{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image, ImageDraw, ImageFont\n",
    "import glob\n",
    "import string\n",
    "import math\n",
    "import pickle\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "# width = 100   # 60 * 4\n",
    "# height = 40 # 60\n",
    "\n",
    "# # 生成验证码和图片\n",
    "# def generate_code(code_str=None, font=None, l=(3,10), p=(20,200)):  \n",
    "#     if code_str == None:\n",
    "#         num = '0123456789'\n",
    "#         sign = '+-*'\n",
    "#         code_str = '{}{}{}='.format(random.choice(num),random.choice(sign),random.choice(num))\n",
    "#     if font == None:\n",
    "#         font = ImageFont.truetype('/usr/share/fonts/WindowsFonts/fonts/ariali.ttf', 32)  # Arial.ttf calibri.ttf  ARLRDBD.TTF  consolai.ttf\n",
    "#     image = Image.new('L', (width, height), (255))    \n",
    "#     # 创建Draw对象\n",
    "#     draw = ImageDraw.Draw(image)\n",
    "#     # 输出文字\n",
    "#     #code_str = '2*6='\n",
    "#     draw.text((random.randint(10,30),random.randint(5,13)), code_str, font=font, fill=(0))\n",
    "#     img_raw = np.array(image)\n",
    "#     # 画点\n",
    "#     for _ in range(random.randint(p[0],p[1])):\n",
    "#         draw.point((random.randint(0, width), random.randint(0, height)), fill=random.randint(100, 250))\n",
    "# #         x = random.randint(0, width)\n",
    "# #         y = random.randint(0, height)\n",
    "# #         draw.arc((x,y,x+4,y+4), 10,80, fill=0)\n",
    "#     # 画线\n",
    "#     for _ in range(random.randint(l[0],l[1])):\n",
    "#         draw.line((random.randint(0, width), random.randint(0, height),\n",
    "#                    random.randint(0, width), random.randint(0, height)),\n",
    "#                   fill=(random.randint(0,0)), width=random.randint(1,2))\n",
    "#     img_noisy = np.array(image)\n",
    "#     return code_str, img_raw, img_noisy\n",
    "\n",
    "\n",
    "# def get_data(n=1000):\n",
    "#     data_raw = []\n",
    "#     data_noisy = []\n",
    "#     num = '0123456789'\n",
    "#     sign = '+*-'\n",
    "#     for i in range(n):\n",
    "#         for file in glob.glob('/usr/share/fonts/WindowsFonts/fonts/*.ttf'):\n",
    "#             font = ImageFont.truetype(file, random.randint(25,32))         \n",
    "#             code_str = '{}{}{}='.format(random.choice(num), random.choice(sign), random.choice(num))\n",
    "#             code_str, img_raw, img_noisy = generate_code(font=font, code_str=code_str,l=(3,5), p=(0,100))\n",
    "#             data_raw.append(img_raw)\n",
    "#             data_noisy.append(img_noisy)\n",
    "# #     return np.array(data_raw), np.array(data_noisy)\n",
    "#     return data_raw, data_noisy\n",
    "# if __name__ == '__main__':    \n",
    "#     code_str = 'ajjib'\n",
    "#     code_str, img_raw, img_noisy = generate_code(code_str=code_str,l=(3,5), p=(0,1000))\n",
    "#     x_test, x_test_noisy = get_data(n=10)\n",
    "#     im = img_raw, img_noisy\n",
    "#     plt.figure(figsize=(50,20))\n",
    "#     for i in range(2):\n",
    "#         plt.subplot(2,2,i+1)\n",
    "#         plt.imshow(im[i])\n",
    "#     plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3600x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "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), int(ws[i]*0.9)+1)\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 = (50, 70)), \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((240,80), Image.BILINEAR), img2.resize((240,80), Image.BILINEAR)\n",
    "\n",
    "characters = string.digits + string.ascii_uppercase + string.ascii_lowercase # 验证码字符集合数字+英文\n",
    "fonts_list = glob.glob('/usr/share/fonts/WindowsFonts/fonts/*.ttf')\n",
    "text = ''.join([random.choice(characters) for _ in range(random.randint(4,6))])\n",
    "img, img2 = gen_captcha(text=text, 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=(50,20))\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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string \n",
    "characters = string.digits + string.ascii_uppercase + string.ascii_lowercase # 验证码字符集合数字+英文\n",
    "def get_data(n=20):\n",
    "#     raw = []\n",
    "#     noisy = []\n",
    "    raw = np.zeros((n, 80, 240, 3))\n",
    "    noisy = np.zeros((n, 80, 240, 3))\n",
    "    for i in range(n):\n",
    "        text = ''.join([random.choice(characters) for _ in range(random.randint(4,6))])\n",
    "        img, img2 = gen_captcha(text, 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",
    "#         raw.append(np.array(img)/255.)\n",
    "#         noisy.append(np.array(img2)/255.)\n",
    "        raw[i] = np.array(img)/255.\n",
    "        noisy[i] = np.array(img2)/255.\n",
    "        del img\n",
    "        del img2\n",
    "#     return np.array([np.array(img)/255. for img in raw]), np.array([np.array(img2)/255. for img in noisy])\n",
    "    return np.array(raw), np.array(noisy)\n",
    "# raw, noisy = get_data(100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "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=240, height=80, \n",
    "                 input_length=12, label_length=6, chars_len=(4, 6)): #  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",
    "            \n",
    "    def __len__(self):\n",
    "        return self.steps\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        raw = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n",
    "        noisy = np.zeros((self.batch_size, self.height, self.width, 3), dtype=np.float32)\n",
    "        for i in range(self.batch_size):           \n",
    "            random_str = ''.join([random.choice(self.characters) for j in range(random.randint(self.chars_len[0],self.chars_len[1]))])\n",
    "            img, img2 = gen_captcha(random_str, 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",
    "            raw[i] = np.array(img)/255.0\n",
    "            noisy[i] = np.array(img2)/255.0\n",
    "            label = [self.characters.find(x) for x in random_str]\n",
    "\n",
    "        return noisy, raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 80, 240, 3)        0         \n",
      "_________________________________________________________________\n",
      "cnn1 (Conv2D)                (None, 80, 240, 32)       4736      \n",
      "_________________________________________________________________\n",
      "bn1 (BatchNormalization)     (None, 80, 240, 32)       128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 80, 240, 32)       0         \n",
      "_________________________________________________________________\n",
      "pool1 (MaxPooling2D)         (None, 80, 240, 32)       0         \n",
      "_________________________________________________________________\n",
      "cnn2 (Conv2D)                (None, 80, 240, 64)       51264     \n",
      "_________________________________________________________________\n",
      "bn2 (BatchNormalization)     (None, 80, 240, 64)       256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 80, 240, 64)       0         \n",
      "_________________________________________________________________\n",
      "pool2 (MaxPooling2D)         (None, 40, 120, 64)       0         \n",
      "_________________________________________________________________\n",
      "cnn3 (Conv2D)                (None, 40, 120, 128)      73856     \n",
      "_________________________________________________________________\n",
      "bn3 (BatchNormalization)     (None, 40, 120, 128)      512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 40, 120, 128)      0         \n",
      "_________________________________________________________________\n",
      "pool3 (MaxPooling2D)         (None, 20, 60, 128)       0         \n",
      "_________________________________________________________________\n",
      "cnn4 (Conv2D)                (None, 20, 60, 128)       147584    \n",
      "_________________________________________________________________\n",
      "bn4 (BatchNormalization)     (None, 20, 60, 128)       512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 20, 60, 128)       0         \n",
      "_________________________________________________________________\n",
      "pool4 (MaxPooling2D)         (None, 10, 30, 128)       0         \n",
      "_________________________________________________________________\n",
      "cnn5 (Conv2D)                (None, 10, 30, 64)        73792     \n",
      "_________________________________________________________________\n",
      "bn5 (BatchNormalization)     (None, 10, 30, 64)        256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 10, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "pool5 (MaxPooling2D)         (None, 5, 15, 64)         0         \n",
      "_________________________________________________________________\n",
      "cnn6 (Conv2D)                (None, 5, 15, 64)         36928     \n",
      "_________________________________________________________________\n",
      "bn6 (BatchNormalization)     (None, 5, 15, 64)         256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_5 (LeakyReLU)    (None, 5, 15, 64)         0         \n",
