Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import loadmat\n",
    "import os\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tqdm import tqdm_notebook\n",
    "import h5py\n",
    "import time\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_age(taken, dob):\n",
    "    \"\"\"\n",
    "    Calculate age of the image based on the date of birth and when the image is taken\n",
    "    \"\"\"\n",
    "    birth = datetime.fromordinal(max(int(dob) - 366, 1))\n",
    "    \n",
    "    if birth.month < 7:\n",
    "        return taken - birth.year\n",
    "    else:\n",
    "        return taken - birth.year - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(data_dir, dataset):\n",
    "    \"\"\"\n",
    "    Loads infomration about the images and filters the images based on quality measures like multiple \n",
    "    people in the image and bougus ages etc.\n",
    "    \"\"\"\n",
    "    \n",
    "    # load from mat file\n",
    "    meta = loadmat(os.path.join(data_dir, \"{}.mat\".format(dataset)))\n",
    "    \n",
    "    # extract information from the metadata-dict\n",
    "    full_path = meta[dataset][0, 0][\"full_path\"][0]\n",
    "    face_score = meta[dataset][0, 0][\"face_score\"][0]\n",
    "    second_face_score = meta[dataset][0, 0][\"second_face_score\"][0]\n",
    "    dob = meta[dataset][0, 0][\"dob\"][0]\n",
    "    photo_taken = meta[dataset][0, 0][\"photo_taken\"][0]\n",
    "    name = meta[dataset][0, 0][\"name\"][0]\n",
    "    gender = meta[dataset][0, 0]['gender'][0]\n",
    "    \n",
    "    # Calculate ages\n",
    "    ages = np.array([calculate_age(photo_taken[i], dob[i]) for i in range(len(dob))])\n",
    "    \n",
    "    # Loop over the images to find what images to delete\n",
    "    indecies_to_delete = []\n",
    "    for i, age in enumerate(ages):\n",
    "        # if age is higher than 100 or less than 0, mark the image for removal\n",
    "        if age > 100 or age < 0:\n",
    "            indecies_to_delete.append(i)\n",
    "        \n",
    "        # If the face detection score is lower than 3, remove the image\n",
    "        elif face_score[i] < 1.5:\n",
    "            indecies_to_delete.append(i)\n",
    "            \n",
    "        # If we have more than 1 face in the image, remove the image\n",
    "        elif not np.isnan(second_face_score[i]):\n",
    "            indecies_to_delete.append(i)\n",
    "        \n",
    "        # If we do not have the gender information, remove the image\n",
    "        elif np.isnan(gender[i]):\n",
    "            indecies_to_delete.append(i)\n",
    "            \n",
    "        #if face_score[i] < 0:\n",
    "            #print(full_path[i], face_score[i])\n",
    "            \n",
    "    print(\"Removing\", len(indecies_to_delete), \"images\")\n",
    "    \n",
    "    # Remove the marked images\n",
    "    ages = np.delete(ages, indecies_to_delete)\n",
    "    full_path = np.delete(full_path, indecies_to_delete)\n",
    "    \n",
    "    gender = np.delete(gender, indecies_to_delete)\n",
    "     \n",
    "    images = []\n",
    "    for img_path in tqdm_notebook(full_path):\n",
