0,0), \n",
    "#                   noise_color=noise_color, bg=[(random.randint(20,100),random.randint(10,100),255)])\n",
    "\n",
    "\n",
    "\n",
    "img2 = Image.open('/data/captcha/shensexiansandian/r4y6_f7bcd30f3c913228ba2404e83aea0806.jpg') #深色线斜体字\n",
    "font_color = [(random.randint(50,100),random.randint(10,50),random.randint(100,250)),\n",
    "             (random.randint(0,50),random.randint(0,50),random.randint(120,250))]\n",
    "noise_color = [(random.randint(200,225),random.randint(0,50),random.randint(20,100)),\n",
    "              (random.randint(50,120),random.randint(150,250),random.randint(50,80))]\n",
    "img = gen_captcha('R1Y6', fig_size=(135,40), fonts=fonts,font_color=font_color,same_color=0, font_size=(20, 28),offset_hor=8,offset_ver=5, rotate=20,\n",
    "                font_noise=0, line=(2,8), line_width=(1,2,1,3,4), point=(100,300),wavy=(0,0), \n",
    "                  noise_color=noise_color, bg=[(random.randint(240,255),random.randint(240,255),random.randint(240,255))])\n",
    "\n",
    "# img2 = Image.open('/data/captcha/0ad9.jpg').resize((200,70), Image.BILINEAR) #小图噪点 \n",
    "# font_color=[(random.randint(210,255),random.randint(0,40),0),\n",
    "#             (0,random.randint(200,255),random.randint(0,90)),\n",
    "#            (random.randint(30,45),random.randint(90,110),255),\n",
    "#            (255,random.randint(210,255),random.randint(0,10)),\n",
    "#            (0,random.randint(0,5),random.randint(0,5))]\n",
    "# noise_color=[(random.randint(210,255),random.randint(0,40),0),\n",
    "#             (0,random.randint(200,255),random.randint(0,90)),\n",
    "#            (random.randint(30,45),random.randint(90,110),255),\n",
    "#            (255,random.randint(210,255),random.randint(0,10)),\n",
    "#            (0,random.randint(0,5),random.randint(0,5))]\n",
    "# img = gen_captcha('0ade9', fig_size=(52,21), fonts=fonts,font_color=font_color,same_color=1, font_size=(15, 18),offset_hor=1,offset_ver=0, rotate=0,\n",
    "#                 font_noise=1, line=(0,0), line_width=(1,2,1), point=(20,100),wavy=(0,0), \n",
    "#                   noise_color=noise_color, bg=[(random.randint(220,255),random.randint(220,255),random.randint(220,255))])\n",
    "\n",
    "# img2 = Image.open('/data/captcha/rph2.jpg').resize((200,70), Image.BILINEAR) #小图多种颜色字、干扰线 \n",
    "# font_color=[(random.randint(60,90),random.randint(60,90),random.randint(60,90)),\n",
    "#             (random.randint(90,120),random.randint(90,120),random.randint(90,120)),\n",
    "#            (random.randint(120,150),random.randint(120,150),random.randint(120,150))]\n",
    "# noise_color=[(random.randint(60,80),random.randint(60,90),random.randint(60,80)),\n",
    "#             (random.randint(90,110),random.randint(90,110),random.randint(90,120)),\n",
    "#            (random.randint(120,140),random.randint(120,140),random.randint(120,140))]\n",
    "# img = gen_captcha('RPH2', fig_size=(70,26), fonts=fonts,font_color=font_color,same_color=0, font_size=(20, 24),offset_hor=1,offset_ver=1, rotate=0,\n",
    "#                 font_noise=1, line=(4,8), line_width=(1,1), point=(0,0),wavy=(0,0), \n",
    "#                   noise_color=noise_color, bg=[(random.randint(220,255),random.randint(220,255),random.randint(220,255))])\n",
    "\n",
    "im = [img, img2]\n",
    "plt.figure(figsize=(50,10))\n",
    "for i in range(1,3):    \n",
    "    \n",
    "    plt.subplot(2,2,i)\n",
    "    plt.imshow(im[i-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_7 (InputLayer)         (None, 70, 200, 3)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_46 (Conv2D)           (None, 70, 200, 32)       896       \n",
      "_________________________________________________________________\n",
      "batch_normalization_46 (Batc (None, 70, 200, 32)       128       \n",
      "_________________________________________________________________\n",
      "activation_46 (Activation)   (None, 70, 200, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_47 (Conv2D)           (None, 70, 200, 32)       9248      \n",
      "_________________________________________________________________\n",
      "batch_normalization_47 (Batc (None, 70, 200, 32)       128       \n",
      "_________________________________________________________________\n",
      "activation_47 (Activation)   (None, 70, 200, 32)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_23 (MaxPooling (None, 35, 100, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_48 (Conv2D)           (None, 35, 100, 64)       18496     \n",
      "_________________________________________________________________\n",
      "batch_normalization_48 (Batc (None, 35, 100, 64)       256       \n",
      "_________________________________________________________________\n",
      "activation_48 (Activation)   (None, 35, 100, 64)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_49 (Conv2D)           (None, 35, 100, 64)       36928     \n",
      "_________________________________________________________________\n",
      "batch_normalization_49 (Batc (None, 35, 100, 64)       256       \n",
      "_________________________________________________________________\n",
      "activation_49 (Activation)   (None, 35, 100, 64)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_24 (MaxPooling (None, 17, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_50 (Conv2D)           (None, 17, 50, 128)       73856     \n",
      "_________________________________________________________________\n",
      "batch_normalization_50 (Batc (None, 17, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_50 (Activation)   (None, 17, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_51 (Conv2D)           (None, 17, 50, 128)       147584    \n",
      "_________________________________________________________________\n",
      "batch_normalization_51 (Batc (None, 17, 50, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_51 (Activation)   (None, 17, 50, 128)       0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_25 (MaxPooling (None, 8, 25, 128)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_52 (Conv2D)           (None, 8, 25, 256)        295168    \n",
      "_________________________________________________________________\n",
      "batch_normalization_52 (Batc (None, 8, 25, 256)        1024      \n",
      "_________________________________________________________________\n",
      "activation_52 (Activation)   (None, 8, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_53 (Conv2D)           (None, 8, 25, 256)        590080    \n",
      "_________________________________________________________________\n",
      "batch_normalization_53 (Batc (None, 8, 25, 256)        1024      \n",
      "_________________________________________________________________\n",
      "activation_53 (Activation)   (None, 8, 25, 256)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_26 (MaxPooling (None, 4, 12, 256)        0         \n",
      "_________________________________________________________________\n",
      "permute_6 (Permute)          (None, 12, 4, 256)        0         \n",
      "_________________________________________________________________\n",
      "time_distributed_6 (TimeDist (None, 12, 1024)          0         \n",
      "_________________________________________________________________\n",
      "bidirectional_12 (Bidirectio (None, 12, 256)           885504    \n",
      "_________________________________________________________________\n",
      "bidirectional_13 (Bidirectio (None, 12, 256)           295680    \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 12, 63)            16191     \n",
      "=================================================================\n",
      "Total params: 2,373,471\n",
      "Trainable params: 2,371,551\n",
      "Non-trainable params: 1,920\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "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, 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",
    "# def cnn_layer(index,inputs, filters, kernel_size, strides):\n",
    "#     x = Conv2D(filters, kernel_size=kernel_size, strides=strides[0], padding='same',name='cnn{}'.format(index + 1))(inputs)\n",
    "#     x = BatchNormalization(name='bn{}'.format(index + 1))(x)\n",
    "#     x = LeakyReLU(0.01)(x)\n",
    "#     x = MaxPooling2D(pool_size=(2,2), strides=strides[1], padding='same',name='pool{}'.format(index + 1))(x)\n",
    "#     return x\n",
    "\n",
    "# x = cnn_layer(0,inputs=x, kernel_size=7, filters=32, strides=(1, 1))\n",