      "_________________________________________________________________\n",
      "upsamp6 (UpSampling2D)       (None, 10, 30, 64)        0         \n",
      "_________________________________________________________________\n",
      "cnn7 (Conv2D)                (None, 10, 30, 128)       73856     \n",
      "_________________________________________________________________\n",
      "bn7 (BatchNormalization)     (None, 10, 30, 128)       512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_6 (LeakyReLU)    (None, 10, 30, 128)       0         \n",
      "_________________________________________________________________\n",
      "upsamp7 (UpSampling2D)       (None, 20, 60, 128)       0         \n",
      "_________________________________________________________________\n",
      "cnn14 (Conv2D)               (None, 20, 60, 64)        204864    \n",
      "_________________________________________________________________\n",
      "bn14 (BatchNormalization)    (None, 20, 60, 64)        256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_7 (LeakyReLU)    (None, 20, 60, 64)        0         \n",
      "_________________________________________________________________\n",
      "upsamp14 (UpSampling2D)      (None, 40, 120, 64)       0         \n",
      "_________________________________________________________________\n",
      "cnn15 (Conv2D)               (None, 40, 120, 32)       100384    \n",
      "_________________________________________________________________\n",
      "bn15 (BatchNormalization)    (None, 40, 120, 32)       128       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_8 (LeakyReLU)    (None, 40, 120, 32)       0         \n",
      "_________________________________________________________________\n",
      "upsamp15 (UpSampling2D)      (None, 80, 240, 32)       0         \n",
      "_________________________________________________________________\n",
      "decode (Conv2D)              (None, 80, 240, 3)        867       \n",
      "=================================================================\n",
      "Total params: 770,947\n",
      "Trainable params: 769,539\n",
      "Non-trainable params: 1,408\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.models import *\n",
    "width = 240   # 60 * 4\n",
    "height = 80\n",
    "\n",
    "inputs = Input(shape=(height,width,3))\n",
    "def cnn_layer(index,inputs, filters, kernel_size, strides, padding='same'):\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=padding,name='pool{}'.format(index + 1))(x)\n",
    "    return x\n",
    "\n",
    "def up_layer(index, inputs, filters, kernel_size, strides):\n",
    "    x = Conv2D(filters=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 = UpSampling2D(size=(2,2),name='upsamp{}'.format(index+1))(x)\n",
    "    return x\n",
    "\n",
    "# x = ZeroPadding2D(padding=(1,1))(x)\n",
    "    \n",
    "x = cnn_layer(0,inputs=inputs, 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 = up_layer(5,inputs=x, kernel_size=3, filters=64, strides=(1, 2))\n",
    "x = up_layer(6,inputs=x, kernel_size=3, filters=128, strides=(1, 2))\n",
    "# x = up_layer(12,inputs=x, kernel_size=3, filters=128, strides=(1, 2))\n",
    "x = up_layer(13,inputs=x, kernel_size=5, filters=64, strides=(1, 2))\n",
    "x = up_layer(14,inputs=x, kernel_size=7, filters=32, strides=(1, 1))\n",
    "x = Conv2D(filters=3, kernel_size=3, strides=1, padding='same', activation='sigmoid', name='decode')(x)\n",
    "model = Model(inputs=inputs, outputs=x)\n",
    "model.summary()\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5078 - acc: 0.0070\n",
      "Epoch 00001: val_loss improved from inf to 0.50698, saving model to model/zibianmaquzao.hdf5\n",
      "1000/1000 [==============================] - 489s 489ms/step - loss: 0.5078 - acc: 0.0070 - val_loss: 0.5070 - val_acc: 0.0071\n",
      "Epoch 2/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5076 - acc: 0.0070\n",
      "Epoch 00002: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5076 - acc: 0.0070 - val_loss: 0.5071 - val_acc: 0.0076\n",
      "Epoch 3/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5072 - acc: 0.0070\n",
      "Epoch 00003: val_loss improved from 0.50698 to 0.50647, saving model to model/zibianmaquzao.hdf5\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5072 - acc: 0.0070 - val_loss: 0.5065 - val_acc: 0.0077\n",
      "Epoch 4/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5075 - acc: 0.0071\n",
      "Epoch 00004: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5075 - acc: 0.0071 - val_loss: 0.5082 - val_acc: 0.0069\n",
      "Epoch 5/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5067 - acc: 0.0070\n",
      "Epoch 00005: val_loss improved from 0.50647 to 0.50588, saving model to model/zibianmaquzao.hdf5\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5067 - acc: 0.0070 - val_loss: 0.5059 - val_acc: 0.0070\n",
      "Epoch 6/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5077 - acc: 0.0070\n",
      "Epoch 00006: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5077 - acc: 0.0070 - val_loss: 0.5072 - val_acc: 0.0066\n",
      "Epoch 7/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5074 - acc: 0.0069\n",
      "Epoch 00007: val_loss improved from 0.50588 to 0.50482, saving model to model/zibianmaquzao.hdf5\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5074 - acc: 0.0069 - val_loss: 0.5048 - val_acc: 0.0069\n",
      "Epoch 8/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5072 - acc: 0.0070\n",
      "Epoch 00008: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5072 - acc: 0.0070 - val_loss: 0.5066 - val_acc: 0.0065\n",
      "Epoch 9/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5074 - acc: 0.0070\n",
      "Epoch 00009: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5074 - acc: 0.0070 - val_loss: 0.5050 - val_acc: 0.0074\n",
      "Epoch 10/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5073 - acc: 0.0071\n",
      "Epoch 00010: val_loss did not improve\n",
      "1000/1000 [==============================] - 486s 486ms/step - loss: 0.5073 - acc: 0.0070 - val_loss: 0.5067 - val_acc: 0.0077\n",
      "Epoch 11/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5070 - acc: 0.0071\n",
      "Epoch 00011: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5070 - acc: 0.0071 - val_loss: 0.5067 - val_acc: 0.0079\n",
      "Epoch 12/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5067 - acc: 0.0071\n",
      "Epoch 00012: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5067 - acc: 0.0071 - val_loss: 0.5061 - val_acc: 0.0069\n",
      "Epoch 13/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5076 - acc: 0.0069\n",
      "Epoch 00013: val_loss did not improve\n",
      "1000/1000 [==============================] - 490s 490ms/step - loss: 0.5076 - acc: 0.0069 - val_loss: 0.5063 - val_acc: 0.0061\n",
      "Epoch 14/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5072 - acc: 0.0071\n",
      "Epoch 00014: val_loss did not improve\n",
      "1000/1000 [==============================] - 494s 494ms/step - loss: 0.5072 - acc: 0.0070 - val_loss: 0.5070 - val_acc: 0.0066\n",
      "Epoch 15/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5073 - acc: 0.0068\n",
      "Epoch 00015: val_loss did not improve\n",
      "1000/1000 [==============================] - 492s 492ms/step - loss: 0.5073 - acc: 0.0068 - val_loss: 0.5095 - val_acc: 0.0068\n",
      "Epoch 16/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5069 - acc: 0.0073\n",
      "Epoch 00016: val_loss did not improve\n",
      "1000/1000 [==============================] - 486s 486ms/step - loss: 0.5069 - acc: 0.0073 - val_loss: 0.5065 - val_acc: 0.0067\n",
      "Epoch 17/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5072 - acc: 0.0070\n",
      "Epoch 00017: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5072 - acc: 0.0070 - val_loss: 0.5058 - val_acc: 0.0068\n",
      "Epoch 18/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5068 - acc: 0.0070\n",
      "Epoch 00018: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5068 - acc: 0.0070 - val_loss: 0.5081 - val_acc: 0.0066\n",
      "Epoch 19/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.0072\n",
      "Epoch 00019: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5066 - acc: 0.0072 - val_loss: 0.5081 - val_acc: 0.0072\n",
      "Epoch 20/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.0070\n",
      "Epoch 00020: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5063 - acc: 0.0070 - val_loss: 0.5072 - val_acc: 0.0063\n",
      "Epoch 21/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.0071\n",
      "Epoch 00021: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5067 - acc: 0.0071 - val_loss: 0.5073 - val_acc: 0.0068\n",
      "Epoch 22/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5070 - acc: 0.0070\n",
      "Epoch 00022: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5071 - acc: 0.0070 - val_loss: 0.5062 - val_acc: 0.0070\n",
      "Epoch 23/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5069 - acc: 0.0070\n",