    "        images.append(img_path[0])\n",
    "    \n",
    "    images = np.array(images)\n",
    "    \n",
    "    return images, ages, gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_images(data_dir, image_paths, image_shape):\n",
    "    \"\"\"\n",
    "    Loads the images from disk and reshape to a standard format of image_shape\n",
    "    \"\"\"\n",
    "    images = []\n",
    "    num_images = len(image_paths)\n",
    "    \n",
    "    for i, image_path in tqdm_notebook(enumerate(image_paths), total=num_images, leave=False):\n",
    "    \n",
    "        try:\n",
    "            # Load image\n",
    "            loaded_image = image.load_img(os.path.join(data_dir, image_path), target_size=image_shape)\n",
    "            \n",
    "            # Convert PIL image to numpy ndarray\n",
    "            loaded_image = image.img_to_array(loaded_image).astype(np.uint8)\n",
    "            #print(loaded_image)\n",
    "\n",
    "            # Add another dimension (Add batch dimension)\n",
    "            #loaded_image = np.expand_dims(loaded_image, axis=0).astype(np.uint8)\n",
    "            #print(loaded_image)\n",
    "            \n",
    "            images.append(loaded_image)\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(\"Error:\", i, e)\n",
    "    images = np.array(images)\n",
    "\n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_chunk_generator(images, chunksize):\n",
    "    \"\"\"Create a simple generator that iterates over blocks of images\"\"\"\n",
    "    for i in range(0, images.shape[0], chunksize):\n",
    "        yield images[i:i + chunksize]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wiki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 27098 images\n"
     ]
    },
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      "\n"
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     "text": [
      "\n"
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   ],
   "source": [
    "w_images, w_ages, w_gender = load_data(\"data/wiki_crop\", \"wiki\")\n",
    "image_shape = (64, 64, 3)\n",
    "\n",
    "w_num_imgs = w_images.shape[0]\n",
    "chunksize = w_num_imgs // 120\n",
    "\n",
    "chunk_generator = image_chunk_generator(w_images, chunksize)\n",
    "\n",
    "w_finnished_images = []\n",
    "\n",
    "for image_chunk in tqdm_notebook(chunk_generator, total = 121, desc=\"Image chunks\"):\n",
    "    #loaded_images = load_images(\"data/imdb_crop\", image_chunk, (image_shape[0], image_shape[1]))\n",
    "    loaded_images = load_images(\"data/wiki_crop\", image_chunk, (image_shape[0], image_shape[1]))\n",
    "    w_finnished_images.append(loaded_images)\n",
    "\n",
    "w_loaded_images = np.concatenate(w_finnished_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59\n",
      "1.0\n",
