Generate_doodles.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  import libraiies\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n",
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data from csv file\n",
    "data_airplane = pd.read_csv('data/airplane.csv')\n",
    "data_bicycle = pd.read_csv('data/bicycle.csv')\n",
    "data_car = pd.read_csv('data/car.csv')\n",
    "data_truck = pd.read_csv('data/truck.csv')\n",
    "data_bus = pd.read_csv('data/bus.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>countrycode</th>\n",
       "      <th>drawing</th>\n",
       "      <th>key_id</th>\n",
       "      <th>recognized</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>[[[167, 109, 80, 69, 58, 31, 57, 117, 99, 52, ...</td>\n",
       "      <td>5152802093400064</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-03-08 21:12:07.266040</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA</td>\n",
       "      <td>[[[2, 14, 34, 126], [57, 45, 38, 47]], [[41, 4...</td>\n",
       "      <td>6622904974311424</td>\n",
       "      <td>False</td>\n",
       "      <td>2017-03-06 21:39:04.690130</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US</td>\n",
       "      <td>[[[90, 88, 95, 104, 112, 122], [65, 31, 12, 0,...</td>\n",
       "      <td>6577010312740864</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-03-23 02:08:35.229980</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US</td>\n",
       "      <td>[[[82, 49, 15, 4, 0, 5, 30, 85, 89, 93, 112, 1...</td>\n",
       "      <td>5643224746033152</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-03-10 00:35:17.531970</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IL</td>\n",
       "      <td>[[[64, 38, 23, 8, 0, 6, 26, 68], [74, 77, 84, ...</td>\n",
       "      <td>6670046841536512</td>\n",
       "      <td>True</td>\n",
       "      <td>2017-01-23 18:11:11.658170</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  countrycode                                            drawing  \\\n",
       "0          US  [[[167, 109, 80, 69, 58, 31, 57, 117, 99, 52, ...   \n",
       "1          CA  [[[2, 14, 34, 126], [57, 45, 38, 47]], [[41, 4...   \n",
       "2          US  [[[90, 88, 95, 104, 112, 122], [65, 31, 12, 0,...   \n",
       "3          US  [[[82, 49, 15, 4, 0, 5, 30, 85, 89, 93, 112, 1...   \n",
       "4          IL  [[[64, 38, 23, 8, 0, 6, 26, 68], [74, 77, 84, ...   \n",
       "\n",
       "             key_id  recognized                   timestamp      word  \n",
       "0  5152802093400064        True  2017-03-08 21:12:07.266040  airplane  \n",
       "1  6622904974311424       False  2017-03-06 21:39:04.690130  airplane  \n",
       "2  6577010312740864        True  2017-03-23 02:08:35.229980  airplane  \n",
       "3  5643224746033152        True  2017-03-10 00:35:17.531970  airplane  \n",
       "4  6670046841536512        True  2017-01-23 18:11:11.658170  airplane  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  print data\n",
    "\n",
    "data_airplane.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter all the images with recognized = True\n",
    "data_airplane = data_airplane[data_airplane.recognized == True]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drawing</th>\n",
       "      <th>key_id</th>\n",
       "      <th>word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[[167, 109, 80, 69, 58, 31, 57, 117, 99, 52, ...</td>\n",
       "      <td>5152802093400064</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[[90, 88, 95, 104, 112, 122], [65, 31, 12, 0,...</td>\n",
       "      <td>6577010312740864</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[[82, 49, 15, 4, 0, 5, 30, 85, 89, 93, 112, 1...</td>\n",
       "      <td>5643224746033152</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[[64, 38, 23, 8, 0, 6, 26, 68], [74, 77, 84, ...</td>\n",
       "      <td>6670046841536512</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[[[195, 164, 127, 40, 13, 0, 4, 28, 93, 172, 2...</td>\n",