    "# x = cnn_layer(1,inputs=x, kernel_size=5, filters=64, strides=(1, 2))\n",
    "# x = cnn_layer(2,inputs=x, kernel_size=3, filters=128, strides=(1, 2))\n",
    "# x = cnn_layer(3,inputs=x, kernel_size=3, filters=128, strides=(1, 2))\n",
    "# x = cnn_layer(4,inputs=x, kernel_size=3, filters=64, strides=(1, 2))\n",
    "\n",
    "x = Permute((2, 1, 3))(x)\n",
    "x = TimeDistributed(Flatten())(x)\n",
    "rnn_size = 128 # 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)\n",
    "# base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 10,
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 200, 3)\n",
      "RGB\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 210,
       "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 get_wavy_line(w = (0, 100),h = (30, 50)):\n",
    "    '''产生波浪线坐标'''\n",
    "    import random\n",
    "    n = 50\n",
    "    x = 0\n",
    "    y = random.randint(h[0],h[1])\n",
    "    flag = random.randint(0,2)\n",
    "    xy = [(x, y)]\n",
    "    while x < w[1]:\n",
    "        temp_y = random.randint(1, 3)\n",
    "        temp_x = random.randint(5, 10)\n",
    "        if flag == 0:\n",
    "            if y + temp_y > h[1]:\n",
    "                y -= temp_y\n",
    "                flag = 1\n",
    "            else:\n",
    "                y += temp_y\n",
    "        else:\n",
    "            if y - temp_y < h[0]:\n",
    "                y += temp_y\n",
    "                flag = 0\n",
    "            else:\n",
    "                y -= temp_y\n",
    "        x = x+temp_x if x+temp_x < w[1] else w[1]\n",
    "        xy.append((x, y))\n",
    "    return xy\n",
    "\n",
    "def get_wavy_text(n = 5,h = (25, 40)):\n",
    "    import random\n",
    "    y = random.randint(h[0],h[1])\n",
    "    flag = random.randint(0,2)\n",
    "    t_h = []\n",
    "    for i in range(n):\n",
    "        temp_y = random.randint(1, 5)\n",
    "        temp_x = random.randint(20, 38)\n",
    "        if flag == 0:\n",
    "            if y + temp_y > h[1]:\n",
    "                y -= temp_y\n",
    "                flag = 1\n",
    "            else:\n",
    "                y += temp_y\n",
    "        else:\n",
    "            if y - temp_y < h[0]:\n",
    "                y += temp_y\n",
    "                flag = 0\n",
    "            else:\n",
    "                y -= temp_y\n",
    "        t_h.append(y)\n",
    "    return t_h\n",
    "\n",
    "def generate_image(chars, background = random_color(255,255), width=200, height=70,char_color = [(213,0,0),(255,214,0),(41,98,255),(0,200,83),(0,0,0)]):\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",
    "#         w, h = ImageDraw.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 = random.randint(10,30)\n",
    "    im_list = []\n",
    "    w_list = []\n",
    "    fonts = ['/usr/share/fonts/WindowsFonts/fonts/ANTQUAB.TTF','/usr/share/fonts/WindowsFonts/fonts/cambriab.ttf',\n",
    "            '/usr/share/fonts/WindowsFonts/fonts/Candarai.ttf','/usr/share/fonts/WindowsFonts/fonts/calibri.ttf']\n",
    "    font = ImageFont.truetype(font=random.choice(fonts) , size=random.randint(33,35))\n",
    "\n",
    "#     f = '/usr/share/fonts/WindowsFonts/fonts/calibri.ttf' # font=random.choice(fonts)\n",
    "#     font = ImageFont.truetype(font=f , size=random.randint(33,35))\n",
    "    \n",
    "    ch_color = random.choice(char_color+[random_color(0,255)])\n",
    "    max_h = height\n",
    "    for c in chars:\n",
    "#         fonts = ['/usr/share/fonts/WindowsFonts/fonts/ariali.ttf','/usr/share/fonts/WindowsFonts/fonts/arial.ttf',\n",
    "#                 '/usr/share/fonts/WindowsFonts/fonts/cambriab.ttf','/usr/share/fonts/WindowsFonts/fonts/verdana.ttf']\n",
    "#         font = ImageFont.truetype(font=random.choice(fonts), size=random.randint(18,25))\n",
    "        char_img = get_char_img(char=c, font=font, color=ch_color, angle=random.randint(-10,10))\n",
    "        w, h = char_img.size\n",
    "        max_h = h if h > max_h else max_h\n",
    "        w_all += random.randint(-4,1)  \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",
    "    w_all = width if w_all < width else w_all\n",
    "#     if w_all > width or max_h>height:\n",
    "    image = Image.new('RGB', (w_all, max_h), color=background)\n",
    "    draw = ImageDraw.Draw(image)\n",
    "    \n",
    "    temp_h = 0\n",
    "    t_h = get_wavy_text(n = 6,h = (10, 30))\n",
    "    for i in range(len(w_list)):\n",
    "#         temp = random.randint(2,5)\n",
    "#         h_img = temp+temp_h if temp+temp_h <30 else temp_h-temp# height-im_list[i].size[1]) if height-im_list[i].size[1]>0 else 0\n",
    "# #         print('h_img', h_img)\n",
    "#         temp_h = h_img\n",
    "        image.paste(im_list[i], (w_list[i], t_h[i]))\n",
    "        \n",
    "    for _ in range(random.randint(1, 1)):\n",
    "        x2 = random.randint(30,50)\n",
    "        x3 = x2 + random.randint(40,80)\n",
    "        y = random.randint(30,50)\n",
    "        draw.line(xy=get_wavy_line(w = (0, 200),h = (30, 50)), fill=ch_color, width=random.randint(1,3))\n",
    "#         draw.line(xy=(((0,y),(x2,random.randint(30,55)),(x3,random.randint(30,55)),(width,random.randint(30,55)))),fill=ch_color,width=3)\n",
    "#     for _ in range(random.randint(500,1050)):\n",
    "#         draw.point(xy=(random_xy(width,height)),fill=random_color(255, 255))  \n",
    "    for _ in range(random.randint(0,650)):\n",
    "        draw.point(xy=(random_xy(width,height)),fill=random_color(10, 255))         \n",
    "#     for _ in range(random.randint(0, 10)):\n",
    "#         draw.line(xy=(random_xy(width, height),random_xy(width, height)), fill=random_color(20, 250), width=random.randint(1,2))        \n",
    "    return image.resize((200,70), Image.NEAREST)        \n",
    "\n",
    "\n",
    "img = generate_image('cmftq', width=200, height=70)\n",
    "img2 = Image.open('FileInfo0508/31c1f481-912a-11ea-b24d-408d5cd36814_cmftq.jpg')\n",
    "# img2 = Image.open('/data/esa_sdk/gan/english/7cff9614-fbc3-11e9-9bc7-408d5cd36814_73zr.jpg')\n",
    "img2 = img2.convert('RGB')\n",
    "print(np.array(img).shape)\n",
    "print(img2.mode)\n",
    "# img2 = Image.open('/data/esa_sdk/gan/english/7cff9614-fbc3-11e9-9bc7-408d5cd36814_73zr.jpg') # \n",
    "\n",
    "img2 = img2.resize((200,70), Image.NEAREST) # 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(img2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 200, 3)\n",
      "RGB\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 2263
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gen_captcha(text, size=(200,70), fonts=['/usr/share/fonts/WindowsFonts/fonts/calibri.ttf'],fill=(0,255), rotate=(-30,30),\n",
    "                line=(0,10), point=(0,500), color=(0,255), bg=(255,255,255)):\n",
    "    img = Image.new(mode='RGB', size=size, color=bg) #\n",
    "    draw = ImageDraw.Draw(im=img, mode='RGB') # im, mode=None\n",
    "    font = ImageFont.truetype(random.choice(fonts), size=random.randint(30, 45)) # font=None, size=10, index=0, encoding=\"\"\n",
    "    def get_char_img(char,font,color,angle,bg):\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",
    "#         w, h = ImageDraw.Draw.textsize(char, font=font)\n",
    "        im = Image.new('RGBA',(w,h), color=bg)\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,color=bg)\n",
    "        im = Image.composite(rot, bg, rot)\n",
    "        return im\n",
    "    #draw.text(xy=(20,30),\n",
    "              #text=text,\n",
    "              #fill=tuple([random.randint(fill[0], fill[1]) for _ in range(3)]),\n",
    "              #font=font) #xy, text, fill=None, font=None, anchor=None\n",
    "    char_color = tuple([random.randint(fill[0],fill[1]) for _ in range(3)])\n",
    "    char_imgs = [get_char_img(char, font, color=char_color, angle=random.randint(rotate[0], rotate[1]), bg=bg) for char in text]\n",
    "    ws = [img.size[0] for img in char_imgs]\n",
    "    hs = [img.size[1] for img in char_imgs]\n",
    "    w = max(sum(ws), size[0])\n",
    "    h = max(max(hs), size[1])\n",
    "    if w>size[0] or h>size[1]:\n",
    "        img = img.resize((w, h), Image.BILINEAR)\n",
    "        draw = ImageDraw.Draw(im=img, mode='RGB')  # im, mode=None\n",
    "        size = img.size\n",
    "\n",
    "#     assert sum(ws) < size[0]\n",
    "#     assert max(hs) < size[1]\n",
    "    temp_x = random.randint(int((size[0]-sum(ws))/5)-1, int((size[0]-sum(ws))/2))\n",
    "    for i in range(len(char_imgs)):\n",
    "        img.paste(char_imgs[i], box=(temp_x, random.randint(int((size[1]-hs[i])/4-1), int((size[1]-hs[i])/2)))) #im, box=None, mask=None\n",