      "Epoch 00023: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5069 - acc: 0.0070 - val_loss: 0.5077 - val_acc: 0.0073\n",
      "Epoch 24/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5067 - acc: 0.0071\n",
      "Epoch 00024: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5067 - acc: 0.0071 - val_loss: 0.5088 - val_acc: 0.0066\n",
      "Epoch 25/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.0071\n",
      "Epoch 00025: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5066 - acc: 0.0071 - val_loss: 0.5068 - val_acc: 0.0071\n",
      "Epoch 26/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.0069\n",
      "Epoch 00026: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5066 - acc: 0.0069 - val_loss: 0.5060 - val_acc: 0.0069\n",
      "Epoch 27/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5071 - acc: 0.0071\n",
      "Epoch 00027: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5071 - acc: 0.0071 - val_loss: 0.5075 - val_acc: 0.0064\n",
      "Epoch 28/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5068 - acc: 0.0070\n",
      "Epoch 00028: val_loss did not improve\n",
      "1000/1000 [==============================] - 491s 491ms/step - loss: 0.5068 - acc: 0.0070 - val_loss: 0.5059 - val_acc: 0.0070\n",
      "Epoch 29/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.0072\n",
      "Epoch 00029: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5066 - acc: 0.0072 - val_loss: 0.5081 - val_acc: 0.0066\n",
      "Epoch 30/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5064 - acc: 0.0069\n",
      "Epoch 00030: val_loss did not improve\n",
      "1000/1000 [==============================] - 478s 478ms/step - loss: 0.5064 - acc: 0.0069 - val_loss: 0.5059 - val_acc: 0.0065\n",
      "Epoch 31/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0071\n",
      "Epoch 00031: val_loss did not improve\n",
      "1000/1000 [==============================] - 487s 487ms/step - loss: 0.5060 - acc: 0.0071 - val_loss: 0.5067 - val_acc: 0.0069\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 32/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0071\n",
      "Epoch 00032: val_loss did not improve\n",
      "1000/1000 [==============================] - 484s 484ms/step - loss: 0.5062 - acc: 0.0071 - val_loss: 0.5076 - val_acc: 0.0067\n",
      "Epoch 33/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.0069\n",
      "Epoch 00033: val_loss improved from 0.50482 to 0.50461, saving model to model/zibianmaquzao.hdf5\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5066 - acc: 0.0069 - val_loss: 0.5046 - val_acc: 0.0066\n",
      "Epoch 34/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5065 - acc: 0.0070\n",
      "Epoch 00034: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5065 - acc: 0.0070 - val_loss: 0.5064 - val_acc: 0.0068\n",
      "Epoch 35/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0071\n",
      "Epoch 00035: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5063 - acc: 0.0071 - val_loss: 0.5060 - val_acc: 0.0074\n",
      "Epoch 36/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.0069\n",
      "Epoch 00036: val_loss did not improve\n",
      "1000/1000 [==============================] - 484s 484ms/step - loss: 0.5066 - acc: 0.0069 - val_loss: 0.5052 - val_acc: 0.0070\n",
      "Epoch 37/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5065 - acc: 0.0071\n",
      "Epoch 00037: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5065 - acc: 0.0071 - val_loss: 0.5049 - val_acc: 0.0069\n",
      "Epoch 38/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0069\n",
      "Epoch 00038: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5060 - acc: 0.0069 - val_loss: 0.5055 - val_acc: 0.0071\n",
      "Epoch 39/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.0070\n",
      "Epoch 00039: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5063 - acc: 0.0070 - val_loss: 0.5080 - val_acc: 0.0067\n",
      "Epoch 40/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.0070\n",
      "Epoch 00040: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5063 - acc: 0.0070 - val_loss: 0.5065 - val_acc: 0.0072\n",
      "Epoch 41/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0071\n",
      "Epoch 00041: val_loss did not improve\n",
      "1000/1000 [==============================] - 484s 484ms/step - loss: 0.5061 - acc: 0.0071 - val_loss: 0.5049 - val_acc: 0.0069\n",
      "Epoch 42/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0072\n",
      "Epoch 00042: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5061 - acc: 0.0072 - val_loss: 0.5065 - val_acc: 0.0072\n",
      "Epoch 43/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5069 - acc: 0.0070\n",
      "Epoch 00043: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5069 - acc: 0.0070 - val_loss: 0.5065 - val_acc: 0.0066\n",
      "Epoch 44/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0070\n",
      "Epoch 00044: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5059 - acc: 0.0070 - val_loss: 0.5069 - val_acc: 0.0067\n",
      "Epoch 45/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5068 - acc: 0.0070\n",
      "Epoch 00045: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5068 - acc: 0.0070 - val_loss: 0.5049 - val_acc: 0.0072\n",
      "Epoch 46/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0069\n",
      "Epoch 00046: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5060 - acc: 0.0069 - val_loss: 0.5066 - val_acc: 0.0067\n",
      "Epoch 47/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0071\n",
      "Epoch 00047: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5060 - acc: 0.0071 - val_loss: 0.5070 - val_acc: 0.0064\n",
      "Epoch 48/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0072\n",
      "Epoch 00048: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5061 - acc: 0.0072 - val_loss: 0.5055 - val_acc: 0.0072\n",
      "Epoch 49/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0070\n",
      "Epoch 00049: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5062 - acc: 0.0070 - val_loss: 0.5082 - val_acc: 0.0071\n",
      "Epoch 50/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0070\n",
      "Epoch 00050: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5059 - acc: 0.0070 - val_loss: 0.5073 - val_acc: 0.0066\n",
      "Epoch 51/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5065 - acc: 0.0069\n",
      "Epoch 00051: val_loss did not improve\n",
      "1000/1000 [==============================] - 491s 491ms/step - loss: 0.5065 - acc: 0.0069 - val_loss: 0.5081 - val_acc: 0.0072\n",
      "Epoch 52/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.0070\n",
      "Epoch 00052: val_loss did not improve\n",
      "1000/1000 [==============================] - 487s 487ms/step - loss: 0.5063 - acc: 0.0070 - val_loss: 0.5065 - val_acc: 0.0073\n",
      "Epoch 53/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5068 - acc: 0.0070\n",
      "Epoch 00053: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5068 - acc: 0.0070 - val_loss: 0.5071 - val_acc: 0.0071\n",
      "Epoch 54/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5065 - acc: 0.0068\n",
      "Epoch 00054: val_loss did not improve\n",
      "1000/1000 [==============================] - 507s 507ms/step - loss: 0.5065 - acc: 0.0068 - val_loss: 0.5064 - val_acc: 0.0069\n",
      "Epoch 55/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0071\n",
      "Epoch 00055: val_loss did not improve\n",
      "1000/1000 [==============================] - 499s 499ms/step - loss: 0.5061 - acc: 0.0071 - val_loss: 0.5066 - val_acc: 0.0070\n",
      "Epoch 56/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5065 - acc: 0.0068\n",
      "Epoch 00056: val_loss did not improve\n",
      "1000/1000 [==============================] - 488s 488ms/step - loss: 0.5065 - acc: 0.0068 - val_loss: 0.5055 - val_acc: 0.0069\n",
      "Epoch 57/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5067 - acc: 0.0070\n",
      "Epoch 00057: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5067 - acc: 0.0070 - val_loss: 0.5076 - val_acc: 0.0067\n",
      "Epoch 58/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0071\n",
      "Epoch 00058: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5062 - acc: 0.0071 - val_loss: 0.5059 - val_acc: 0.0062\n",
      "Epoch 59/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5057 - acc: 0.0071\n",
      "Epoch 00059: val_loss did not improve\n",
      "1000/1000 [==============================] - 485s 485ms/step - loss: 0.5057 - acc: 0.0071 - val_loss: 0.5063 - val_acc: 0.0062\n",
      "Epoch 60/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0069\n",
      "Epoch 00060: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5061 - acc: 0.0069 - val_loss: 0.5053 - val_acc: 0.0076\n",
      "Epoch 61/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0070\n",
      "Epoch 00061: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5059 - acc: 0.0070 - val_loss: 0.5059 - val_acc: 0.0079\n",