       "      <td>5509429904539648</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             drawing            key_id  \\\n",
       "0  [[[167, 109, 80, 69, 58, 31, 57, 117, 99, 52, ...  5152802093400064   \n",
       "2  [[[90, 88, 95, 104, 112, 122], [65, 31, 12, 0,...  6577010312740864   \n",
       "3  [[[82, 49, 15, 4, 0, 5, 30, 85, 89, 93, 112, 1...  5643224746033152   \n",
       "4  [[[64, 38, 23, 8, 0, 6, 26, 68], [74, 77, 84, ...  6670046841536512   \n",
       "5  [[[195, 164, 127, 40, 13, 0, 4, 28, 93, 172, 2...  5509429904539648   \n",
       "\n",
       "       word  \n",
       "0  airplane  \n",
       "2  airplane  \n",
       "3  airplane  \n",
       "4  airplane  \n",
       "5  airplane  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  drop timestamp, recognised, countrycode,\n",
    "data_airplane = data_airplane.drop(['timestamp', 'recognized', 'countrycode'], axis=1)\n",
    "\n",
    "data_airplane.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drawing    0\n",
       "key_id     0\n",
       "word       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  count NA\n",
    "data_airplane.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(drawing, meta_data_dict=\"IMAGE\", size=(5,5)):\n",
    "    plt.figure(figsize=size)\n",
    "    plt.title(meta_data_dict)\n",
    "    for x,y in drawing:\n",
    "        plt.plot(x, -np.array(y))\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[167, 109, 80, 69, 58, 31, 57, 117, 99, 52, 30, 6, 1, 2, 66, 98, 253, 254, 246, 182, 165], [140, 194, 227, 232, 229, 229, 206, 124, 123, 149, 157, 159, 153, 110, 82, 77, 74, 109, 121, 127, 120]], [[207, 207, 210, 221, 238], [74, 103, 114, 128, 135]], [[119, 107, 76, 70, 49, 39, 60, 93], [72, 41, 3, 0, 1, 5, 38, 70]]]\n"
     ]
    }
   ],
   "source": [
    "print(data_airplane.drawing.values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawing = np.array(eval(data_airplane.drawing.values[0]), dtype=object)\n",
    "draw(drawing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = data_airplane.drawing.values[0]\n",
    "label = \"IMAGE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  draw image\n",
    "def draw_image(image, label):\n",
    "    image = ast.literal_eval(image)\n",
    "    plt.figure(figsize=(5, 5))\n",
    "    for x, y in image:\n",
    "        pl.plot(x, y, marker='.')\n",
    "    plt.title(label)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_image(image, label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating our Dataset with Real Image Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# my_dict = {\n",
    "#     'airplane': [],\n",
    "#     'bicycle': [],\n",
    "#     'bus': [],\n",
    "#     'car': [],\n",
    "#     'truck': []\n",
    "# }\n",
    "# # Add the images to the dataframe\n",
    "# for i in range(10000):\n",
    "\n",
    "#     img = cv2.imread('images/airplane_'+str(i)+'.png', cv2.IMREAD_GRAYSCALE)\n",
    "#     img = cv2.resize(img, (100, 100))\n",
    "#     my_dict['airplane'].append(img)\n",
    "\n",
    "#     img = cv2.imread('images/bicycle_'+str(i)+'.png', cv2.IMREAD_GRAYSCALE)\n",
    "#     img = cv2.resize(img, (100, 100))\n",
    "#     my_dict['bicycle'].append(img)\n",
    "\n",
    "#     img = cv2.imread('images/bus_'+str(i)+'.png', cv2.IMREAD_GRAYSCALE)\n",
    "#     img = cv2.resize(img, (100, 100))\n",
    "#     my_dict['bus'].append(img)\n",
    "\n",
    "#     img = cv2.imread('images/car_'+str(i)+'.png', cv2.IMREAD_GRAYSCALE)\n",
    "#     img = cv2.resize(img, (100, 100))\n",
    "#     my_dict['car'].append(img)\n",
    "\n",
    "#     img = cv2.imread('images/truck_'+str(i)+'.png', cv2.IMREAD_GRAYSCALE)\n",
    "#     img = cv2.resize(img, (100, 100))\n",
    "#     my_dict['truck'].append(img)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Ignore the warning -> Append is deprecated.... hence giving waring\n",
    "# import warnings\n",