    "        temp_x += random.randint(int(ws[i]*0.8)-1, int(ws[i]*0.9))\n",
    "#     import copy    \n",
    "#     img2 = copy.deepcopy(img)\n",
    "#     draw = ImageDraw.Draw(im=img2, mode='RGB')  # im, mode=None    \n",
    "    \n",
    "    for i in range(random.randint(line[0], line[1])):\n",
    "        draw.line(xy=([(random.randint(0, size[0]), random.randint(0, size[1])) for _ in range(2)]),\n",
    "                  fill=tuple([random.randint(color[0], color[1]) for _ in range(3)]),\n",
    "                  width=random.randint(0,2)) # xy, fill=None, width=0\n",
    "    for i in range(random.randint(point[0], point[1])):\n",
    "        draw.point(xy=(random.randint(0, size[0]), random.randint(0, size[1])),\n",
    "                   fill=tuple([random.randint(color[0], color[1]) for _ in range(3)])) # xy, fill=None\n",
    "    for _ in range(random.randint(0, 2)):\n",
    "        draw.line(xy=get_wavy_line(w = (0, 200),h = (40, 60)), \n",
    "                  fill=tuple([random.randint(color[0], color[1]) for _ in range(3)]), width=random.randint(1,3))\n",
    "    return img.resize((200,70), Image.BILINEAR)\n",
    "#     return img.resize((200,70), Image.BILINEAR), img2.resize((200,70), Image.BILINEAR)\n",
    "\n",
    "fonts_list = glob.glob('/usr/share/fonts/WindowsFonts/fonts/*.ttf')\n",
    "img = gen_captcha('GJBIL', fonts=fonts_list, fill=(0,255), rotate=(-20,20),\n",
    "            line=(0,20), point=(0,500), color=(0,255), bg=tuple([random.randint(0,255) for _ in range(3)]))\n",
    "img2 = Image.open('FileInfo0508/31c1f481-912a-11ea-b24d-408d5cd36814_cmftq.jpg')\n",
    "# img2 = Image.open('/data/esa_sdk/gan/english/7cff9614-fbc3-11e9-9bc7-408d5cd36814_73zr.jpg')\n",
    "img2 = img2.convert('RGB')\n",
    "print(np.array(img).shape)\n",
    "print(img2.mode)\n",
    "# img2 = Image.open('/data/esa_sdk/gan/english/7cff9614-fbc3-11e9-9bc7-408d5cd36814_73zr.jpg') # \n",
    "img2 = img2.resize((200,70), Image.BILINEAR) # Image.BILINEAR\n",
    "\n",
    "# img, img2 = gen_captcha('GJBIL', fonts=fonts_list, fill=(0,255), rotate=(-20,20),\n",
    "#             line=(0,20), point=(0,500), color=(0,255), bg=tuple([random.randint(0,255) for _ in range(3)]))\n",
    "\n",
    "im = [img, img2]\n",
    "plt.figure(figsize=(50,10))\n",
    "for i in range(1,3):    \n",
    "    plt.subplot(2,2,i)\n",
    "    plt.imshow(im[i-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9TCL'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重组验证码\n",
    "def rebuild_img(path):\n",
    "    '''\n",
    "    读取本地验证码图片进行随机加点噪声为新图片\n",
    "    参数：path：图片路径\n",
    "    返回：重组后图片    \n",
    "    '''\n",
    "    if re.search('FileInfo0508', path)!=None:\n",
    "        label = path.split('_')[-1][:-4].lower().replace('1','l')\n",
    "#         print(re.search('FileInfo0508', path))\n",
    "    else:\n",
    "        label = path.split('_')[-1][:-4].upper()\n",
    "#         print('label',label)\n",
    "    crop_n = len(label) \n",
    "    img = Image.open(path)\n",
    "    img = img.convert('RGB')\n",
    "    width, height = img.size\n",
    "#     img2 = img2.resize((100,50), Image.BILINEAR)\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    for _ in range(random.randint(0,450)):\n",
    "        draw.point(xy=(random_xy(width,height)),fill=random_color(25, 255))  \n",
    "#     for _ in range(random.randint(0, 3)):\n",
    "#         draw.line(xy=(random_xy(width, height),random_xy(width, height)), fill=random_color(20, 250), width=random.randint(1,2))\n",
    "    return img.resize((200,70), Image.NEAREST), label\n",
    "\n",
    "import re\n",
    "real_imgs = []\n",
    "paths = 'FileInfo0508_2/*.jpg'\n",
    "# paths = '/data/esa_sdk/gan/english/*.jpg'\n",
    "for path in glob.glob(paths)[:2024]:\n",
    "    label = path.split('_')[-1][:-4].lower().replace('1','l')\n",
    "    if len(label) ==5 and re.search('[0-9]', label)==None:\n",
    "        real_imgs.append(path)\n",
    "random.shuffle(real_imgs)\n",
    "img, l = rebuild_img(glob.glob('/data/esa_sdk/gan/english/*.jpg')[0])\n",
    "l\n",
    "# plt.imshow(img)\n",
    "# l2 = [rebuild_img(real_imgs[i])[1] for i in range(200,300)]\n",
    "# [characters.find(x) for x in l2[6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2024"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "# real_imgs = []\n",
    "paths = 'FileInfo0508/*.jpg'\n",
    "# paths = '/data/esa_sdk/gan/english/*.jpg'\n",
    "# for path in glob.glob(paths)[:1024]:\n",
    "#     label = path.split('_')[-1][:-4].lower()\n",
    "#     if re.search('FileInfo0508', paths)!=None:\n",
    "#         print(paths)\n",
    "#     if len(label) ==4 and re.search('[0-9]', label)==None:\n",
    "#         real_imgs.append(path)\n",
    "# img, l = rebuild_img(real_imgs[0])\n",
    "# plt.imshow(img)\n",
    "len(real_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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=6, width=200, height=70, \n",
    "                 input_length=13, label_length=6, chars_len=(5, 5)): #  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",
    "#         self.fonts_list = glob.glob('/usr/share/fonts/WindowsFonts/fonts/*.ttf')\n",
    "        self.fonts_list = ['/usr/share/fonts/WindowsFonts/fonts/arial.ttf','/usr/share/fonts/WindowsFonts/fonts/ANTQUAB.TTF',\n",
    "                            '/usr/share/fonts/WindowsFonts/fonts/BKANT.TTF','/usr/share/fonts/WindowsFonts/fonts/cambriab.ttf',\n",
    "                            '/usr/share/fonts/WindowsFonts/fonts/comic.ttf','/usr/share/fonts/WindowsFonts/fonts/GOTHIC.TTF']\n",
    "            \n",
    "    def __len__(self):\n",
    "        return self.steps\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        batch_label_length = random.choice([5])\n",
    "#         print('batch_label_length',batch_label_length)\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.uint8)\n",
    "#         y = np.zeros((self.batch_size, batch_label_length), dtype=np.uint8)\n",
    "        input_length = np.ones(self.batch_size)*self.input_length\n",
    "        label_length = np.ones(self.batch_size)*batch_label_length \n",
    "#         print('id(x):',id(x))\n",
    "#         print('id(batch_label_length)', id(batch_label_length))\n",
    "        if batch_label_length == 4:  \n",
    "#             print('batch_label_length == 4', batch_label_length == 4)\n",
    "            for i in range(self.batch_size):\n",
    "#                 print('len 4',y.shape, i)\n",
    "                # 定义验证码字符集 (大写字母、小写字母、大写字母+数字)\n",
    "                gen_characters = random.choice([string.ascii_lowercase, string.ascii_uppercase, string.digits + string.ascii_uppercase])  \n",
    "                if i % 4 == 0: \n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    image = generate_image(random_str, background =random_color(255,255), width=200, height=70)\n",
    "                elif i % 4 ==1:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fonts=self.fonts_list, fill=(0,250), rotate=(-10,10), line=(0,2), point=(0,500), \n",
    "                                      color=(0,255), bg=tuple([random.randint(240,255) for _ in range(3)]))\n",
    "                elif i % 6 < 5:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fonts=self.fonts_list, fill=(0,250), rotate=(-20,20), line=(0,10), point=(0,500), \n",
    "                                      color=(0,255), bg=tuple([random.randint(230,255) for _ in range(3)]))                    \n",
    "                else:\n",
    "                    image, random_str = rebuild_img(random.choice(glob.glob('/data/esa_sdk/gan/english/*.jpg')))   \n",
    "                X[i] = np.array(image)/255.0\n",
    "                label = [self.characters.find(x) for x in random_str]\n",
    "                if len(random_str) < self.n_len:\n",
    "                    label += [self.n_class]*(self.n_len-len(random_str)) \n",
    "                y[i] = label\n",
    "        elif batch_label_length == 5:   \n",
    "#             print('batch_label_length == 5',batch_label_length == 5)\n",
    "            for i in range(self.batch_size):\n",
    "#                 print('len 5',y.shape)\n",
    "                # 定义验证码字符集 (大写字母、小写字母、大写字母+数字)\n",
    "                gen_characters = random.choice([string.ascii_lowercase, string.ascii_uppercase, string.digits + string.ascii_uppercase])\n",
    "                if i % 4 == 0: \n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    image = generate_image(random_str, background =random_color(255,255), width=200, height=70)              \n",