      "Epoch 62/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5067 - acc: 0.0071\n",
      "Epoch 00062: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5067 - acc: 0.0071 - val_loss: 0.5061 - val_acc: 0.0068\n",
      "Epoch 63/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0070\n",
      "Epoch 00063: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5062 - acc: 0.0071 - val_loss: 0.5062 - val_acc: 0.0072\n",
      "Epoch 64/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0069\n",
      "Epoch 00064: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5062 - acc: 0.0069 - val_loss: 0.5060 - val_acc: 0.0068\n",
      "Epoch 65/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0071\n",
      "Epoch 00065: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5062 - acc: 0.0071 - val_loss: 0.5055 - val_acc: 0.0074\n",
      "Epoch 66/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0069\n",
      "Epoch 00066: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5061 - acc: 0.0069 - val_loss: 0.5048 - val_acc: 0.0069\n",
      "Epoch 67/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0070\n",
      "Epoch 00067: val_loss improved from 0.50461 to 0.50363, saving model to model/zibianmaquzao.hdf5\n",
      "1000/1000 [==============================] - 488s 488ms/step - loss: 0.5061 - acc: 0.0070 - val_loss: 0.5036 - val_acc: 0.0077\n",
      "Epoch 68/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0071\n",
      "Epoch 00068: val_loss did not improve\n",
      "1000/1000 [==============================] - 489s 489ms/step - loss: 0.5059 - acc: 0.0071 - val_loss: 0.5067 - val_acc: 0.0066\n",
      "Epoch 69/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0068\n",
      "Epoch 00069: val_loss did not improve\n",
      "1000/1000 [==============================] - 484s 484ms/step - loss: 0.5062 - acc: 0.0068 - val_loss: 0.5057 - val_acc: 0.0071\n",
      "Epoch 70/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0071\n",
      "Epoch 00070: val_loss did not improve\n",
      "1000/1000 [==============================] - 492s 492ms/step - loss: 0.5059 - acc: 0.0071 - val_loss: 0.5068 - val_acc: 0.0071\n",
      "Epoch 71/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5054 - acc: 0.0070\n",
      "Epoch 00071: val_loss did not improve\n",
      "1000/1000 [==============================] - 488s 488ms/step - loss: 0.5054 - acc: 0.0070 - val_loss: 0.5060 - val_acc: 0.0075\n",
      "Epoch 72/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0069\n",
      "Epoch 00072: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5061 - acc: 0.0069 - val_loss: 0.5051 - val_acc: 0.0069\n",
      "Epoch 73/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5064 - acc: 0.0069\n",
      "Epoch 00073: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5064 - acc: 0.0069 - val_loss: 0.5060 - val_acc: 0.0074\n",
      "Epoch 74/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5064 - acc: 0.0071\n",
      "Epoch 00074: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5064 - acc: 0.0071 - val_loss: 0.5056 - val_acc: 0.0074\n",
      "Epoch 75/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0070\n",
      "Epoch 00075: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5061 - acc: 0.0070 - val_loss: 0.5059 - val_acc: 0.0072\n",
      "Epoch 76/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5066 - acc: 0.0069\n",
      "Epoch 00076: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5066 - acc: 0.0069 - val_loss: 0.5046 - val_acc: 0.0070\n",
      "Epoch 77/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0071\n",
      "Epoch 00077: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5058 - acc: 0.0071 - val_loss: 0.5065 - val_acc: 0.0075\n",
      "Epoch 78/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0071\n",
      "Epoch 00078: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5061 - acc: 0.0071 - val_loss: 0.5070 - val_acc: 0.0066\n",
      "Epoch 79/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.0070\n",
      "Epoch 00079: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5063 - acc: 0.0070 - val_loss: 0.5060 - val_acc: 0.0065\n",
      "Epoch 80/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0070\n",
      "Epoch 00080: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5060 - acc: 0.0070 - val_loss: 0.5058 - val_acc: 0.0069\n",
      "Epoch 81/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0070\n",
      "Epoch 00081: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5061 - acc: 0.0070 - val_loss: 0.5057 - val_acc: 0.0072\n",
      "Epoch 82/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0071\n",
      "Epoch 00082: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5058 - acc: 0.0071 - val_loss: 0.5061 - val_acc: 0.0063\n",
      "Epoch 83/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0071\n",
      "Epoch 00083: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5059 - acc: 0.0071 - val_loss: 0.5063 - val_acc: 0.0064\n",
      "Epoch 84/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0070\n",
      "Epoch 00084: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5058 - acc: 0.0070 - val_loss: 0.5052 - val_acc: 0.0069\n",
      "Epoch 85/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.0069\n",
      "Epoch 00085: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5063 - acc: 0.0069 - val_loss: 0.5045 - val_acc: 0.0073\n",
      "Epoch 86/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0070\n",
      "Epoch 00086: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5061 - acc: 0.0070 - val_loss: 0.5071 - val_acc: 0.0076\n",
      "Epoch 87/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5065 - acc: 0.0066\n",
      "Epoch 00087: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5064 - acc: 0.0066 - val_loss: 0.5048 - val_acc: 0.0072\n",
      "Epoch 88/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0069\n",
      "Epoch 00088: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5059 - acc: 0.0069 - val_loss: 0.5072 - val_acc: 0.0071\n",
      "Epoch 89/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0070\n",
      "Epoch 00089: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5059 - acc: 0.0070 - val_loss: 0.5066 - val_acc: 0.0070\n",
      "Epoch 90/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0068\n",
      "Epoch 00090: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5055 - acc: 0.0068 - val_loss: 0.5050 - val_acc: 0.0072\n",
      "Epoch 91/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5057 - acc: 0.0070\n",
      "Epoch 00091: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5056 - acc: 0.0070 - val_loss: 0.5053 - val_acc: 0.0071\n",
      "Epoch 92/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0071\n",
      "Epoch 00092: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5061 - acc: 0.0071 - val_loss: 0.5042 - val_acc: 0.0075\n",
      "Epoch 93/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5054 - acc: 0.0072\n",
      "Epoch 00093: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5054 - acc: 0.0072 - val_loss: 0.5046 - val_acc: 0.0069\n",
      "Epoch 94/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0071\n",
      "Epoch 00094: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5060 - acc: 0.0071 - val_loss: 0.5063 - val_acc: 0.0072\n",
      "Epoch 95/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0071\n",
      "Epoch 00095: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5056 - acc: 0.0071 - val_loss: 0.5058 - val_acc: 0.0070\n",
      "Epoch 96/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.0068\n",
      "Epoch 00096: val_loss did not improve\n",
      "1000/1000 [==============================] - 489s 489ms/step - loss: 0.5063 - acc: 0.0068 - val_loss: 0.5066 - val_acc: 0.0068\n",
      "Epoch 97/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0070\n",
      "Epoch 00097: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5055 - acc: 0.0070 - val_loss: 0.5058 - val_acc: 0.0066\n",
      "Epoch 98/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0067\n",
      "Epoch 00098: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5060 - acc: 0.0067 - val_loss: 0.5072 - val_acc: 0.0065\n",
      "Epoch 99/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0070\n",
      "Epoch 00099: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5059 - acc: 0.0070 - val_loss: 0.5045 - val_acc: 0.0080\n",
      "Epoch 100/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0071\n",
      "Epoch 00100: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5059 - acc: 0.0071 - val_loss: 0.5056 - val_acc: 0.0067\n",
      "Epoch 101/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0069\n",
      "Epoch 00101: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5058 - acc: 0.0069 - val_loss: 0.5062 - val_acc: 0.0066\n",
      "Epoch 102/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0069\n",
      "Epoch 00102: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5056 - acc: 0.0069 - val_loss: 0.5057 - val_acc: 0.0066\n",
      "Epoch 103/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5057 - acc: 0.0069\n",