    "# warnings.filterwarnings('ignore')\n",
    "# # Convert the dictionary to a dataframe\n",
    "# df = pd.DataFrame(columns=['image', 'label'])\n",
    "# for key, value in my_dict.items():\n",
    "#     for image in value:\n",
    "#         df = df.append({'image': image, 'label': key}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               image     label\n",
       "0  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "1  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "2  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "3  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "4  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the dataframe\n",
    "# Save as pickle file\n",
    "# df.to_pickle('df.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the pickle file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  read df.pkl file\n",
    "df = pd.read_pickle('data/df.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aiplane dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>[[255, 255, 255, 255, 255, 255, 255, 255, 255,...</td>\n",
       "      <td>airplane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  image     label\n",
       "0     [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "1     [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "2     [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "3     [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "4     [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "...                                                 ...       ...\n",
       "9995  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "9996  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "9997  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "9998  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "9999  [[255, 255, 255, 255, 255, 255, 255, 255, 255,...  airplane\n",
       "\n",
       "[10000 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_airplane = df[df['label'] == 'airplane']\n",
    "\n",
    "df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract Images\n",
    "X = np.array(df.image.values.tolist())\n",
    "\n",
    "# Reshape to 3d\n",
    "X = X.reshape(-1, 100, 100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing X\n",
    "X = X/255.0\n",
    "\n",
    "# Currently: 1->white and 0->blank and images are drawn with black color\n",
    "# We need to draw them with white color\n",
    "# So, we need to invert the images\n",
    "\n",
    "X = 1 - X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing the images\n",
    "plt.figure(figsize=(15,15))\n",
    "for i in range(5):\n",
    "    # Random Number\n",
    "    rand_num = np.random.randint(0, len(df))\n",
    "    plt.subplot(1, 5, i+1)\n",
    "    plt.imshow(X[rand_num].reshape(100, 100), cmap='gray')\n",
    "    plt.title(df.label.values[rand_num])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of VAE is to generate a realistic image given a random vector that is generated from a pre-defined distribution.\n",
    "\n",
    "The stategy is as follows:\n",
    "\n",
    "1. Encoder network: Given an image, the encoder network will generate two vectors: mean and variance. The mean and variance will be used to generate a random vector from a pre-defined distribution. We sum the mean and variance and multiply it by a random vector from a pre-defined distribution. The result is the input to the decoder network.\n",
    "\n",
    "2. Decoder network: Given the input from the encoder network, the decoder network will generate an image. The loss function is combination of L2 distance and KL divergence. The L2 distance is the difference between the generated image and the original image. The KL divergence is the difference between the distribution of the random vector and the pre-defined distribution. \n",
    "\n",
    "\n",
    "The mean vector has distribution around 0 and the variance vector has distribution around 1. The pre-defined distribution is a normal distribution with mean 0 and variance 1. The KL divergence is calculated as follows:\n",