      "(35230, 64, 64, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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TbcgYZ4wpCPU1S2x44YLkTuvbJLrt8GZtCuphHIeOREOdPR2Ts42IKAt7AvY+YhrrRkP2N3bu2W36kOuenNLXUiuJLo4pm+tGp+zpEZOdJY3YBC7DUzPiurxlRBNxvOOtQoR5znDbvwjkGEiQkkrpcdQgQtCmn8b5UjLuyUr/DvGmt6T0352Cv9kdji6BL3aHo0vQcdnisnhtxRwUlZJ4tOK86Yi0yGlF0zjvt6S0uNaTrwRmHIyMsuJnCkx2/f3a5IWiet5w26Pn1sS4eL9VqjpSLNsv4rM14wz1i4ibQVOk8ZJLuscEHoFp8KabmdcppKgkfVRr+l7V6hARB8+iXI0nlxjs0R505YYxUbVQLGsvvFqQZ9ggfb97++ReLYDHIn5PRLS0JOO16huaasfGtMkO1cBsJr3mMfbzKrNwjLnsmjjpnTfe4XD4Ync4ugQbtkW4erdcxJxs1nJ0ZaEd7hRbnjn0rtPnC7ATW66KeFib1yIy7vaXq7oTFL82bxbut2MnT6h2BZLxBkNAcOcdt0flupFS+3tEZC5mRKTNmoYZUBNGhodUHQMJSAaIHELDBAZRvEdXDxBuVBZkV93eq0BynN1tToFouggeaMWsVl3Qg65uqMEroJYhwUbO0DIPFmUc9p5uG5Qd/ZPTQEbSq8cxMiL38WaTTfapp56KylOz2oMunxJrSC3EZ2DFuWrF9lRM8EqjHq9ixuJ6qKQdDsdfDfhidzi6BL7YHY4uwYaZ3qy+neTVhmmesM7qiUnmpCooc6iXLxoCiQyYqIKJNsPoNkzZbHXe0ryY4rbfuU/XgQfW0pLWgRtVMQftHhLTWyatiS/TQXvNxY0R72mjpvcOGqDD2/0T5FBX57UmIzCN1Vj3nwFXvkJBxru0oM2UKmWXIWLEtNvLwExsraU4J0p1HcU41CPkG7W0MJ5fLOt7j30cP35c1aH5dGKr5tiPG38Sh/wqz0/MEh7Qy29938X+Znc4ugS+2B2OLkHHOegui+9WREnyoItL12TbWT5uBIpVKHabeAhi+GKJdWVpSUTQ0SHJZZk3nOk337o7Kp86rYMqFiCF1LbRUVU3MAiea/NCoDBoeOMLReCDN7amxWUZM2ZjTRupcgBMT9mMSQ2VQuIM4PyzJkAIhKk0tWdcDbwNkccuw/oZNVOidlh1aH4G+OBzwGlX1apXA/jskZ+PiGhqSTjjBobEM662rE1vS8uihrx2+BVVt327BCVlsrp/tGhyqr0gL+tdh+QeekpfS9CYe9A5HF0PX+wOR5fAF7vD0SXoOHnFZd3c6uxJfPBoTkJ9Z1W7BDLKRgynvO2jiXaduqmrgFtmRaKkduzYodqdOiWECTkT2XbnnXdG5UHNDknjBYwAgxS/abOHAeOyZA2oA5cWRW+2pBFDY2JO2jKqXW4nBsUVeMsWKddNYs6FOdnDWJg6r+qKQOjRU5Rz1WpaZ+8dlL2P+WVtDktVpe3MvOx1NNi45kKe6ozZtunvFx377ALq+nr+oRl3586dqg4JRJFsg0jP20yI15eR4MTmI8CZqs2gV5/rLSHHalvpn3Yw85PM/Aozv8zMn2x9P8LM32Lmw63/w1fqy+FwbBzaEePrRPRLIYR9RPQAEX2CmfcR0aeJ6IkQwi1E9ETrs8PheIOinVxv54joXKu8wMwHiWgbEX2QiB5uNfs8EX2HiD51xTO2RO2KMbNkMb1wU4sizGt7JlkRGSPnyGbOAZEQSR2qDS0qMUQupXLaUy0LIuKFSyJWZnu16WrnzbukDxO5VABxrm94QNUNN0BMrognWDWn79VsXS7u4iUt+p6/KN52E5vE22vacKf98KxwxhUar6q6rUNiEizm5Fk8/PADql2WgOQho8kgegfks7KUZfQzGx0Ts1aYMXztZfDQKwHhiDEvzcyKOlGqmCjGJbmPhZSce8G44R0+ejwq9xf1GAPkErjt0dtUXR3zHSR4cKLo3jCvWJxzSZ5314ur2qBj5t1EdA8RPU1Em1s/BERE54loc8xhDofjDYC2Fzsz9xHRV4noF0MIalcorOxyrfmzxsyPMfN+Zt5fLpfXauJwODqAthY7M2dpZaH/QQjhD1tfX2DmiVb9BBFNrnVsCOHxEML9IYT7MSDC4XB0FlfU2XnFFvA5IjoYQvhNqPoGEX2MiH6t9f/rV3Niy7WO+ctsFJYyb4CJxLpXZoEo0ZJFon6PPzrFota362BeYxMR1wDml50Dolv1GbLFS1OSBro4rI0UOI65izqabUuPXFsPMNAsGD303OS09JfRud4wQuvYEWHQyWT1dU4Myz0YSOt9i0pZ+t+0WfYfTr76umo31i+ReXmyuqY834EB2ZuYn9XvhDMX5fPLJ0+qusNnZF/h4qLsOYxOaI3xOOw/LM/riL0dwL9fW4bIOWNWxXllTWNbIPW1dXVFzT+fi5+bGXDzbpp7lYL5je7gq3LCXSfasbO/k4j+LhG9yMwHWt/9M1pZ5F9m5o8T0Qki+si6jszhcKwr2tmN/y7Fe9e/Z32H43A4bhQ6T17Rkp7qVS2iIKGgJbZAkxp6vNlUyXjcKgJEiORCAks2kW2hCSmkslrVyJbExPP3Hn04Kp+e132cmjwelY8asS8PYvfpcxdU3aF+Ea1vzstxPUHfK+Slf+01LVo3gFDi5jc/GJXf95M/rdr96be/F5Wf+/7vq7p77pJU0tlB4HKf1rzxtT7xJuvN6WfB4ClXash9W1zSJsATl6BueUHV1Zelbgh2l06/pKPSaiU518UlLcY3luVejQ0Jp/7YxIRqd3xSiCT78zq91F971yPSn/W8S8t8QRHfeojCtKJQNyoPiO44b5NyGlwL3Dfe4egS+GJ3OLoEnRXjg4g6VgTHnUcrgscRViSl2FkdsADEENAfZmMlImoAx3kw3m8hL+PaMi7eaeWMHsdNN0nwyIWnn1N1uFu+75abVN3xY7Ib/ewB8WorGBF5x67dUfmufbeouipkTC0vimj6+5/7D6rd/Q+LaDp79nZVN39exPUfTss4sjn9bnjHsOzwN/I6dVOmT+TWIyeOR+Wjl6ZVu3QTVJSUvs4eYym5jAff/ib1eRZSTZ04qbPJHoVzo6dj1YjIRVDtVpmIc6gCWtfM9pAk4mMdroNrPVcc/M3ucHQJfLE7HF0CX+wOR5egozo7pzjynLNeShngaF9tels7KsjqNJhq13ofpcFEojzoClpnL2XE3NNsaA7yRq+Y4qbBVFOtaR1vYFR0w53jW1TdEnj2DRo9965tMJY0kFH2anKJ5YtHonLNhFDNzcieQxXsPeW0Ptff/0UJUPy5F7T5rrgsJsFdeTn3ctBms/2vnYnKR04eUXX/58lnovK/ePjHo/KmzdqcmanIvfs7v/Srqu6hhx6Kyj/7sz8blScPvaTa5YLovJWGfhZDY3Ifv/+C7J+85e0PqXZv2i37J7tu0bnemjA37dsR5yp6elq9HPeabGQbHodluyfVFhIycfub3eHoEvhidzi6BJyUMmm9MT4+Hj74d/42ESWLMhZxoo0132Gftr+49FJT0zoY5ezZ01G52jDBNMAJtrsi/X3g3jerdluAZ/x54yV3DtIw5WpaLO4pi9owCCrD7LIexzyQOgz0abF4GVJPpYJoabMV/ZxHtkpwSo+R/ebPAtc9mPIGTeqjUxcl0vn+XeOqbnBYPp8HL7nnXj+m2k2Mj0XlzVv3qLpnT4k33EuHDkflmzdpcomLl4T/bmxAE4KcXRKvvFEg5ThyRpvoPvDIe6PyvBGfe4Hfv9FI4HhLSGGG83FVqvG0HIfz2wZztYNvfPWrdHFqas3F5G92h6NL4Ivd4egS+GJ3OLoEHTW9BRJdxurUqONYs0UJ3CGRbKJp9htSCb9d2D/q+lbvV7AJ0oDo8RKY7/oHtQ6ZAgLBfEObABsLoudmCub214GUsCZ97N6xXTU7dETcatN1rddt3yT6JaZeTqe0nlgDwsxqReuoo8Oy54BUYmdndFTaobOyH/HgTTqKbBki1gJQPJxb1OfaNCB1x17TJrV+eNbvuFX2Ji6emNHt0nL/MzYaG6LvbrldyCJfX9IRfM8//3xUzg7qqLd9Y2PwSc85NP8mkUXi3tiq1NfQJ5KbWLfxdpCU5tnf7A5Hl8AXu8PRJeg4ecXl3xfLDZ8FoghrcihARFIKTB+reL5ABLLmDSSvQGmrUNAiOEalzc4ZDjroswwk8j013UcG0hPdtENHtp2Ye1bGX9UibSqLqXvlmgd6dP9jkE6pbrz8CLzJ+uHaerNavMV71zeiefIwDVMRxOJdJs3VCPC79ffp9NOLwJuXA1Xp1ts173oxJapB+pIWg5fnpA7F24lN+lyzQCqSzut7VSzJvDp1Tvjuxhs6om5kk/DMbdm3T9U16vhO1GNEUxma0MiakmG+r+JYhDrkYlxvDnl/szscXQJf7A5Hl6DzHHQtEcaKMigO2bRONaB0xp3MpEywSbuSScBzrwpm4LXFqvNLmuq5By5t2iTG0NdtrAlwPmy3uLhIccgZawJQ11EFKK7rFZM9dQiCbljXbRsVkbYEIuayUZv6QKQtG6+zvryIyYsluD8zeie9lhPrRC6tg1gqJM+9WhJ1RYnLRDQAu/1NIz2P9UkAEBchs2y/DgzaBiQg1YzxvoTsrGwyteJ8bIR4i1KAOuu1Gud55+QVDofjmuCL3eHoEvhidzi6BB3X2S/rJKt0GhiJVbcLWdHl0CxnPYyQbHGVl1JT9Do8dyal2+WzorMHQ9uNprFqkHGcrmvz1y5MDVzTXmeoYadYX+hiWXTzm7eIeWnJRL1t3SyEEtV5Q7AB5sFUQdrlilqnrsH9CGbvQJsmRc8t9mkSzwIQRPYXTB0QfOay4pFWWtTmzJMzos/3ZPR17t4tpr3FWTmuvKz3SOopuba5mr4f5aaMPwM5AjI9dv7J+DNGp240Yc6Z+RJHXhEa8Waz1XNz7WjNpEjQOCQdc8U3OzMXmPkZZn6BmV9m5l9pfb+HmZ9m5teZ+UvMnLtSXw6HY+PQjhhfIaJHQghvIaK7iehRZn6AiH6diD4bQthLRDNE9PEbN0yHw3G9aCfXWyCiy/JltvUXiOgRIrqcU+jzRPSviOi3r9RfnEkMzRGr0t7Ax6TggCRTBR6XFJSA7ey5Asj1NTA1HThzQrXbPi7EDTt36wCRudeEhKGekKSTwfutvqDNdz0Zqds8ofnpMHClySJmL5bnVbtaVYgzxsc18USpCsfNyXF9WW2uajREnC5kjSdiUcTiIgh9I3Pa9HYWyDYGe/WzQA+6YkbO3T+sCTsUn2FFqwJTi3I/hmAe5bfuVu3aJXGx7TCQCs3HSaJ6kuktieTietFufvZ0K4PrJBF9i4iOENFsCJFv5mki2rauI3M4HOuKthZ7CKERQribiLYT0duI6LYrHBKBmR9j5v3MvL9sHEwcDkfncFWmtxDCLBE9SUQPEtEQcxTxsZ2IzsQc83gI4f4Qwv2r0uo4HI6O4Yo6OzOPE1EthDDLzEUiei+tbM49SUQfIqIvEtHHiOjrV+orhBBFW1nSiCzwn1udJoS13WBtO0746Yrj7c5kTJRUUbjnrd5lue4vY9GYY+ZKomvWmnpQtaroYYWM1j3rcG2zixdlvClt1kLdsJ42kXNNeaSD4BLaZ9JPz8xKBNj0tNajK3DdI31yP3pSJuIrBfz7KX0fsz1yXBWebTqrr2WsKGa5dFoTcPb3gMluRs49MKJJJS8FMN8ZNTddlz0HdKsNQzrSr2lJLwB1iETLFvW8rcLGC2b/tnMT51IScUtSSvJ2kGR6a8fOPkFEn+eVzIgpIvpyCOGbzPwKEX2RmX+ViJ4nos9d9cgcDkfH0M5u/A+J6J41vj9KK/q7w+H4EUBn0z8xR+KMFZHrYDK5nCIqqkP+dhaxtdHQImw6rY9DoBkDTWrWvIFiVG+P5iJDwocUAR/dgvbaevLFV6Lye++5V/cP4nTDpIRGsQ1VhkxeEy1gFFyW9H0MwMc2eV448K1ImAKShyyblEwwLc5ckDRXg306VVZIichYC1pd2dIrIn7IQARcTatCFYgkzNSMN2OvjHk7pMGuk75vPSyqwSXDk7cJyE76N22OyktpI0oniOC411S3nO/o8QVJYdgAAB8JSURBVBbz/Vqf1bljSCquJadD0jHuG+9wdAl8sTscXYINI6+w/HH5BP44dGSrVsVWb8X9WiOe5ysOVuxB8oq8EZ8Zwlhqddk5ThlPu3PLcm1f/c5Tqu6+PbvkQz4+qCIFQSarSDrAOmHJGg4dl2yqA0OihvQV9W55qSridDGtRfwGpJdK5SBzrQlAuXmP8OudPnlS1ZUgOGgZONymFzWFcw22sGeN91sRrnPu3NGo3F/U6kQaH2Ew9xTm2YnzQn09uFObgQOoMlbFVHTRq4KG4gkrEGhBWZX+KSa92bWI8Um8df5mdzi6BL7YHY4ugS92h6NL0HGd/XLkmNWpkUxhVV0VPd5E16rUtH6Sychv12q9f+2ot1TGRMqBElwA7zEiovyc6LZN0EmDUZPKoNcNjmmO8yoomL0FrSuHmoy/AHVlo4eFnLSrmrTP/aPiGVaHPYyGcS/Mp8Vc1cjouqUi3APQ5wtGhTx1UfTvBdL7J82U3LvzFyQ9cn/BePJVxVQ2Mqyj6pbgsqssune5pNNsjxblmqeXtOltGfIFbL1JSCUXLXlKXS6uasIRGUy8KeNpl0LCigQdOynSsgEpwrLZ6yOZTF0PeYXD4firAV/sDkeXoLMedCQijE3xlCS+oAmsrvjJ9W9VkhkEz6d46Y3UgypEX58WK+ujIpKfh/RGqIIQaTFtflmH9ZYHgWe8oc1heG2FgpiXSktarESz5cSmTapublbEWAzysWpNFjji8iYasQhce8jJlzWqwIW5S3CMFs8DBL+MjogJsFyeVe36wDxYMubYeUjrVATxv2zSZhULEFzUqwNtGuAah2pB03j8pVPxASh47xo1fW6cSzj/rAkMn601GRN4QWK7a8l9kGSs8ze7w9El8MXucHQJfLE7HF2CjursgcQEkUTmmDJkEKijJpFKMiu/SVWHx6EZJJjU0ajC51Jaoce9g0KPED02F7UpCDnqGyYvWSonfTRSWv/DceUgAmx4QOvUi0CimMoakxqmnJ4UAozeXm1GHLvp1qh8ZuqsqhsG09sL+5+B7/Uexi277ovKS4YfPz8C5BVl0YEnp/VeTRoi/+bNvkID3GXHxsaicl2finqGxfR29KQmTEKyDIZ9CpPBmtBqZvVyfC7WLKzmUoLpLSnSEtOXY2rx0J7Hd9vwN7vD0SXwxe5wdAk67EEXIhHGmjfQVLE6Kmht0d2aN9BSYcUtFbmUEBmU1G5oSER3HOOpquZkF4bt1eNA4onRMU2O0QCSjhq45Q0YfrfT88IfN9OvI/OaIEqO94sovWTSSp8+/FJUnjSsv2P3iKfZHe8UMqLdu3erdotpuR+H/uK7qu62fjC35UX8/5sf/Ihq91//y3+Iyid++KKqu/2uO6JyoyZj3LFru2p34IiI7oU+zU/XV9CfLyOR5GFVuuX2PONQ3Uzij1s9b9fmjU9dfdAbcQKXnr/ZHY4ugS92h6NL0GExnuny70u9rkUZFJ/TaStGwe42BA1YERl3MpO45bSorndeGw3MqKnVBzwf7q7mi0YchwAdNiIbitOpER0kk07Ltc0DV1uxoR/Tzdt2ROWLJU0GMdQPaZeyIuLns7qPUlkCefqH9fif++6zUbkAt+fky8dUu3vfLvx6E0OG0w0orkcmROz+T//x36l2Zw5LOqzb7rxF1fVD5tkaWGhKVS3f1qdFjRrfsVXVlSEwCFi8r2pXXYndKTvnZK6iN521GqmddTs3G2tncbXnagsJh/ib3eHoEvhidzi6BL7YHY4uQYej3jjSda35AfUkHdmmdfZiUfRQGzmXFPWGKaSSPKI0+Z/WreL0M3suZUoxHnrLQW65Jd3EKLuzc6JT503apcE+6b9W0mazJvDBNzOi9zeDPldxELjcFzQBxp07hRRzJAceaHkTDXZazIh9Za2jVi8K33ypKaaxPWY/Jj0m4+CmHkezCveYxYT2zHPPq3Zvvv1NUfmlsxdVXWZEUman4bnY+YfP3dYp/TtBj26GhKg38Kpc5QUaQ4BxLSmb14U3vpW2+Xlm/mbr8x5mfpqZX2fmLzFz7kp9OByOjcPViPGfJKKD8PnXieizIYS9RDRDRB9fz4E5HI71RVtiPDNvJ6IPENG/IaJ/zCsyzyNE9NOtJp8non9FRL+d1E+gEInkq8xrILJYQSkDKY2qy5AWyQbTJKRxRWldiTpBi6acQhOdCZJhVDWq0C4pOMfw6UHbWsOYcRZFdB8aGInKi6TVmnEYY0+/DnCZmZcoEYZoDzbBHYPAB889evzNmlzbmUtiKsxkNDEEmpPsdTbKIpI3mtLfbGVRtRsYEPF8wDg2LgIv3NSskF5sG9UZWC/MSBbaqaoWfbegygbTJRjiQPQ8W80ZgVlWdaXm+oc6I+4H4ANcpSZk1g7SqhsuvOtFu2/2f09Ev0xy1aNENBvEL/Q0EW1b15E5HI51xRUXOzP/JBFNhhCevVLbmOMfY+b9zLy/bHywHQ5H59COGP9OIvobzPx+IioQ0QAR/RYRDTFzpvV2305EZ9Y6OITwOBE9TkQ0Nj5+Da79DodjPdBOfvbPENFniIiY+WEi+ichhJ9h5q8Q0YeI6ItE9DEi+no7J7ysr6zSWzD1rXWDRZ0YdKSaMdExEPcpUknSZq4kMwsnRM5hW0zja/cO4o4hIjpxXPKNFZd1+uJbt4qe3tOUutdPnlDtNt355qic7tXEFpWS6NjokjxsosEw3bUlkqzCfkQOfp6bxtQ5BIQS58+fV3W9cP9Ly7KPUDCmSMKUzYanvwqEkyeh/9HbtEvs0UvS/9Zb7tLdw7WhqdZGpaHLaqL5apUr99ptrfkY93WS8hAmjbEdJPV9PU41n6KVzbrXaUWH/9x19OVwOG4wrsqpJoTwHSL6Tqt8lIjeltTe4XC8cdDx9E+XYcVbFIutZ1kVPOgI+OWDJaG4Bu+j1aK6HJfJJJny5DjLA451M7OaJx0yJdO2vberutqSiPiLC8Jrt2vPTtXu9MVpqds2oep6wL6UScu4QlNfSyYHKaErWuTEFEJ1IOJokuEGLMvF5DNa5Kwui+mtCNzzl8o6Sq8fovTYqEOz0yLG5/qk3dzSsmpX7ZG6dEGnc24G4OsD05idHylleot/7nbe4mdU55IIMGwdjgXbWQ/RdtBcDw86h8Pxow1f7A5Hl2DD0j9Z1ECUsQxxuFuMYhMbiSVJTEMRC3dKma2XnIhzxumMMjn5ogFiMWZLJSK6eFF2hysla1mQ42aWtDdZLshudKMmhAzNOS22MnhWLZR1XYDsr4slqcv26UddB++0jNkhb0BwTRFSSKWN+JkG0T0/qHf750AEbTRlvMFIphgotFgxpBRBVLvBUTnXs5MXVLu73vlIVG42zEODKZ5u0wqTtBtvU/ZmwbsTz71KjIfrXO29B2sCPPSy6au3VCfRXfib3eHoEvhidzi6BL7YHY4uQcfTP13WpVcR8iWYJuLIAG27JM531OFRL7fjUHsCCR505bIon68eel21Q32YWZuk0MQzvaDNUA8++K6ofOq570flrLEiboU0xzOLWmcf6hECjHJZvOkWTC4hTKOcMv2ns2K+akLqZRv11kxKfcSiv2L04ED/iGq23JTxz5v9k7NTQkSRhdRT++57QI+jKXozczzRaKIuDrCea0kmMOwT94XsuTjBQxSjArM5MHvWrt70lpS02d/sDkeXwBe7w9El6DgHXTq9IiKtChQAEY6NeN4E0SSJy6tRjSeUQGc4PHeDrEeUNDxngjsOHhSiHhTtQlN7/KF6EQyPHcO4pqdnVF19WUTat/7Yw1H50Pd0dPGRKRnXQE4TOaQU0QJwolWNOjEnpr3enA6mCXUZx9Yx8d7r3ay99ZaWpY9qRV/n8jKkmwKRnuo64+3MknjalXMmLRJw3u25986oXMpoj8UqqgyrMqSCh1tK+lsdMCJzrFLRAUrYR9rw7wcgFkEyktVeePhcdE0mTvTmq1+env7J4XD4Ync4ugW+2B2OLkGHTW8h0n9Wuc0249McI0kFklJYl1jsc1XkHPyuzc6KyeuZ536o2s1DtBm6ihLpyLwUEApyyrqigo5qyA4YdL6ZZU3TdfyM6OJ50FdHbnuzajfzutSVGloHnoLs0RkgMixktWvu3JLoiUMDOtdbBUw+pZlzUbk2e1a1G+2XXHX1muHHh3NfmpExlkp6r+b0nOjsFxa0G+ztD/1YVK6C62zd+DHnIa8cZeJTgaco3p0ap5yNYsS2jRAf9RaXWpwo3l2biKgJewkqp0FC1GUsbhB5hcPh+BGCL3aHo0vQcfKKJrVEImNtyKEoY6KwUBxFhlrL/bZQlbqnv/+cqpucFG+sQhqIG4zk1Qt86r15LcZX6yAGguiYMVziyupi+N1QJEyT7n/TZuF0yxaGovLd996j2r3QI9FxS4deVHWXZk/LqQtyLUtGfM5npW55SpsAFysiWi8Og9nJPLOZRRH3LXNwCkTQI2dE/K+Ze4WBbnsfeFDVKdISBlOniWxrwvOkhLRimJ4pyTRmxWyEVTFRddT5CAyvXzU+1Th+wrlfrep0WO3Arh2Ev9kdji6BL3aHo0vQcTH+svhkA1XKDRFz7E59dVlEuAMHDkTlycnJNfsmIqrWtAiEBBgZCJbI5IqqHYpOdkefeG2Rrdm0YrzcVrZpgDAwg3SgwxPf+m5UfuQn3xuVD7ykRfWfeN9fj8r/d1nvKu/YJh51hw4eicqlqhbvFpZhdz5rvA3hYwmeSy6tp8vMnHDhWbG4viTjmofd87k5rTL8o3/+K1H53372P6u6vbffEpVHxsRiYMeBYncugdY7qAy9luo5nthCBV9l4nfcdfCLvt+pNjPIJgV6tYUE9gp/szscXQJf7A5Hl8AXu8PRJeiozt4MTSqVVnTiYkETFJ46Janijh7TZBCLi+LxhnpMyujDNSBlZ/M7FkAnqxB4RFUMmSPoT6mc9qTCNNPptBA5rEpYiaYVy54J5w5pk6IK9gseffChqHxhXpNc/Pmf/XlUHtusySBmlsQMtWuX6KWDKb0/cPqSuNpNGz26CWM8f0Gey4IhyOzLCaFExXjQYcRasxJPxFiGaLmzZ86punPn5DOSUvT0aBIN7NOaY++5R8yWg+My59I5GzEZH7EWa14zQOJIS2SK+Qisk5vK9JxCApZ4E+C1oN387MeJaIFWZmo9hHA/M48Q0ZeIaDcRHSeij4QQZuL6cDgcG4urEeN/LIRwdwjh/tbnTxPREyGEW4joidZnh8PxBgW3w8vVerPfH0K4CN+9SkQPhxDOMfMEEX0nhHBrUj/5QjFs27WXiIhKkG2UiCgDQSdNQwaBJjA0W1jTWB6CZJJ47FRQggmIQOKCvr4+VadViPigCjxXNq0DM+oN6T9jAm2QQ/3Df/tvSf9GNL15x01R2ZpxpqfFHJbOAKnDsWOq3a5+ERcXZnQwzSKYpebBRFfs1VlWl2py3YcvaKKPJojn2K5vZEi1u1CRe3D42ClVVwPzaQW8I614q9MuqSoVONXfL9x6veZatm/fHpUnJjRJBx5nVTbNVycnt15yAbwB7TNTKg/U2Xbt4Ev/4w9o8vz5NQ1w7b7ZAxH9KTM/y8yPtb7bHEK4rFSdJ6LNVz0yh8PRMbS7QfdQCOEMM28iom8x8yGsDCEEtjsSLbR+HB4j0hlEHA5HZ9HWmz2EcKb1f5KIvkYrqZovtMR3av2fjDn28RDC/SGE++NSPzkcjhuPK77ZmbmXiFIhhIVW+X1E9K+J6BtE9DEi+rXW/69fqa9mM9BSK1Kq0bR6ruhC1ZqJoALiPdRjLMlAAT4vLmozUQbcZesNiHAybo2og1kTD+rzqKdbgkL8UcsYYgtFTlA3OhmYEv/w6/87Kv/8L/9T1ex1cIPt6dcmzF6IZquAPvzoP/wF1e6pr3wtKg9ltfkxVRez3E27d0XlmTmdfnoROOsnDKf8ZEX6wL2PlCG3HOwRN1g+pskx0KM1l8VnrZ87otHUcwf3dRZmZZ+oaqIAZyG19qFDSnBV+vf73//+2Drc/lqll6fi/Vhrq/LTtfoIV6+zJ/HGtyPGbyair7UuKkNE/zOE8MfM/AMi+jIzf5yIThDRR65hZA6Ho0O44mIPIRwlores8f00Eb3nRgzK4XCsPzoc9dakZrhsTtHiRq0GvNpNQxCQhTS2YGaxprcFEN3R5LLS6driTcp8j2acSimeP1yZ17JaNEVVwJqJGEg6GsZOhCJhDlSI80ePqHbjEzui8s5t46ru2JHjUfme+4S77rtPfU+1u/sDPx6VX3hSc9DN/uX/jcrZPIjF5rkUYMM11a/v9xLLvVoYEFNW1ZA6vPVt74jKB145rOqydYhEa8A4ElSjXFab1BrA5d4EL7zlihb3uYriuH7u+DyfeOLPVR2a6fa+6eaovDqtGJ5M34MsRE0qU7hlVmkDzhvvcDh8sTsc3QJf7A5Hl6DDOjtHbqar3XRlKDkTkZROSVvU01fleoPINkVWSER5YJlBs1nK9JHEKKJ0Q9gTCIY4EllQjHqm9PKsqcTjygtiJpo8q6PBsllho3n18Lyqu22XuH1+//mXo/KW4VHV7uk/fjoq732b9nIu5eT+XHj1pag8uKx12To8p5q+3bT3HcKmMwOmzmxGuyC//NprUfnWW