    "                elif i % 4 ==1:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fonts=self.fonts_list, fill=(0,250), rotate=(-10,10), line=(0,2), point=(100,500), \n",
    "                                      color=(0,255), bg=tuple([random.randint(240,255) for _ in range(3)]))\n",
    "                elif i % 6 < 5:\n",
    "                    random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                    image = gen_captcha(random_str, fonts=self.fonts_list, fill=(0,250), rotate=(-20,20), line=(0,10), point=(0,500), \n",
    "                                      color=(0,255), bg=tuple([random.randint(240,255) for _ in range(3)]))   \n",
    "                else:\n",
    "                    image, random_str = rebuild_img(random.choice(real_imgs))   \n",
    "                X[i] = np.array(image)/255.0\n",
    "                label = [self.characters.find(x) for x in random_str]\n",
    "                if len(random_str) < self.n_len:\n",
    "                    label += [self.n_class]*(self.n_len-len(random_str))                 \n",
    "                y[i] = label\n",
    "        else:\n",
    "#             print('batch_label_length == 6',batch_label_length == 6)\n",
    "            for i in range(self.batch_size):\n",
    "#                 print('len 6',y.shape)\n",
    "                # 定义验证码字符集 (大写字母、小写字母、大写字母+数字)\n",
    "                gen_characters = random.choice([string.ascii_lowercase, string.ascii_uppercase, string.digits + string.ascii_uppercase])\n",
    "#                 if i % 4 <= 2: \n",
    "                random_str = ''.join([random.choice(gen_characters) for j in range(batch_label_length)])\n",
    "                image = generate_image(random_str, background =random_color(240,255), width=200, height=70)              \n",
    "#                 else:\n",
    "#                     image, random_str = rebuild_img(random.choice(real_imgs))   \n",
    "                X[i] = np.array(image)/255.0\n",
    "                label = [self.characters.find(x) for x in random_str]\n",
    "                if len(random_str) < self.n_len:\n",
    "                    label += [self.n_class]*(self.n_len-len(random_str))         \n",
    "                y[i] = label\n",
    "        return [X, y, input_length, label_length], np.ones(self.batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14 24 23 31 35 62]\n",
      "139974952355280\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 163,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # 测试生成器\n",
    "data = CaptchaSequence(characters, batch_size=4,n_len=6, width=200, height=70, steps=1, chars_len=(4, 6))\n",
    "[X_test, y_test, input_length, label_length], _  = data[0]\n",
    "idx =0\n",
    "plt.imshow(X_test[idx])\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)\n",
    "print(y_test[idx])\n",
    "# characters\n",
    "print(id(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 30, 80, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f36b5ddbeb8>"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 158,
       "width": 370
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": 12,
   "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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_1 True\n",
      "conv2d True\n",
      "batch_normalization True\n",
      "activation True\n",
      "conv2d_1 True\n",
      "batch_normalization_1 True\n",
      "activation_1 True\n",
      "max_pooling2d True\n"
     ]
    }
   ],
   "source": [
    "model.load_weights('gru_english4to6_ctc_best.h5')\n",
    "# 前8层不训练\n",
    "for layer in model.layers[:8]:\n",
    "    layer.trainable = True\n",
    "    print(layer.name, layer.trainable)\n",
    "# del train_data\n",
    "# del valid_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "1000/1000 [==============================] - 349s 349ms/step - loss: 0.0947 - val_loss: 0.1069\n",
      "Epoch 2/200\n",
      "1000/1000 [==============================] - 348s 348ms/step - loss: 0.0913 - val_loss: 1.0192\n",
      "Epoch 3/200\n",
      "1000/1000 [==============================] - 347s 347ms/step - loss: 0.0897 - val_loss: 0.1711\n",
      "Epoch 4/200\n",
      "1000/1000 [==============================] - 383s 383ms/step - loss: 0.0828 - val_loss: 0.9216\n",
      "Epoch 5/200\n",
      "1000/1000 [==============================] - 358s 358ms/step - loss: 0.0821 - val_loss: 0.0882\n",
      "Epoch 6/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0796 - val_loss: 0.1026\n",
      "Epoch 7/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0763 - val_loss: 0.3352\n",
      "Epoch 8/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0737 - val_loss: 0.0766\n",
      "Epoch 9/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0689 - val_loss: 0.1046\n",
      "Epoch 10/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0692 - val_loss: 0.7021\n",
      "Epoch 11/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0673 - val_loss: 0.1087\n",
      "Epoch 12/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0681 - val_loss: 0.0853\n",
      "Epoch 13/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0649 - val_loss: 0.1258\n",
      "Epoch 14/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0627 - val_loss: 0.0668\n",
      "Epoch 15/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0637 - val_loss: 1.6448\n",
      "Epoch 16/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0611 - val_loss: 0.0827\n",
      "Epoch 17/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0625 - val_loss: 0.1113\n",
      "Epoch 18/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0564 - val_loss: 0.0621\n",
      "Epoch 19/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0582 - val_loss: 0.0628\n",
      "Epoch 20/200\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0584 - val_loss: 0.1358\n",
      "Epoch 21/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0571 - val_loss: 0.0546\n",
      "Epoch 22/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0556 - val_loss: 0.0610\n",
      "Epoch 23/200\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0539 - val_loss: 0.2364\n",
      "Epoch 24/200\n",
      "1000/1000 [==============================] - 361s 361ms/step - loss: 0.0529 - val_loss: 0.0595\n",
      "Epoch 25/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0513 - val_loss: 0.2403\n",
      "Epoch 26/200\n",
      "1000/1000 [==============================] - 347s 347ms/step - loss: 0.0495 - val_loss: 0.0732\n",
      "Epoch 27/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0502 - val_loss: 0.0597\n",
      "Epoch 28/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0477 - val_loss: 0.0854\n",
      "Epoch 29/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0489 - val_loss: 0.0709\n",
      "Epoch 30/200\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0470 - val_loss: 0.5855\n",
      "Epoch 31/200\n",
      "1000/1000 [==============================] - 361s 361ms/step - loss: 0.0461 - val_loss: 0.1502\n",
      "Epoch 32/200\n",
      "1000/1000 [==============================] - 353s 353ms/step - loss: 0.0473 - val_loss: 0.1252\n",
      "Epoch 33/200\n",
      "1000/1000 [==============================] - 356s 356ms/step - loss: 0.0472 - val_loss: 0.1643\n",
      "Epoch 34/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0448 - val_loss: 0.2586\n",
      "Epoch 35/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0465 - val_loss: 0.5817\n",
      "Epoch 36/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0428 - val_loss: 0.0519\n",
      "Epoch 37/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0466 - val_loss: 0.0444\n",
      "Epoch 38/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0431 - val_loss: 0.5121\n",
      "Epoch 39/200\n",
      "1000/1000 [==============================] - 348s 348ms/step - loss: 0.0451 - val_loss: 0.0593\n",
      "Epoch 40/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0436 - val_loss: 0.1909\n",
      "Epoch 41/200\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0412 - val_loss: 0.6993\n",
      "Epoch 42/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0398 - val_loss: 0.1674\n",
      "Epoch 43/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0398 - val_loss: 1.5403\n",
      "Epoch 44/200\n",
      "1000/1000 [==============================] - 353s 353ms/step - loss: 0.0377 - val_loss: 0.0451\n",
      "Epoch 45/200\n",
      "1000/1000 [==============================] - 359s 359ms/step - loss: 0.0384 - val_loss: 0.3528\n",