    "\n",
    "$$KL = - \\sum \\frac{1}{2} (1 + \\log(\\sigma^2) - \\mu^2 - \\sigma^2)$$\n",
    "\n",
    "where p(x) is the distribution of the random vector and q(x) is the pre-defined distribution.\n",
    "\n",
    "The loss function is calculated as follows:\n",
    "\n",
    "$$L = \\sum_{i=1}^{n} (x_i - \\hat{x_i})^2 + KL$$\n",
    "\n",
    "where x is the original image and $\\hat{x}$ is the generated image.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  import libraries\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, Model, load_model, save_model\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, BatchNormalization, Input, concatenate\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.layers import Lambda\n",
    "from tensorflow.keras.layers import Reshape\n",
    "from tensorflow.keras.layers import Conv2DTranspose, UpSampling2D, MaxPooling2D\n",
    "\n",
    "#  import mse loss function\n",
    "from tensorflow.keras.losses import mse\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOT WORKING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #  build a VAE ( Variational Autoencoder) with latent dimension = 5 \n",
    "\n",
    "\n",
    "# class VAE:\n",
    "\n",
    "#     def __init__(self, input_dim, latent_dim):\n",
    "#         self.input_dim = input_dim\n",
    "#         self.latent_dim = latent_dim\n",
    "#         self.encoder = None\n",
    "#         self.decoder = None\n",
    "#         self.model = None\n",
    "        \n",
    "#         self._build()\n",
    "\n",
    "#     def sampling(self, args):\n",
    "#         z_mean, z_log_var = args\n",
    "#         batch = K.shape(z_mean)[0]\n",
    "#         dim = K.int_shape(z_mean)[1]\n",
    "#         epsilon = K.random_normal(shape=(batch, dim))\n",
    "#         return z_mean + K.exp(0.5 * z_log_var) * epsilon\n",
    "\n",
    "#     def _encoder_layers(self):\n",
    "#         inputs = Input(shape=(self.input_dim, self.input_dim, 1), name='encoder_input')\n",
    "#         x = inputs\n",
    "#         x = Conv2D(64, 3, activation='relu', strides=2, padding='same')(x)\n",
    "#         # dropout layer\n",
    "#         x = Dropout(0.2)(x)\n",
    "#         #  batch normalization\n",
    "#         x = BatchNormalization()(x)\n",
    "#         x = Conv2D(32, 3, activation='relu', strides=2, padding='same')(x)\n",
    "#         x = Dropout(0.2)(x)\n",
    "#         x = BatchNormalization()(x)\n",
    "    \n",
    "#         x = Flatten()(x)\n",
    "#         x = Dense(16, activation='relu')(x)\n",
    "\n",
    "#         z_mean = Dense(self.latent_dim, name='z_mean')(x)\n",
    "#         z_log_var = Dense(self.latent_dim, name='z_log_var')(x)\n",
    "#         z = Lambda(self.sampling, output_shape=(self.latent_dim,), name='z')([z_mean, z_log_var])\n",
    "#         return inputs, z_mean, z_log_var, z\n",
    "    \n",
    "#     def _decoder_layers(self, x):\n",
    "        \n",
    "#         x = UpSampling2D()(x)\n",
    "#         x = Conv2DTranspose(32, 3, activation='relu', strides=2, padding='same')(x)\n",
    "#         x = Dropout(0.2)(x)\n",
    "#         x = BatchNormalization()(x)\n",
    "#         x = UpSampling2D()(x)\n",
    "#         x = Conv2DTranspose(64, 3, activation='relu', strides=2, padding='same')(x)\n",
    "#         x = Dropout(0.2)(x)\n",
    "#         x = BatchNormalization()(x)\n",
    "\n",
    "#         return x\n",
    "\n",
    "\n",
    "\n",
    "#     def _loss(self):\n",
    "#         reconstruction_loss = mse(K.flatten(self.inputs), K.flatten(self.outputs))\n",
    "#         reconstruction_loss *= self.input_dim[0] * self.input_dim[1]\n",
    "#         kl_loss = 1 + self.z_log_var - K.square(self.z_mean) - K.exp(self.z_log_var)\n",
    "#         kl_loss = K.sum(kl_loss, axis=-1)\n",
    "#         kl_loss *= -0.5\n",