      "Epoch 00103: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5057 - acc: 0.0069 - val_loss: 0.5056 - val_acc: 0.0070\n",
      "Epoch 104/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5065 - acc: 0.0067\n",
      "Epoch 00104: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5065 - acc: 0.0067 - val_loss: 0.5074 - val_acc: 0.0072\n",
      "Epoch 105/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0072\n",
      "Epoch 00105: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5061 - acc: 0.0072 - val_loss: 0.5058 - val_acc: 0.0068\n",
      "Epoch 106/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0069\n",
      "Epoch 00106: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5062 - acc: 0.0069 - val_loss: 0.5058 - val_acc: 0.0072\n",
      "Epoch 107/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.0067\n",
      "Epoch 00107: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5063 - acc: 0.0067 - val_loss: 0.5069 - val_acc: 0.0070\n",
      "Epoch 108/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5054 - acc: 0.0070\n",
      "Epoch 00108: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5054 - acc: 0.0070 - val_loss: 0.5043 - val_acc: 0.0075\n",
      "Epoch 109/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0070\n",
      "Epoch 00109: val_loss did not improve\n",
      "1000/1000 [==============================] - 477s 477ms/step - loss: 0.5061 - acc: 0.0070 - val_loss: 0.5065 - val_acc: 0.0069\n",
      "Epoch 110/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0069\n",
      "Epoch 00110: val_loss did not improve\n",
      "1000/1000 [==============================] - 478s 478ms/step - loss: 0.5061 - acc: 0.0069 - val_loss: 0.5056 - val_acc: 0.0065\n",
      "Epoch 111/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5054 - acc: 0.0071\n",
      "Epoch 00111: val_loss did not improve\n",
      "1000/1000 [==============================] - 484s 484ms/step - loss: 0.5054 - acc: 0.0071 - val_loss: 0.5062 - val_acc: 0.0076\n",
      "Epoch 112/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0067\n",
      "Epoch 00112: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5059 - acc: 0.0067 - val_loss: 0.5059 - val_acc: 0.0071\n",
      "Epoch 113/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0070\n",
      "Epoch 00113: val_loss did not improve\n",
      "1000/1000 [==============================] - 478s 478ms/step - loss: 0.5058 - acc: 0.0070 - val_loss: 0.5050 - val_acc: 0.0069\n",
      "Epoch 114/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5054 - acc: 0.0071\n",
      "Epoch 00114: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5054 - acc: 0.0071 - val_loss: 0.5059 - val_acc: 0.0070\n",
      "Epoch 115/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0070\n",
      "Epoch 00115: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5062 - acc: 0.0070 - val_loss: 0.5052 - val_acc: 0.0066\n",
      "Epoch 116/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0068\n",
      "Epoch 00116: val_loss did not improve\n",
      "1000/1000 [==============================] - 478s 478ms/step - loss: 0.5055 - acc: 0.0068 - val_loss: 0.5063 - val_acc: 0.0073\n",
      "Epoch 117/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0072\n",
      "Epoch 00117: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5056 - acc: 0.0072 - val_loss: 0.5041 - val_acc: 0.0074\n",
      "Epoch 118/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0070\n",
      "Epoch 00118: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5056 - acc: 0.0070 - val_loss: 0.5052 - val_acc: 0.0070\n",
      "Epoch 119/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0070\n",
      "Epoch 00119: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5056 - acc: 0.0070 - val_loss: 0.5067 - val_acc: 0.0066\n",
      "Epoch 120/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0070\n",
      "Epoch 00120: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5058 - acc: 0.0070 - val_loss: 0.5063 - val_acc: 0.0073\n",
      "Epoch 121/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5053 - acc: 0.0069\n",
      "Epoch 00121: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5053 - acc: 0.0069 - val_loss: 0.5062 - val_acc: 0.0069\n",
      "Epoch 122/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0074\n",
      "Epoch 00122: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5062 - acc: 0.0074 - val_loss: 0.5041 - val_acc: 0.0074\n",
      "Epoch 123/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0071\n",
      "Epoch 00123: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5055 - acc: 0.0071 - val_loss: 0.5060 - val_acc: 0.0069\n",
      "Epoch 124/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0070\n",
      "Epoch 00124: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5062 - acc: 0.0070 - val_loss: 0.5074 - val_acc: 0.0072\n",
      "Epoch 125/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0070\n",
      "Epoch 00125: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5056 - acc: 0.0070 - val_loss: 0.5061 - val_acc: 0.0071\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 126/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0071\n",
      "Epoch 00126: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5055 - acc: 0.0071 - val_loss: 0.5045 - val_acc: 0.0071\n",
      "Epoch 127/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0071\n",
      "Epoch 00127: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5062 - acc: 0.0071 - val_loss: 0.5063 - val_acc: 0.0068\n",
      "Epoch 128/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0071\n",
      "Epoch 00128: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5058 - acc: 0.0071 - val_loss: 0.5060 - val_acc: 0.0066\n",
      "Epoch 129/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5057 - acc: 0.0070\n",
      "Epoch 00129: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5057 - acc: 0.0070 - val_loss: 0.5072 - val_acc: 0.0072\n",
      "Epoch 130/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0073\n",
      "Epoch 00130: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5058 - acc: 0.0073 - val_loss: 0.5057 - val_acc: 0.0067\n",
      "Epoch 131/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0071\n",
      "Epoch 00131: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5056 - acc: 0.0071 - val_loss: 0.5047 - val_acc: 0.0070\n",
      "Epoch 132/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0069\n",
      "Epoch 00132: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5058 - acc: 0.0069 - val_loss: 0.5060 - val_acc: 0.0070\n",
      "Epoch 133/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5054 - acc: 0.0070\n",
      "Epoch 00133: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5054 - acc: 0.0070 - val_loss: 0.5049 - val_acc: 0.0066\n",
      "Epoch 134/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5057 - acc: 0.0071\n",
      "Epoch 00134: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5057 - acc: 0.0071 - val_loss: 0.5064 - val_acc: 0.0073\n",
      "Epoch 135/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0069\n",
      "Epoch 00135: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5056 - acc: 0.0069 - val_loss: 0.5059 - val_acc: 0.0071\n",
      "Epoch 136/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0071\n",
      "Epoch 00136: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5056 - acc: 0.0071 - val_loss: 0.5060 - val_acc: 0.0067\n",
      "Epoch 137/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5057 - acc: 0.0069\n",
      "Epoch 00137: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5058 - acc: 0.0069 - val_loss: 0.5064 - val_acc: 0.0067\n",
      "Epoch 138/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5053 - acc: 0.0070\n",
      "Epoch 00138: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5053 - acc: 0.0070 - val_loss: 0.5060 - val_acc: 0.0071\n",
      "Epoch 139/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0070\n",
      "Epoch 00139: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5062 - acc: 0.0070 - val_loss: 0.5050 - val_acc: 0.0076\n",
      "Epoch 140/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5052 - acc: 0.0067\n",
      "Epoch 00140: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5052 - acc: 0.0067 - val_loss: 0.5057 - val_acc: 0.0068\n",
      "Epoch 141/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0070\n",
      "Epoch 00141: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5055 - acc: 0.0070 - val_loss: 0.5054 - val_acc: 0.0070\n",
      "Epoch 142/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0070\n",
      "Epoch 00142: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5058 - acc: 0.0070 - val_loss: 0.5072 - val_acc: 0.0067\n",
      "Epoch 143/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5053 - acc: 0.0068\n",
      "Epoch 00143: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5053 - acc: 0.0068 - val_loss: 0.5060 - val_acc: 0.0068\n",
      "Epoch 144/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0070\n",