      "Epoch 46/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0380 - val_loss: 0.0444\n",
      "Epoch 47/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0396 - val_loss: 0.0402\n",
      "Epoch 48/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0404 - val_loss: 0.1070\n",
      "Epoch 49/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0396 - val_loss: 0.1598\n",
      "Epoch 50/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0370 - val_loss: 0.0805\n",
      "Epoch 51/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0389 - val_loss: 1.7038\n",
      "Epoch 52/200\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0348 - val_loss: 0.7530\n",
      "Epoch 53/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0366 - val_loss: 0.0703\n",
      "Epoch 54/200\n",
      "1000/1000 [==============================] - 348s 348ms/step - loss: 0.0361 - val_loss: 0.2907\n",
      "Epoch 55/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0375 - val_loss: 0.0661\n",
      "Epoch 56/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0380 - val_loss: 3.1149\n",
      "Epoch 57/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0340 - val_loss: 0.1123\n",
      "Epoch 58/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0343 - val_loss: 0.0794\n",
      "Epoch 59/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0349 - val_loss: 6.1426\n",
      "Epoch 60/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0350 - val_loss: 0.1803\n",
      "Epoch 61/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0339 - val_loss: 0.0547\n",
      "Epoch 62/200\n",
      "1000/1000 [==============================] - 347s 347ms/step - loss: 0.0342 - val_loss: 0.4151\n",
      "Epoch 63/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0341 - val_loss: 0.1174\n",
      "Epoch 64/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0324 - val_loss: 0.0623\n",
      "Epoch 65/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0322 - val_loss: 0.3983\n",
      "Epoch 66/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0321 - val_loss: 0.0439\n",
      "Epoch 67/200\n",
      "1000/1000 [==============================] - 381s 381ms/step - loss: 0.0340 - val_loss: 0.4052\n",
      "Epoch 68/200\n",
      "1000/1000 [==============================] - 350s 350ms/step - loss: 0.0312 - val_loss: 1.0505\n",
      "Epoch 69/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0311 - val_loss: 0.0453\n",
      "Epoch 70/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0326 - val_loss: 0.0362\n",
      "Epoch 71/200\n",
      "1000/1000 [==============================] - 347s 347ms/step - loss: 0.0319 - val_loss: 0.0431\n",
      "Epoch 72/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0307 - val_loss: 0.0488\n",
      "Epoch 73/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0289 - val_loss: 0.0524\n",
      "Epoch 74/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0327 - val_loss: 0.0397\n",
      "Epoch 75/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0301 - val_loss: 1.9282\n",
      "Epoch 76/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0298 - val_loss: 1.1432\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 77/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0300 - val_loss: 0.0760\n",
      "Epoch 78/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0308 - val_loss: 0.0290\n",
      "Epoch 79/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0302 - val_loss: 1.0770\n",
      "Epoch 80/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0278 - val_loss: 0.0412\n",
      "Epoch 81/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0330 - val_loss: 0.0311\n",
      "Epoch 82/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0297 - val_loss: 1.2792\n",
      "Epoch 83/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0296 - val_loss: 0.2815\n",
      "Epoch 84/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0282 - val_loss: 0.1074\n",
      "Epoch 85/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0294 - val_loss: 0.1222\n",
      "Epoch 86/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0275 - val_loss: 0.0943\n",
      "Epoch 87/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0280 - val_loss: 0.5908\n",
      "Epoch 88/200\n",
      "1000/1000 [==============================] - 374s 374ms/step - loss: 0.0271 - val_loss: 0.0384\n",
      "Epoch 89/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0281 - val_loss: 0.0594\n",
      "Epoch 90/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0267 - val_loss: 0.3062\n",
      "Epoch 91/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0278 - val_loss: 0.0256\n",
      "Epoch 92/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0273 - val_loss: 0.0623\n",
      "Epoch 93/200\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0250 - val_loss: 0.0422\n",
      "Epoch 94/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0258 - val_loss: 0.0676\n",
      "Epoch 95/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0264 - val_loss: 0.0303\n",
      "Epoch 96/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0260 - val_loss: 0.0289\n",
      "Epoch 97/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0266 - val_loss: 0.0874\n",
      "Epoch 98/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0268 - val_loss: 0.0782\n",
      "Epoch 99/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0243 - val_loss: 0.1453\n",
      "Epoch 100/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0256 - val_loss: 0.0350\n",
      "Epoch 101/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0255 - val_loss: 1.9497\n",
      "Epoch 102/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0260 - val_loss: 0.9368\n",
      "Epoch 103/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0249 - val_loss: 0.0303\n",
      "Epoch 104/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0266 - val_loss: 0.0741\n",
      "Epoch 105/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0248 - val_loss: 0.1845\n",
      "Epoch 106/200\n",
      "1000/1000 [==============================] - 358s 358ms/step - loss: 0.0245 - val_loss: 2.2114\n",
      "Epoch 107/200\n",
      "1000/1000 [==============================] - 359s 359ms/step - loss: 0.0253 - val_loss: 0.1818\n",
      "Epoch 108/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0240 - val_loss: 0.0289\n",
      "Epoch 109/200\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0237 - val_loss: 0.5190\n",
      "Epoch 110/200\n",
      "1000/1000 [==============================] - 353s 353ms/step - loss: 0.0216 - val_loss: 0.1273\n",
      "Epoch 111/200\n",
      "1000/1000 [==============================] - 359s 359ms/step - loss: 0.0244 - val_loss: 0.0289\n",
      "Epoch 112/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0248 - val_loss: 0.0847\n",
      "Epoch 113/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0238 - val_loss: 0.0349\n",
      "Epoch 114/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0242 - val_loss: 2.8033\n",
      "Epoch 115/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0238 - val_loss: 5.4596\n",
      "Epoch 116/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0238 - val_loss: 0.1535\n",
      "Epoch 117/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0225 - val_loss: 0.0663\n",
      "Epoch 118/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0225 - val_loss: 0.0448\n",
      "Epoch 119/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0240 - val_loss: 2.5194\n",
      "Epoch 120/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0242 - val_loss: 0.1277\n",
      "Epoch 121/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0227 - val_loss: 0.1144\n",
      "Epoch 122/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0236 - val_loss: 2.1682\n",
      "Epoch 123/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0234 - val_loss: 0.2609\n",
      "Epoch 124/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0218 - val_loss: 3.2897\n",
      "Epoch 125/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0220 - val_loss: 0.2720\n",
      "Epoch 126/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0234 - val_loss: 0.0580\n",
      "Epoch 127/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0229 - val_loss: 0.0414\n",
      "Epoch 128/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0226 - val_loss: 0.0227\n",
      "Epoch 129/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0217 - val_loss: 0.2672\n",
      "Epoch 130/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0239 - val_loss: 0.0544\n",
      "Epoch 131/200\n",
      "1000/1000 [==============================] - 356s 356ms/step - loss: 0.0226 - val_loss: 0.0195\n",
      "Epoch 132/200\n",
      "1000/1000 [==============================] - 349s 349ms/step - loss: 0.0221 - val_loss: 0.0278\n",
      "Epoch 133/200\n",
      "1000/1000 [==============================] - 348s 348ms/step - loss: 0.0216 - val_loss: 0.2993\n",
      "Epoch 134/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0216 - val_loss: 0.0453\n",
      "Epoch 135/200\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0202 - val_loss: 0.0403\n",
      "Epoch 136/200\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0221 - val_loss: 0.0317\n",
      "Epoch 137/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0217 - val_loss: 0.0287\n",
      "Epoch 138/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0221 - val_loss: 0.1488\n",
      "Epoch 139/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0223 - val_loss: 0.9375\n",
      "Epoch 140/200\n",
      "1000/1000 [==============================] - 347s 347ms/step - loss: 0.0211 - val_loss: 0.3113\n",
      "Epoch 141/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0217 - val_loss: 0.0258\n",
      "Epoch 142/200\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0212 - val_loss: 1.5649\n",
      "Epoch 143/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0203 - val_loss: 0.0534\n",
      "Epoch 144/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0210 - val_loss: 0.0252\n",
      "Epoch 145/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0202 - val_loss: 0.3474\n",
      "Epoch 146/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0205 - val_loss: 1.5241\n",
      "Epoch 147/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0211 - val_loss: 0.0248\n",
      "Epoch 148/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0194 - val_loss: 0.0378\n",
      "Epoch 149/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0190 - val_loss: 0.7174\n",
      "Epoch 150/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0197 - val_loss: 0.0361\n",
      "Epoch 151/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0213 - val_loss: 0.2472\n",
      "Epoch 152/200\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0196 - val_loss: 0.7572\n",
      "Epoch 153/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0201 - val_loss: 2.8542\n",
      "Epoch 154/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0189 - val_loss: 0.1633\n",
      "Epoch 155/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0199 - val_loss: 0.7304\n",
      "Epoch 156/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0196 - val_loss: 0.0891\n",
      "Epoch 157/200\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0185 - val_loss: 0.1303\n",
      "Epoch 158/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0200 - val_loss: 0.1431\n",
      "Epoch 159/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0193 - val_loss: 0.0320\n",
      "Epoch 160/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0195 - val_loss: 0.0268\n",
      "Epoch 161/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0180 - val_loss: 0.0667\n",
      "Epoch 162/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0184 - val_loss: 0.0699\n",
      "Epoch 163/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0192 - val_loss: 0.0278\n",
      "Epoch 164/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0195 - val_loss: 2.8104\n",
      "Epoch 165/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0197 - val_loss: 0.0558\n",
      "Epoch 166/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0190 - val_loss: 0.0255\n",
      "Epoch 167/200\n",
      "1000/1000 [==============================] - 344s 344ms/step - loss: 0.0201 - val_loss: 0.9792\n",
      "Epoch 168/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0190 - val_loss: 0.0349\n",
      "Epoch 169/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0197 - val_loss: 0.0832\n",
      "Epoch 170/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0191 - val_loss: 0.0273\n",
      "Epoch 171/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0186 - val_loss: 0.0233\n",
      "Epoch 172/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0200 - val_loss: 0.2878\n",
      "Epoch 173/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0207 - val_loss: 0.4199\n",
      "Epoch 174/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0191 - val_loss: 0.0778\n",
      "Epoch 175/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0192 - val_loss: 0.0261\n",
      "Epoch 176/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0186 - val_loss: 0.0293\n",
      "Epoch 177/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0195 - val_loss: 0.0347\n",
      "Epoch 178/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0176 - val_loss: 0.2089\n",
      "Epoch 179/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0178 - val_loss: 0.1249\n",
      "Epoch 180/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0172 - val_loss: 0.1915\n",
      "Epoch 181/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0177 - val_loss: 0.0207\n",
      "Epoch 182/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0202 - val_loss: 6.6726\n",
      "Epoch 183/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0171 - val_loss: 0.1873\n",
      "Epoch 184/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0175 - val_loss: 0.0202\n",
      "Epoch 185/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0166 - val_loss: 0.2659\n",
      "Epoch 186/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0158 - val_loss: 0.0221\n",
      "Epoch 187/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0172 - val_loss: 1.3676\n",
      "Epoch 188/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0186 - val_loss: 0.1579\n",
      "Epoch 189/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0182 - val_loss: 2.7702\n",
      "Epoch 190/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0160 - val_loss: 0.0439\n",
      "Epoch 191/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0178 - val_loss: 0.0375\n",
      "Epoch 192/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0167 - val_loss: 0.0273\n",
      "Epoch 193/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0174 - val_loss: 0.0351\n",
      "Epoch 194/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0182 - val_loss: 0.0310\n",
      "Epoch 195/200\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0183 - val_loss: 0.0383\n",
      "Epoch 196/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0170 - val_loss: 0.0727\n",
      "Epoch 197/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0180 - val_loss: 0.0562\n",
      "Epoch 198/200\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0170 - val_loss: 0.0275\n",
      "Epoch 199/200\n",
      "1000/1000 [==============================] - 340s 340ms/step - loss: 0.0170 - val_loss: 0.7341\n",
      "Epoch 200/200\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0184 - val_loss: 0.0294\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f4e74f78668>"
      ]
     },
     "execution_count": 11,
     "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",
    "\n",
    "# model.load_weights('gru_english4to6_ctc_best.h5')\n",
    "\n",
    "# train_data = CaptchaSequence(characters, batch_size=128, steps=1000,input_length=13, label_length=6,chars_len=(5, 5)) # (characters, batch_size=128, steps=1000)\n",
    "# valid_data = CaptchaSequence(characters, batch_size=128, steps=100,input_length=13, label_length=6,chars_len=(5, 5))  # (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=3),ModelCheckpoint('gru_english4to6_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=8, validation_data=valid_data, workers=2, use_multiprocessing=True,\n",
    "#                    callbacks=callbacks)\n",
    "\n",
    "train_data = CaptchaSequence(characters, batch_size=128, steps=1000,input_length=13, label_length=6) # (characters, batch_size=128, steps=1000)\n",
    "valid_data = CaptchaSequence(characters, batch_size=128, steps=100,input_length=13, label_length=6)  # (characters, batch_size=128, steps=100)\n",
    "# 载入最好的模型继续训练一会\n",
    "model.load_weights('gru_english4to6_ctc_best.h5')\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_english4to6_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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "1000/1000 [==============================] - 293s 293ms/step - loss: 0.0032 - val_loss: 0.0028\n",
      "Epoch 2/300\n",