/U4Xn1ZWIlKwLFfMSmUUaeu1hd0XUZ0eJWD7ypSKmPbc5PTqm4B7smlWal7+9vfrtrhXlBSOnGETaXdFhK83/3N7nB0CXyxOxxdgs6K8SFQs7EiggUTDRbqIp7byKVlMIGh15wVxXqCiJWWULAM0WYo/lsvJRwVm7oUEB2W6xBFR8aMA+U0axEfRbZgVQjgWi8HEVWfff551e7DH/poVH7mpZdU3ZELIsbuBLKGc5emVLtcUc79vb94WtV98AOPROWvnZKUTM+8rElFbt0jpqb3f+jDqu5Lf/R/onJ/v5j2ymVNedBYFHWlUtGRkMBFooghMka+rUAEZTpr0jmDR10+r8lFEageWrOtStlsLFs1ID85c0ru/RPz31btxsYkJ8Bb36rzADRpbRVidVrpNuCEkw6Hwxe7w9El6KgYzymOxPCS2b5N4shGsUrtdFsxB6TihhH1suC5hv1ZTypUDep10weI8XjuRkPvUiMs/4Da7W/Ee9D1gvVgVsep0PPPPBWV832aRuCmW2Q3/tVXxPNu+xbdbvKSiNNjpu7JJ74fld/9LvFwO39Qe+HtPyLi/8nf08QTe266Myr3FsUb0FpQkGduz549qu4lsCagRaZu6PxzWek/pOI9Ii/zHxLpjLxEySKzCl4y81SRTTSk3fzcQmy7b39bi/j33ffWqDwwIKpXO8QyVwN/szscXQJf7A5Hl8AXu8PRJeiozh4CU72eaZ1Y626B0fRmdFllEEMPOt1/tQrpkFPatId83OWy6NhFY6pBvnnO6HGgeQZ5BhvGgw7NgyVDUIgEG42GifyD/Yg6sHzlDfFBpS6Pbduo4d8/Kimbd+0V/f3waydUu81D4sl2Zkp7he3dK1F1/+mzvxGVM5M6+u6BRyVy7ocvakLLP3v1T6LyO94tkdCLVWOmhNxpW7frCL4hMFeVK6IDl6oXVTsMxgtmkwT3eOp1SDFd0ve0p1fMctajTZlqzTNDEhZsZ02/Cwsy/sVFfe7vP70/Ko8NyfO8735torte+Jvd4egS+GJ3OLoEbfHGrxdy+WLYNLGbiNYIMgHx3I4pnRLRtxlQPNLiVg7SL1dWpXUCDjoQsfp7dWAG8oJbc0yceYZZqwxo7rF3F6+bU/H3vgRpjnuzuv+lWeF5/8QvfELV3bR7b1Q+eV443Sa27FLtDh2UNNA7t2ue9JmzItYfPiBEFqNDWl0p9IHIeY8mM/rdz38lKmcHtkblfQ+8S7V79dDRqNxj+l+Ylef77HNiDlya11542gxai61Tqb3MM6vWRLWznPJqPgY9B+IITayJsd6IV1PRM3MT8OqnM1fvQXfo4CFaXlq6Lt54h8PxIw5f7A5Hl8AXu8PRJeio6a3RaNLcwoou3dOrTV5ZEvfFctPqXZCKWelg+reqGuS4jPkdaxAST8i5anVNhJCGaLyqMROhHqYIE0y6YkqheVCrT8ghyEHf/jKQUw73Sn4xmxcv1yfuoVs26ZTNx85JdNueXbuj8vR5ncPj0ff8RFQ+cPBZVbfvZjGB/ekXhFxilneodoUxuXd/+b3fUXULs3Itm9IS9faiidIb6ZO66rLex5mFVNJNuFdpE9qVgvtWD1ZdBSJJRldoPcey4EK9tKz3e9C9Os1aF8d5UAG9n5rxzBOr9oLA1Dc7K2QkNv10O4gjwiDyN7vD0TXwxe5wdAk6KsanU0z9xZZ5LGgPoyaJSJgyJqkqcI4Vi/GeTpReO6UyEVENTB81ELfyhvM9nQZSiqIhQlDcZyJyosmPSJsRrYkRRbimGX8Kxo8RWtaMkwGPuslJzUH3lrvvisonz4rpzXpjHT8hKZYfftf7VN0/+PB7o/Kd+4Sg4sRr2nPtrnu2ReWfu+NRVfcbv/V7UXl2Tvj83/1uzTN3/uSZqHzvfferupkFEadfOyzEGWlDQsFo1qpptUyJzDCv8iYNVb0ebxpD2Dmnot6Q7CQVz21oTcvo3Ylz7JpyI14vBx0zDzHz/2LmQ8x8kJkfZOYRZv4WMx9u/R++ck8Oh2Oj0K4Y/1tE9MchhNtoJRXUQSL6NBE9EUK4hYieaH12OBxvULSTxXWQiN5NRH+PiCiEUCWiKjN/kIgebjX7PBF9h4g+ldRXIKJmy+stQ3o3NIBomrYiEOzU5wtSV1rWu5q1ClBE2yyxGFgCHlKhamh9UQQ3WVYzaREfkduMTdANWgICt88HrIInIB1WpkerE5fm56Lyxek5VffDF5+JyiOjsnu+/5kfqHaplOzoP/XU/1N199z5QFTetReIQ8y1DI6J9+Hvfu5/qroCpGhK9YvQVzUZS6vwnAYHdRqqi7MSPILiuE1+m4ZAqWDmFUEaqgaQkeSy8d5piZ6THN82A4FT3NTydBM+N01gUxpVASgn7axfC9p5s+8hoiki+u/M/Dwz/7dW6ubNIYTLNCPnaSXbq8PheIOincWeIaJ7iei3Qwj3ENESGZE9rOw4rLk1wMyPMfN+Zt7fTLA9OhyOG4t2FvtpIjodQrhMOPa/aGXxX2DmCSKi1v/JtQ4OITweQrg/hHB/6hqyUjocjvXBFRd7COE8EZ1i5ss2k/cQ0StE9A0i+ljru48R0ddvyAgdDse6oF07+88T0R8wc46IjhLRz9HKD8WXmfnjRHSCiD5yY4bocDjWA20t9hDCASK6f42q96zxncPheAPC3WUdji6BL3aHo0vgi93h6BL4Ync4ugS+2B2OLoEvdoejS+CL3eHoEnSUN56Zp2jFAWeMiC5eofmNxhthDEQ+Dgsfh8bVjmNXCGF8rYqOLvbopMz7QwhrOel01Rh8HD6OTo7DxXiHo0vgi93h6BJs1GJ/fIPOi3gjjIHIx2Hh49BYt3FsiM7ucDg6DxfjHY4uQUcXOzM/ysyvMvPrzNwxNlpm/h1mnmTml+C7jlNhM/MOZn6SmV9h5peZ+ZMbMRZmLjDzM8z8Qmscv9L6fg8zP916Pl9q8RfccDBzusVv+M2NGgczH2fmF5n5ADPvb323EXPkhtG2d2yxM3OaiP4zEf0EEe0joo8y874Onf53iehR891GUGHXieiXQgj7iOgBIvpE6x50eiwVInokhPAWIrqbiB5l5geI6NeJ6LMhhL1ENENEH7/B47iMT9IKPfllbNQ4fiyEcDeYujZijtw42vYQQkf+iOhBIvoT+PwZIvpMB8+/m4hegs+vEtFEqzxBRK92aiwwhq8T0Xs3cixE1ENEzxHR22nFeSOz1vO6geff3prAjxDRN2mFrHkjxnGciMbMdx19LkQ0SETHqLWXtt7j6KQYv42ITsHn063vNgobSoXNzLuJ6B4ienojxtISnQ/QClHot4joCBHNhhDl5erU8/n3RPTLJPm0RjdoHIGI/pSZn2Xmx1rfdfq53FDadt+go2Qq7BsBZu4joq8S0S+GEFSytk6NJYTQCCHcTStv1rcR0W03+pwWzPyTRDQZQnj2io1vPB4KIdxLK2rmJ5j53VjZoedyXbTtV0InF/sZIsIE39tb320U2qLCXm8wc5ZWFvofhBD+cCPHQkQUQpgloidpRVweYubLvISdeD7vJKK/wczHieiLtCLK/9YGjINCCGda/yeJ6Gu08gPY6edyXbTtV0InF/sPiOiW1k5rjoh+ilboqDcKHafC5pVcQZ8jooMhhN/cqLEw8zgzD7XKRVrZNzhIK4v+Q50aRwjhMyGE7SGE3bQyH74dQviZTo+DmXuZuf9ymYjeR0QvUYefS7jRtO03euPDbDS8n4heoxX98J938LxfIKJzRFSjlV/Pj9OKbvgEER0moj8jopEOjOMhWhHBfkhEB1p/7+/0WIjoLiJ6vjWOl4joX7a+v4mIniGi14noK0SU7+AzepiIvrkR42id74XW38uX5+YGzZG7iWh/69n8byIaXq9xuAedw9El8A06h6NL4Ivd4egS+GJ3OLoEvtgdji6BL3aHo0vgi93h6BL4Ync4ugS+2B2OLsH/B7Mr/JWMzmArAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=1\n",
    "plt.imshow(w_loaded_images[n]);\n",
    "print(w_ages[n])\n",
    "print(w_gender[n])\n",
    "print(w_loaded_images.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 307035 images\n"
     ]
    },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=384), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=384), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=384), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=384), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=88), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "i_images, i_ages, i_gender = load_data(\"data/imdb_crop\", \"imdb\")\n",
    "image_shape = (64, 64, 3)\n",
    "\n",
    "i_num_imgs = i_images.shape[0]\n",
    "chunksize = i_num_imgs // 400\n",
    "\n",
    "chunk_generator = image_chunk_generator(i_images, chunksize)\n",
    "\n",
    "i_finnished_images = []\n",
    "\n",
    "for image_chunk in tqdm_notebook(chunk_generator, total = 401, desc=\"Image chunks\"):\n",
    "    loaded_images = load_images(\"data/imdb_crop\", image_chunk, (image_shape[0], image_shape[1]))\n",
    "    i_finnished_images.append(loaded_images)\n",
    "\n",
    "i_loaded_images = np.concatenate(i_finnished_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69\n",
      "1.0\n",
      "(153688, 64, 64, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n=0\n",
    "plt.imshow(i_loaded_images[n]);\n",
    "print(i_ages[n])\n",
    "print(i_gender[n])\n",
    "print(i_loaded_images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(188918, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "images = np.concatenate([w_loaded_images, i_loaded_images])\n",
    "ages = np.concatenate([w_ages, i_ages])\n",
    "gender = np.concatenate([w_gender, i_gender])\n",
    "print(images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py.File(\"preprocesed_gender_age_test.hdf5\", \"w\") as f:\n",
    "    f.create_dataset(\"images\", data=images, compression=\"gzip\", compression_opts=4)\n",
    "    f.create_dataset(\"ages\", data=ages)\n",
    "    f.create_dataset(\"gender\", data=gender)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Age-cGAN",
   "language": "python",
   "name": "age-cgan"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}