      "Epoch 00144: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5060 - acc: 0.0070 - val_loss: 0.5081 - val_acc: 0.0071\n",
      "Epoch 145/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0071\n",
      "Epoch 00145: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5059 - acc: 0.0071 - val_loss: 0.5062 - val_acc: 0.0067\n",
      "Epoch 146/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5061 - acc: 0.0070\n",
      "Epoch 00146: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5061 - acc: 0.0070 - val_loss: 0.5061 - val_acc: 0.0070\n",
      "Epoch 147/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5054 - acc: 0.0070\n",
      "Epoch 00147: val_loss did not improve\n",
      "1000/1000 [==============================] - 483s 483ms/step - loss: 0.5054 - acc: 0.0070 - val_loss: 0.5043 - val_acc: 0.0080\n",
      "Epoch 148/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5051 - acc: 0.0070\n",
      "Epoch 00148: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5051 - acc: 0.0070 - val_loss: 0.5063 - val_acc: 0.0069\n",
      "Epoch 149/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5053 - acc: 0.0070\n",
      "Epoch 00149: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5053 - acc: 0.0070 - val_loss: 0.5049 - val_acc: 0.0073\n",
      "Epoch 150/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5053 - acc: 0.0069\n",
      "Epoch 00150: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5053 - acc: 0.0069 - val_loss: 0.5059 - val_acc: 0.0071\n",
      "Epoch 151/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0070\n",
      "Epoch 00151: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5059 - acc: 0.0070 - val_loss: 0.5050 - val_acc: 0.0074\n",
      "Epoch 152/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5051 - acc: 0.0072\n",
      "Epoch 00152: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5051 - acc: 0.0072 - val_loss: 0.5062 - val_acc: 0.0074\n",
      "Epoch 153/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0070\n",
      "Epoch 00153: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5056 - acc: 0.0070 - val_loss: 0.5046 - val_acc: 0.0078\n",
      "Epoch 154/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5059 - acc: 0.0070\n",
      "Epoch 00154: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5059 - acc: 0.0070 - val_loss: 0.5053 - val_acc: 0.0074\n",
      "Epoch 155/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0072\n",
      "Epoch 00155: val_loss did not improve\n",
      "1000/1000 [==============================] - 479s 479ms/step - loss: 0.5055 - acc: 0.0072 - val_loss: 0.5070 - val_acc: 0.0066\n",
      "Epoch 156/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5062 - acc: 0.0069\n",
      "Epoch 00156: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5062 - acc: 0.0069 - val_loss: 0.5055 - val_acc: 0.0066\n",
      "Epoch 157/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.0069\n",
      "Epoch 00157: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5060 - acc: 0.0070 - val_loss: 0.5055 - val_acc: 0.0065\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 158/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5054 - acc: 0.0069\n",
      "Epoch 00158: val_loss did not improve\n",
      "1000/1000 [==============================] - 478s 478ms/step - loss: 0.5054 - acc: 0.0069 - val_loss: 0.5056 - val_acc: 0.0075\n",
      "Epoch 159/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0070\n",
      "Epoch 00159: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5056 - acc: 0.0070 - val_loss: 0.5062 - val_acc: 0.0069\n",
      "Epoch 160/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0068\n",
      "Epoch 00160: val_loss did not improve\n",
      "1000/1000 [==============================] - 478s 478ms/step - loss: 0.5056 - acc: 0.0068 - val_loss: 0.5054 - val_acc: 0.0074\n",
      "Epoch 161/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0070\n",
      "Epoch 00161: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5055 - acc: 0.0070 - val_loss: 0.5055 - val_acc: 0.0068\n",
      "Epoch 162/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5055 - acc: 0.0072\n",
      "Epoch 00162: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5055 - acc: 0.0072 - val_loss: 0.5058 - val_acc: 0.0069\n",
      "Epoch 163/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5056 - acc: 0.0071\n",
      "Epoch 00163: val_loss did not improve\n",
      "1000/1000 [==============================] - 482s 482ms/step - loss: 0.5056 - acc: 0.0071 - val_loss: 0.5051 - val_acc: 0.0069\n",
      "Epoch 164/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5053 - acc: 0.0071\n",
      "Epoch 00164: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5053 - acc: 0.0071 - val_loss: 0.5042 - val_acc: 0.0071\n",
      "Epoch 165/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5050 - acc: 0.0072\n",
      "Epoch 00165: val_loss did not improve\n",
      "1000/1000 [==============================] - 481s 481ms/step - loss: 0.5050 - acc: 0.0072 - val_loss: 0.5047 - val_acc: 0.0072\n",
      "Epoch 166/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5052 - acc: 0.0071\n",
      "Epoch 00166: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5052 - acc: 0.0071 - val_loss: 0.5062 - val_acc: 0.0064\n",
      "Epoch 167/200\n",
      " 999/1000 [============================>.] - ETA: 0s - loss: 0.5058 - acc: 0.0068\n",
      "Epoch 00167: val_loss did not improve\n",
      "1000/1000 [==============================] - 480s 480ms/step - loss: 0.5058 - acc: 0.0068 - val_loss: 0.5062 - val_acc: 0.0066\n",
      "Epoch 168/200\n",
      " 974/1000 [============================>.] - ETA: 11s - loss: 0.5056 - acc: 0.0070"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process ForkPoolWorker-1997:\n",
      "Process ForkPoolWorker-1998:\n",
      "Process ForkPoolWorker-1999:\n",
      "Process ForkPoolWorker-2000:\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",
      "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 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/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\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 342, in get\n",
      "    with self._rlock:\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/multiprocessing/pool.py\", line 108, in worker\n",
      "    task = get()\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/synchronize.py\", line 96, in __enter__\n",
      "    return self._semlock.__enter__()\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/queues.py\", line 342, in get\n",
      "    with self._rlock:\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/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 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "KeyboardInterrupt\n",
      "KeyboardInterrupt\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "  File \"<ipython-input-4-0055c86dc8a0>\", line 34, in __getitem__\n",
      "    line=(0,20), point=(0,500), color=(0,255), bg=tuple([random.randint(0,255) for _ in range(3)]))\n",
      "  File \"<ipython-input-2-e21954efbb17>\", line 82, in gen_captcha\n",
      "    return img.resize((240,80), Image.BILINEAR), img2.resize((240,80), Image.BILINEAR)\n",
      "KeyboardInterrupt\n",
      "  File \"/home/python/anaconda3/envs/dl_nlp/lib/python3.5/site-packages/PIL/Image.py\", line 1806, in resize\n",
      "    return self._new(self.im.resize(size, resample, box))\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-9-9b84cf229d71>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mvalid_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCaptchaSequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcharacters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# (characters, batch_size=128, steps=100)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m model.fit_generator(train_data, epochs=200, validation_data=valid_data, workers=4, use_multiprocessing=True,\n\u001b[0;32m---> 20\u001b[0;31m                    callbacks=[checkpoint])\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 keras.callbacks import TensorBoard, ModelCheckpoint\n",
    "\n",
    "checkpoint = ModelCheckpoint(filepath='model/zibianmaquzao.hdf5',\n",
    "                             verbose=1, save_weights_only=True, save_best_only=True)\n",
    "# x_train, x_train_noisy = get_data(5000)\n",
    "# x_test, x_test_noisy = get_data(1000)\n",
    "\n",
    "# model.load_weights('model/zibianmaquzao.hdf5')\n",
    "# model.fit(x_train_noisy, x_train,\n",
    "#                 epochs=100,\n",
    "#                 batch_size=128,\n",
    "#                 shuffle=True,\n",
    "#                 validation_data=(x_test_noisy, x_test),\n",
    "#                 callbacks=[checkpoint])\n",
    "\n",
    "model.load_weights('model/zibianmaquzao.hdf5')\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",
    "model.fit_generator(train_data, epochs=200, validation_data=valid_data, workers=4, use_multiprocessing=True,\n",
    "                   callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.load_weights('model/zibianmaquzao.hdf5')\n",