      "1000/1000 [==============================] - 310s 310ms/step - loss: 0.0024 - val_loss: 0.0102\n",
      "Epoch 3/300\n",
      "1000/1000 [==============================] - 311s 311ms/step - loss: 0.0029 - val_loss: 0.0028\n",
      "Epoch 4/300\n",
      "1000/1000 [==============================] - 292s 292ms/step - loss: 0.0018 - val_loss: 17.9769\n",
      "Epoch 5/300\n",
      "1000/1000 [==============================] - 324s 324ms/step - loss: 0.0022 - val_loss: 0.0018\n",
      "Epoch 6/300\n",
      "1000/1000 [==============================] - 310s 310ms/step - loss: 0.0025 - val_loss: 0.0032\n",
      "Epoch 7/300\n",
      "1000/1000 [==============================] - 315s 315ms/step - loss: 0.0022 - val_loss: 0.0027\n",
      "Epoch 8/300\n",
      "1000/1000 [==============================] - 309s 309ms/step - loss: 0.0016 - val_loss: 0.0023\n",
      "Epoch 9/300\n",
      "1000/1000 [==============================] - 328s 328ms/step - loss: 0.0018 - val_loss: 0.0018\n",
      "Epoch 10/300\n",
      "1000/1000 [==============================] - 309s 309ms/step - loss: 0.0019 - val_loss: 0.2085\n",
      "Epoch 11/300\n",
      "1000/1000 [==============================] - 296s 296ms/step - loss: 0.0017 - val_loss: 15.9265\n",
      "Epoch 12/300\n",
      "1000/1000 [==============================] - 306s 306ms/step - loss: 0.0013 - val_loss: 0.1947\n",
      "Epoch 13/300\n",
      "1000/1000 [==============================] - 357s 357ms/step - loss: 0.0020 - val_loss: 0.0032\n",
      "Epoch 14/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0017 - val_loss: 9.3167e-04\n",
      "Epoch 15/300\n",
      "1000/1000 [==============================] - 322s 322ms/step - loss: 0.0013 - val_loss: 0.0012\n",
      "Epoch 16/300\n",
      "1000/1000 [==============================] - 318s 318ms/step - loss: 0.0019 - val_loss: 0.0032\n",
      "Epoch 17/300\n",
      "1000/1000 [==============================] - 364s 364ms/step - loss: 0.0012 - val_loss: 0.0014\n",
      "Epoch 18/300\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0014 - val_loss: 0.1355\n",
      "Epoch 19/300\n",
      "1000/1000 [==============================] - 354s 354ms/step - loss: 0.0014 - val_loss: 5.9481e-04\n",
      "Epoch 20/300\n",
      "1000/1000 [==============================] - 386s 386ms/step - loss: 0.0014 - val_loss: 0.0018\n",
      "Epoch 21/300\n",
      "1000/1000 [==============================] - 366s 366ms/step - loss: 0.0013 - val_loss: 0.0014\n",
      "Epoch 22/300\n",
      "1000/1000 [==============================] - 359s 359ms/step - loss: 0.0012 - val_loss: 0.0015\n",
      "Epoch 23/300\n",
      "1000/1000 [==============================] - 381s 381ms/step - loss: 0.0012 - val_loss: 4.8570e-04\n",
      "Epoch 24/300\n",
      "1000/1000 [==============================] - 342s 342ms/step - loss: 0.0019 - val_loss: 6.8652e-04\n",
      "Epoch 25/300\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0014 - val_loss: 8.7500e-04\n",
      "Epoch 26/300\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0018 - val_loss: 3.8954e-04\n",
      "Epoch 27/300\n",
      "1000/1000 [==============================] - 370s 370ms/step - loss: 0.0010 - val_loss: 0.0012\n",
      "Epoch 28/300\n",
      "1000/1000 [==============================] - 363s 363ms/step - loss: 0.0012 - val_loss: 0.0017\n",
      "Epoch 29/300\n",
      "1000/1000 [==============================] - 382s 382ms/step - loss: 0.0013 - val_loss: 0.0015\n",
      "Epoch 30/300\n",
      "1000/1000 [==============================] - 338s 338ms/step - loss: 0.0015 - val_loss: 9.9728e-04\n",
      "Epoch 31/300\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 0.0012 - val_loss: 0.0143\n",
      "Epoch 32/300\n",
      "1000/1000 [==============================] - 327s 327ms/step - loss: 0.0012 - val_loss: 0.0013\n",
      "Epoch 33/300\n",
      "1000/1000 [==============================] - 361s 361ms/step - loss: 9.3152e-04 - val_loss: 0.0014\n",
      "Epoch 34/300\n",
      "1000/1000 [==============================] - 343s 343ms/step - loss: 0.0017 - val_loss: 0.0012\n",
      "Epoch 35/300\n",
      "1000/1000 [==============================] - 341s 341ms/step - loss: 0.0018 - val_loss: 4.7312e-04\n",
      "Epoch 36/300\n",
      "1000/1000 [==============================] - 339s 339ms/step - loss: 0.0010 - val_loss: 6.0925e-04\n",
      "Epoch 37/300\n",
      "1000/1000 [==============================] - 349s 349ms/step - loss: 0.0013 - val_loss: 5.1787e-04\n",
      "Epoch 38/300\n",
      "1000/1000 [==============================] - 345s 345ms/step - loss: 8.9243e-04 - val_loss: 7.5217e-04\n",
      "Epoch 39/300\n",
      "1000/1000 [==============================] - 337s 337ms/step - loss: 0.0012 - val_loss: 0.0012\n",
      "Epoch 40/300\n",
      "1000/1000 [==============================] - 346s 346ms/step - loss: 0.0013 - val_loss: 7.8339e-04\n",
      "Epoch 41/300\n",
      "1000/1000 [==============================] - 367s 367ms/step - loss: 9.1171e-04 - val_loss: 7.2432e-04\n",
      "Epoch 42/300\n",
      "1000/1000 [==============================] - 365s 365ms/step - loss: 0.0017 - val_loss: 4.9699e-04\n",
      "Epoch 43/300\n",
      "1000/1000 [==============================] - 385s 385ms/step - loss: 0.0012 - val_loss: 0.0013\n",
      "Epoch 44/300\n",
      "1000/1000 [==============================] - 362s 362ms/step - loss: 0.0016 - val_loss: 3.5807e-04\n",
      "Epoch 45/300\n",
      "1000/1000 [==============================] - 361s 361ms/step - loss: 9.0703e-04 - val_loss: 5.6368e-04\n",
      "Epoch 46/300\n",
      "1000/1000 [==============================] - 356s 356ms/step - loss: 0.0010 - val_loss: 0.0011\n",
      "Epoch 47/300\n",
      "1000/1000 [==============================] - 391s 391ms/step - loss: 8.9139e-04 - val_loss: 6.0659e-04\n",
      "Epoch 48/300\n",
      " 104/1000 [==>...........................] - ETA: 4:29 - loss: 3.8686e-04"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-883:\n",
      "Process ForkPoolWorker-881:\n",
      "Process ForkPoolWorker-882:\n",
      "Process ForkPoolWorker-880:\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 254, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 254, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 254, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 254, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/utils/data_utils.py\", line 432, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/pool.py\", line 119, in worker\n",
      "    result = (True, func(*args, **kwds))\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/queues.py\", line 343, in get\n",
      "    res = self._reader.recv_bytes()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/utils/data_utils.py\", line 432, in get_index\n",
      "    return _SHARED_SEQUENCES[uid][i]\n",
      "  File \"<ipython-input-8-a13a054b6cf6>\", line 42, in __getitem__\n",
      "    image = generate_image(random_str, background =random_color(255,255), width=200, height=70)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\n",
      "  File \"<ipython-input-6-0962014c7ea7>\", line 146, in generate_image\n",
      "    draw.point(xy=(random_xy(width,height)),fill=random_color(155, 255))\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/queues.py\", line 342, in get\n",
      "    with self._rlock:\n",
      "  File \"<ipython-input-8-a13a054b6cf6>\", line 42, in __getitem__\n",
      "    image = generate_image(random_str, background =random_color(255,255), width=200, height=70)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/PIL/ImageDraw.py\", line 231, in point\n",
      "    ink, fill = self._getink(fill)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"<ipython-input-6-0962014c7ea7>\", line 115, in generate_image\n",
      "    char_img = get_char_img(char=c, font=font, color=ch_color, angle=random.randint(-10,10))\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/PIL/ImageDraw.py\", line 101, in _getink\n",
      "    def _getink(self, ink, fill=None):\n",
      "KeyboardInterrupt\n",
      "  File \"<ipython-input-6-0962014c7ea7>\", line 95, in get_char_img\n",
      "    rot = im.rotate(angle,Image.BILINEAR,expand=1)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/PIL/Image.py\", line 1915, in rotate\n",
      "    fillcolor=fillcolor)\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/PIL/Image.py\", line 2192, in transform\n",
      "    return self.convert('RGBa').transform(\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/PIL/Image.py\", line 1030, in convert\n",