    "x_test, x_test_noisy = get_data(20)\n",
    "pre = model.predict(x_test_noisy[:20])\n",
    "plt.figure(figsize=(80,10))\n",
    "for i in range(6):\n",
    "    plt.subplot(2,6,i+1)\n",
    "    plt.imshow( x_test_noisy[i])\n",
    "    plt.subplot(2,6,i+7)\n",
    "    plt.imshow(pre[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "da (10, 80, 240, 3)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 5760x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import glob\n",
    "import re\n",
    "img_arr = []\n",
    "path = '/home/python/lishimin/linuxPro/captcha_pro/FileInfo0508_2/*.jpg'\n",
    "# path = '/data/esa_sdk/gan/english/*.jpg'\n",
    "for file in glob.glob(path)[:10]: \n",
    "    label = file.split('_')[-1][:-4]    \n",
    "    if re.search('[a-z]', label) != None:\n",
    "#     if label.isdigit() and len(label)==4:\n",
    "#         print(label)\n",
    "        img = Image.open(file)\n",
    "#         print(img.size)\n",
    "        img = img.resize((240, 80), Image.BILINEAR) \n",
    "        img = img.convert('RGB')\n",
    "#         print(img.size)\n",
    "        arr = np.array(img)/255.0\n",
    "#         arr = arr.astype('float32') / 255.\n",
    "#         arr = np.reshape(arr, (height, width, 1))\n",
    "        img_arr.append(arr)    \n",
    "da = np.array(img_arr)\n",
    "print('da',da.shape)\n",
    "pre = model.predict(da)\n",
    "plt.figure(figsize=(80,10))\n",
    "for i in range(6):\n",
    "    plt.subplot(2,6,i+1)\n",
    "    plt.imshow( da[i])\n",
    "    plt.subplot(2,6,i+7)\n",
    "    plt.imshow(pre[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_7 (InputLayer)         (None, 80, 240, 1)        0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, 80, 240, 32)       320       \n",
      "_________________________________________________________________\n",
      "pool1 (MaxPooling2D)         (None, 40, 120, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2 (Conv2D)               (None, 40, 120, 32)       9248      \n",
      "_________________________________________________________________\n",
      "pool2 (MaxPooling2D)         (None, 20, 60, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv3 (Conv2D)               (None, 20, 60, 32)        9248      \n",
      "_________________________________________________________________\n",
      "upsamp1 (UpSampling2D)       (None, 40, 120, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv4 (Conv2D)               (None, 40, 120, 32)       9248      \n",
      "_________________________________________________________________\n",
      "upsamp2 (UpSampling2D)       (None, 80, 240, 32)       0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 80, 240, 32)       1056      \n",
      "_________________________________________________________________\n",
      "conv5 (Conv2D)               (None, 80, 240, 1)        289       \n",
      "=================================================================\n",
      "Total params: 29,409\n",
      "Trainable params: 29,409\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, UpSampling2D\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adadelta, Adam\n",
    "width = 240   # 60 * 4\n",
    "height = 80\n",
    "input_img = Input(shape=(height,width,1))\n",
    "\n",
    "x = Convolution2D(32, 3,strides=(1,1), activation='relu', padding='same', name='conv1')(input_img)\n",
    "x = MaxPooling2D((2,2), padding='same', name='pool1')(x)\n",
    " \n",
    "x = Convolution2D(32,3,strides=(1,1), activation='relu', padding='same', name='conv2')(x)\n",
    "encoded = MaxPooling2D((2,2), padding='same', name='pool2')(x)\n",
    "\n",
    "x = Convolution2D(32,3,strides=(1,1),activation='relu', padding='same', name='conv3')(encoded)\n",
    "x = UpSampling2D((2,2), name='upsamp1')(x)\n",
    " \n",
    "x = Convolution2D(32,3,strides=(1,1), padding='same', activation='relu', name='conv4')(x)\n",
    "x = UpSampling2D((2,2), name='upsamp2')(x)\n",
    "# x = Dense(32, activation='relu')(x)\n",
    "decoded = Convolution2D(1,3,strides=(1,1),activation='sigmoid', padding='same', name='conv5')(x)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)\n",
    "autoencoder.summary()\n",
    "opt = Adam(lr=0.001) # Adadelta(lr=0.005, rho=0.95, epsilon=1e-06)\n",
    "\n",
    "autoencoder.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "from keras.callbacks import TensorBoard, ModelCheckpoint\n",
    "\n",
    "checkpoint = ModelCheckpoint(filepath='model/weight.epoch{epoch:03d}-val_loss{val_loss:.04f}-val_acc{val_acc:.04f}.hdf5',\n",
    "                             verbose=1, save_weights_only=True, save_best_only=True)\n",
    "# autoencoder.load_weights('model/addcmweight.epoch158-val_loss0.03-val_acc0.94.hdf5')\n",
    "# autoencoder.fit(x_train_noisy, x_train,\n",
    "#                 epochs=200,\n",
    "#                 batch_size=128,\n",
    "#                 shuffle=True,\n",
    "#                 validation_data=(x_test_noisy, x_test),\n",
    "#                 callbacks=[checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " \n",
       " \n",
       "        [[[  3.22995692e-01],\n",
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       " \n",
       "         [[  1.64419815e-01],\n",
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       " \n",
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       " \n",
       " \n",
       "        [[[  4.48469147e-02],\n",
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       "          [ -5.88997267e-02]],\n",
       " \n",
       "         [[ -8.02091509e-02],\n",
       "          [  1.92373902e-01],\n",
       "          [ -1.69473380e-01],\n",
       "          [ -1.07479021e-01],\n",
       "          [ -1.17388256e-02],\n",
       "          [ -1.90735862e-01],\n",
       "          [  3.79042864e-01],\n",
       "          [ -2.57829037e-02],\n",
       "          [ -1.75433010e-01],\n",
       "          [  2.26696000e-01],\n",
       "          [  1.44554943e-01],\n",
       "          [ -3.78732681e-02],\n",
       "          [  1.89648181e-01],\n",
       "          [  2.87158843e-02],\n",
       "          [  1.01052754e-01],\n",
       "          [  1.54116657e-02],\n",
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       "          [ -2.17473894e-01],\n",
       "          [ -5.94084524e-02],\n",
       "          [ -1.85162678e-01],\n",
       "          [ -2.32067588e-03],\n",
       "          [  1.10475264e-01],\n",
       "          [  1.15396939e-01],\n",
       "          [ -2.82163262e-01],\n",
       "          [  6.65283501e-02],\n",
       "          [ -7.34883687e-03],\n",
       "          [  2.61833489e-01],\n",
       "          [ -3.03260777e-02]],\n",
       " \n",
       "         [[ -7.94551298e-02],\n",
       "          [ -1.00004353e-01],\n",
       "          [ -1.05567850e-01],\n",
       "          [  1.34841539e-02],\n",
       "          [ -3.24415565e-02],\n",
       "          [ -1.18692607e-01],\n",
       "          [  3.29127342e-01],\n",
       "          [ -8.83445665e-02],\n",
       "          [  1.35647267e-01],\n",
       "          [ -1.42086213e-02],\n",
       "          [  1.81110110e-02],\n",
       "          [  1.47897890e-02],\n",
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       "          [  4.45766896e-02],\n",
       "          [ -2.73148138e-02],\n",
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       "          [  7.23792333e-03],\n",
       "          [  1.99012890e-01],\n",
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       "          [ -7.56523758e-02],\n",
       "          [ -1.75852135e-01],\n",
       "          [ -7.92524498e-03],\n",
       "          [ -2.93286494e-03],\n",
       "          [  1.21940948e-01],\n",
       "          [ -1.88333541e-02],\n",
       "          [  6.49412721e-02],\n",
       "          [ -1.38626918e-02],\n",
       "          [  2.43606046e-02]]]], dtype=float32),\n",
       " array([ 0.74692518], dtype=float32)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.load_weights('model/autoencoder_test_saveweight.h5', by_name=True)\n",
    "autoencoder.layers[10].get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " \n",
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       "             1.64700504e-02,   6.25911122e-03]],\n",
       " \n",
       "         [[  3.25396657e-02,  -6.85974807e-02,   1.83412712e-02,\n",
       "             6.70518726e-02,  -1.14333354e-01,  -6.91502914e-02,\n",
       "            -2.01184372e-03,   8.63260962e-03,  -7.94945061e-02,\n",
       "             2.02049967e-02,  -2.39307061e-02,   8.04044190e-04,\n",
       "            -8.64433497e-02,  -9.90194734e-03,  -3.93370055e-02,\n",
       "            -4.15951217e-04,  -6.11635037e-02,  -3.47021851e-04,\n",
       "            -2.73259245e-02,   1.27217382e-01,   6.15744991e-03,\n",
       "            -2.63002664e-02,   6.22170091e-01,   4.68930490e-02,\n",
       "             7.05144228e-03,  -1.89197471e-03,  -1.05472226e-02,\n",
       "            -7.54342135e-03,   1.78553909e-03,  -5.99147715e-02,\n",
       "             1.62178110e-02,  -3.88858356e-02]]],\n",
       " \n",
       " \n",
       "        [[[ -2.63509527e-02,  -2.12623719e-02,   5.27150668e-02,\n",
       "            -9.16644707e-02,   8.40872943e-01,   1.04212426e-02,\n",
       "             1.26547188e-01,  -8.70469119e-03,  -1.92954969e-02,\n",
       "             1.18961670e-01,  -2.45489287e+00,  -8.02818406e-03,\n",
       "             1.06230766e-01,  -2.89838389e-03,   9.58794262e-04,\n",
       "            -6.33039623e-02,   6.94660749e-03,  -1.00672750e+01,\n",
       "             9.38649774e-02,   7.47567177e-01,   4.47106920e-02,\n",
       "            -3.13322842e-02,  -9.43726953e-03,  -7.73693919e-02,\n",
       "            -9.49168578e-03,   4.04074229e-03,   1.72523540e-02,\n",
       "            -3.51060107e-02,  -1.41131980e-02,  -2.53312774e-02,\n",
       "            -6.36487752e-02,  -1.02308527e-01]],\n",
       " \n",
       "         [[ -5.77700622e-02,  -3.62511352e-02,  -2.41477996e-01,\n",
       "            -2.55447102e+01,   1.72392398e-01,  -1.82124972e-02,\n",
       "             1.03064947e-01,   1.77840221e+00,  -2.10542583e+01,\n",
       "             9.72616486e-03,  -9.76539898e+00,  -3.65493596e-02,\n",
       "            -1.27252638e-02,  -5.17697893e-02,   1.64317206e-01,\n",
       "            -1.90025438e-02,   6.84394687e-02,   1.58764422e+00,\n",
       "            -3.97882285e-03,  -1.30425513e-01,   1.32419646e+00,\n",
       "             1.63111314e-01,  -2.61361916e-02,  -5.02715707e-02,\n",
       "             3.24662030e-02,   4.72083921e-03,   4.34467159e-02,\n",
       "            -1.14966659e-02,   1.79741178e-02,  -3.59967835e-02,\n",
       "            -1.36932880e-01,  -1.01782694e-01]],\n",
       " \n",
       "         [[ -1.21197835e-01,  -5.22145890e-02,   4.74970900e-02,\n",
       "            -2.75611057e+01,  -4.95225191e-02,  -1.14125781e-01,\n",
       "             1.45364990e-02,  -1.41485710e+01,   2.42911682e-01,\n",
       "            -3.56069370e-03,  -6.58181636e-03,   1.80039555e-02,\n",
       "            -1.03990801e-01,  -5.89074753e-03,  -1.36719540e-01,\n",
       "            -1.76281810e+00,  -1.87068731e-01,  -6.96087861e-03,\n",
       "            -1.39926389e-01,   1.22549072e-01,   7.81905092e-03,\n",
       "             6.96479678e-02,  -1.97307396e+01,  -8.03062245e-02,\n",
       "            -8.37905426e-03,  -1.52374003e-02,   5.34028448e-02,\n",
       "             2.36724596e-03,   1.49075115e+00,   3.86683233e-02,\n",
       "            -1.25613973e-01,   1.98410779e-01]]],\n",
       " \n",
       " \n",
       "        [[[  3.00068520e-02,  -4.24975865e-02,   3.37531976e-02,\n",
       "             4.24000919e-02,  -2.69292798e-02,   4.06265706e-02,\n",
       "            -3.36644053e-02,   1.63591816e-03,   7.55347982e-02,\n",
       "             5.14076045e-03,  -9.12017226e-01,  -1.32807180e-01,\n",
       "             4.62271683e-02,  -4.64537852e-02,   3.10397125e-03,\n",
       "             1.02774389e-02,  -5.55254072e-02,  -6.56723138e-03,\n",
       "             3.28822955e-02,  -5.16428314e-02,  -2.24153069e-03,\n",
       "            -2.45668367e-02,  -1.69695374e-02,   1.68647721e-01,\n",
       "             5.69529179e-03,  -1.50333811e-02,  -2.10651718e-02,\n",
       "             5.35986610e-02,  -3.78852966e-03,   3.16350162e-02,\n",
       "            -1.45525374e-02,   3.48507874e-02]],\n",
       " \n",
       "         [[ -9.50206220e-01,  -5.81940338e-02,  -5.55039942e-02,\n",
       "             1.10973679e-01,   1.62198511e-03,   1.37478545e-01,\n",
       "            -1.06569268e-01,   8.31291452e-03,   2.10878551e-02,\n",
       "            -7.62916682e-03,  -8.08357522e-02,  -1.49141457e-02,\n",
       "            -4.70423289e-02,  -1.52571544e-01,   8.70395824e-02,\n",
       "            -8.37382004e-02,  -1.22130312e-01,   2.51776772e-03,\n",
       "            -3.78592238e-02,   6.92776516e-02,   2.12344080e-02,\n",
       "            -2.94342777e-03,  -2.39133760e-02,   5.92904538e-02,\n",
       "             6.16498059e-03,   1.73482549e+00,   5.51204860e-01,\n",
       "             1.57443866e-01,   5.17848879e-03,   9.17110778e-03,\n",
       "             2.25306470e-02,   9.12111904e-03]],\n",
       " \n",
       "         [[ -7.53243387e-01,  -4.11989316e-02,   4.77678282e-03,\n",
       "             7.70632252e-02,  -1.66982226e-03,  -3.05161644e-02,\n",
       "            -6.82743862e-02,  -1.12019256e-02,  -1.00036703e-01,\n",
       "             5.83916809e-03,  -4.67572547e-02,   1.64981991e-01,\n",
       "            -2.77464911e-02,  -4.21278328e-02,  -9.03179646e-02,\n",
       "             2.91885920e-02,   5.25256217e-01,  -1.93914806e-03,\n",
       "            -2.49020811e-02,  -1.10623082e-02,   1.60591733e-02,\n",
       "             3.36800498e-04,  -6.41253293e-02,  -1.12841884e-02,\n",
       "            -7.49013247e-03,   2.70188437e-03,   4.47037756e-01,\n",
       "             1.65108480e-02,   1.94698144e-02,   1.64876115e-02,\n",
       "             1.13720763e-02,  -4.21838388e-02]]]], dtype=float32),\n",
       " array([  1.13214776e-01,   2.56802380e-01,   8.89502615e-02,\n",
       "         -8.48836824e-02,  -9.45339262e-01,   8.34122673e-02,\n",
       "         -5.36020496e-04,  -1.72132802e+00,  -1.18848741e-01,\n",
       "         -6.00708984e-02,   1.05230629e-01,   1.14275040e-02,\n",
       "         -2.96836197e-01,   3.24674964e-01,   4.28024121e-03,\n",
       "          1.40428782e-01,  -3.93324494e-01,  -1.55139017e+00,\n",
       "          8.55848268e-02,  -1.08331251e+00,  -1.31586742e+00,\n",
       "          1.59856817e-03,  -5.56798398e-01,  -2.11576954e-01,\n",
       "         -1.59321058e+00,  -1.68470538e+00,  -9.70036268e-01,\n",
       "          2.27942318e-03,  -1.45711052e+00,   1.16183765e-01,\n",
       "          2.87980765e-01,   6.76769670e-03], dtype=float32)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# autoencoder.load_weights('model/weight.epoch196-val_loss0.0274-val_acc0.9450.hdf5', by_name=False) #, by_name=True , skip_mismatch=True\n",
    "# autoencoder.save_weights('model/autoencoder_test_saveweight.h5')\n",
    "autoencoder.load_weights('model/autoencoder_test_saveweight.h5', by_name=True)\n",
    "autoencoder.layers[1].get_weights()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11b8\n",
      "pliu\n",
      "7b35\n",
      "11b8\n",
      "70b9\n",
      "8qxp\n",
      "jce5\n",
      "b552\n",
      "opn2\n",
      "8b63\n",
      "kxxy\n",
      "00b8\n",
      "8fts\n",
      "4b07\n",
      "q36z\n",
      "swwo\n",
      "b957\n",
      "548b\n",
      "wmxt\n",
      "70b9\n",
      "b463\n",
      "1l8g\n",
      "(22, 40, 100, 1)\n",
      "22\n"
     ]
    }
   ],
   "source": [
    "# da = np.zeros(shape=(4,height, width, 1))\n",
    "autoencoder.load_weights('model/weight.epoch196-val_loss0.0274-val_acc0.9450.hdf5')\n",
    "import glob\n",
    "import re\n",
    "img_arr = []\n",
    "for file in glob.glob('../FileInfo/*.jpg'):\n",
    "    label = file.split('_')[-1][:-4]    \n",
    "    if re.search('[a-z]', label) != None:\n",
    "#     if label.isdigit() and len(label)==4:\n",
    "        print(label)\n",
    "        img = Image.open(file)\n",
    "#         print(img.size)\n",
    "        img = img.resize((width, height), Image.BILINEAR) \n",
    "        img = img.convert('L')\n",
    "#         print(img.size)\n",
    "        arr = np.array(img)\n",
    "        arr = arr.astype('float32') / 255.\n",
    "        arr = np.reshape(arr, (height, width, 1))\n",
    "        img_arr.append(arr)\n",
    "da = np.array(img_arr)\n",
    "\n",
    "# da = np.zeros(shape=x_test_noisy.shape)\n",
    "# da = x_test_noisy\n",
    "da.shape\n",
    "pre1 = autoencoder.predict(da)\n",
    "# pre1 = da[0]\n",
    "pre1 = pre1*255\n",
    "pre1 = pre1.astype(np.uint8)\n",
    "print(pre1.shape)\n",
    "print(len(pre1))\n",
    "pre1 = np.reshape(pre1,(len(pre1),40, 100))\n",
    "# print(pre1)\n",
    "# img_pre2 = Image.fromarray(pre1)\n",
    "# img_pre = img_pre.convert('RGB')\n",
    "# img_pre2.save('test31.jpg')\n",
    "# img_pre2.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2755"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "id = 0\n",
    "n = 5 # how many digits we will display\n",
    "plt.figure(figsize=(30, 4))\n",
    "for i in range(1,6):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i)\n",
    "#     plt.imshow(x_test_noisy[i+id].reshape(40,100))\n",
    "    plt.imshow(da[i+id].reshape(40,100))\n",
    "    plt.gray()\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + n)\n",
    "#     plt.imshow(x_test[i+id].reshape(40,100))\n",
    "    plt.imshow(pre1[i+id].reshape(40,100))\n",
    "    plt.gray()\n",
    "#     ax.get_xaxis().set_visible(False)\n",
    "#     ax.get_yaxis().set_visible(False)\n",
    "plt.show()\n",
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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