      "    im = self.im.convert(mode, dither)\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-8f9bc6f4c790>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'ctc'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamsgrad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m model.fit_generator(train_data, epochs=300, validation_data=valid_data, workers=4, use_multiprocessing=True,\n\u001b[0;32m---> 13\u001b[0;31m                     callbacks=callbacks)\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1777\u001b[0m         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1778\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1779\u001b[0;31m         initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1781\u001b[0m   def evaluate_generator(self,\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         outs = model.train_on_batch(\n\u001b[0;32m--> 204\u001b[0;31m             x, y, sample_weight=sample_weight, class_weight=class_weight)\n\u001b[0m\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1551\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1552\u001b[0;31m       \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2912\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_arrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_symbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbol_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2914\u001b[0;31m     \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2915\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2916\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1380\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1381\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1382\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1383\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1384\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint\n",
    "from tensorflow.keras.optimizers import *\n",
    "# 载入最好的模型继续训练一会\n",
    "model.load_weights('gru_english4to6_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_english4to6_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=300, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.load_weights('ctc_best.h5')\n",
    "# model.load_weights('gru_english4to6_ctc_best.h5')\n",
    "# base_model.save('gru_english_base_model.h5')\n",
    "base_model.load_weights('gru_english_base_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Shapes (1024, 384) and (1024, 192) are incompatible",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-9727b5b90972>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 测试模型\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gru_english4to6_ctc_best_2.h5'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 19层网络\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcharacters2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcharacters\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/network.py\u001b[0m in \u001b[0;36mload_weights\u001b[0;34m(self, filepath, by_name)\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[0msaving\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights_from_hdf5_group_by_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1447\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1448\u001b[0;31m         \u001b[0msaving\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights_from_hdf5_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1450\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_post_build_cleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_weights_from_hdf5_group\u001b[0;34m(f, layers)\u001b[0m\n\u001b[1;32m    800\u001b[0m                        str(len(weight_values)) + ' elements.')\n\u001b[1;32m    801\u001b[0m     \u001b[0mweight_value_tuples\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbolic_weights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 802\u001b[0;31m   \u001b[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    803\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/keras/backend.py\u001b[0m in \u001b[0;36mbatch_set_value\u001b[0;34m(tuples)\u001b[0m\n\u001b[1;32m   2712\u001b[0m           assign_placeholder = array_ops.placeholder(tf_dtype,\n\u001b[1;32m   2713\u001b[0m                                                      shape=value.shape)\n\u001b[0;32m-> 2714\u001b[0;31m           \u001b[0massign_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0massign_placeholder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2715\u001b[0m           \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assign_placeholder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0massign_placeholder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2716\u001b[0m           \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_assign_op\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0massign_op\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(self, value, use_locking, name, read_value)\u001b[0m\n\u001b[1;32m    961\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0m_handle_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    962\u001b[0m       \u001b[0mvalue_tensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 963\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_is_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    964\u001b[0m       assign_op = gen_resource_variable_ops.assign_variable_op(\n\u001b[1;32m    965\u001b[0m           self.handle, value_tensor, name=name)\n",
      "\u001b[0;32m~/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/tensorflow/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36massert_is_compatible_with\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    845\u001b[0m     \"\"\"\n\u001b[1;32m    846\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 847\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Shapes %s and %s are incompatible\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    848\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    849\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mmost_specific_compatible_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Shapes (1024, 384) and (1024, 192) are incompatible"
     ]
    }
   ],
   "source": [
    "# 测试模型\n",
    "model.load_weights('gru_english4to6_ctc_best_2.h5') # 19层网络 \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('FileInfo0508_2/*.jpg')[:]: #FileInfo0508_2/*.jpg  '/data/esa_sdk/gan/english/*.jpg'\n",
    "#         random_str = path.split('_')[-1][:-4].replace('1', 'l') \n",
    "#         if len(random_str) ==5 and re.search('[0-9]', random_str)==None:\n",
    "#             random_str = random_str.lower()\n",
    "\n",
    "    for path in glob.glob('/data/esa_sdk/gan/english/*.jpg')[:]:     \n",
    "        random_str = path.split('_')[-1][:-4]\n",
    "        if len(random_str) ==4:                     \n",
    "            random_str = random_str.upper()\n",
    "            \n",
    "#             print(random_str)\n",
    "#         if random_str.isdigit() and len(random_str) ==4:\n",
    "            img = Image.open(path)\n",
    "            img = img.convert('RGB')\n",
    "            img = img.resize((200,70), Image.NEAREST)\n",
    "            X.append(np.array(img)/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=5, chars_len=(6,6))\n",
    "# \n",
    "pos = neg = 0\n",
    "t1 = time.time()\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])[:, :10 ]\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]])      \n",
    "        y_true = ''.join([characters[x] for x in y_test[j] if x < len(characters)])\n",
    "#         if re.sub(' ','',out) != y_true:\n",
    "        if out != y_true:\n",
    "#             print(' ' in out[-2:])\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('正确数：%d， 错误数：%d'%(pos,neg))\n",
    "# print('y_test长度:', len(y_test), y_test)\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])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensorflow.python.framework.ops.Tensor"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = K.ctc_decode(y_pred, input_length=np.ones(y_pred.shape[0])*y_pred.shape[1])\n",
    "type(ct[0][0])"
   ]
  },
  {
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "num = 0\n",
    "for path in glob.glob('FileInfo0508/*.jpg'): #    Digit5/*.jpg  ../FileInfo1031/*.jpg\n",
    "    random_str = path.split('_')[-1][:-4] \n",
    "    if re.search('[a-zA-Z]', random_str) != None and len(random_str) ==4:\n",
    "        num += 1\n",
    "print(num)"
   ]
  }
 ],
 "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
}