    "#         vae_loss = K.mean(reconstruction_loss + kl_loss)\n",
    "#         return vae_loss\n",
    "\n",
    "\n",
    "#     def _build(self):\n",
    "#         # encoder\n",
    "#         inputs = Input(shape=(self.input_dim, self.input_dim, 1), name='encoder_input')\n",
    "#         x = inputs\n",
    "\n",
    "#         #  encoder layers\n",
    "#         inputs, z_mean, z_log_var, z = self._encoder_layers()\n",
    "#         self.encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')\n",
    "\n",
    "#         latent_inputs = Input(shape=(self.latent_dim,), name='z_sampling')\n",
    "#         #  shape spatial dimension as list\n",
    "#         shape_spatial = [1, self.input_dim, self.input_dim]\n",
    "#         x = Dense(np.prod(shape_spatial), activation='relu')(latent_inputs)\n",
    "#         x = Reshape(shape_spatial[1:])(x)\n",
    "\n",
    "#         #  decoder layers\n",
    "#         x = self._decoder_layers(x)\n",
    "#         outputs = Conv2DTranspose(1, 3, activation='sigmoid', padding='same', name='decoder_output')(x)\n",
    "\n",
    "#         self.decoder = Model(latent_inputs, outputs, name='decoder')\n",
    "\n",
    "#         self.outputs = self.decoder(self.encoder(inputs)[2])\n",
    "#         self.model = Model(inputs, self.outputs, name='vae_mlp')\n",
    "        \n",
    "#         self.model.compile(optimizer='adam', loss=self._loss())\n",
    "\n",
    "#     def summary(self):\n",
    "#         self.encoder.summary()\n",
    "#         self.decoder.summary()\n",
    "#         self.model.summary()\n",
    "\n",
    "#     def train(self, x_train, batch_size=128, epochs=10):\n",
    "#         self.model.fit(x_train, x_train, batch_size=batch_size, epochs=epochs)\n",
    "\n",
    "#     def save(self, path):\n",
    "#         self.encoder.save(path + 'encoder.h5')\n",
    "#         self.decoder.save(path + 'decoder.h5')\n",
    "#         self.model.save(path + 'vae.h5')\n",
    "\n",
    "#     def load(self, path):\n",
    "#         self.encoder = load_model(path + 'encoder.h5')\n",
    "#         self.decoder = load_model(path + 'decoder.h5')\n",
    "#         self.model = load_model(path + 'vae.h5')\n",
    "\n",
    "#     def predict(self, x):\n",
    "#         return self.model.predict(x)\n",
    "    \n",
    "#     def predict_and_plot(self, x):\n",
    "#         x_pred = self.predict(x)\n",
    "#         plt.figure(figsize=(10, 10))\n",
    "#         for i in range(10):\n",
    "#             plt.subplot(2, 10, i + 1)\n",
    "#             plt.imshow(x[i], cmap='gray')\n",
    "#             plt.axis('off')\n",
    "#             plt.subplot(2, 10, i + 11)\n",
    "#             plt.imshow(x_pred[i], cmap='gray')\n",
    "#             plt.axis('off')\n",
    "#         plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VAE Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VAE class with 2 encoding layers + 1 latent layer + 2 decoding layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  build a VAE ( Variational Autoencoder) with latent dimension = 5 \n",
    "\n",
    "\n",
    "class VAE2:\n",
    "\n",
    "    def __init__(self, input_dim, latent_dim):\n",
    "        self.input_dim = input_dim\n",
    "        self.latent_dim = latent_dim\n",
    "        self.encoder = None\n",
    "        self.decoder = None\n",
    "        self.model = None\n",
    "        \n",
    "        self._build()\n",
    "\n",
    "    def sampling(self, args):\n",
    "        z_mean, z_log_var = args\n",
    "        batch = K.shape(z_mean)[0]\n",
    "        dim = K.int_shape(z_mean)[1]\n",
    "        epsilon = K.random_normal(shape=(batch, dim))\n",
    "        return z_mean + K.exp(0.5 * z_log_var) * epsilon\n",
    "\n",
    "    def _build(self):\n",
    "        # encoder\n",
    "        inputs = Input(shape=(self.input_dim, self.input_dim, 1), name='encoder_input')\n",
    "        x = inputs\n",
    "\n",
    "        # 64 filters of 3x3 each with stride 2 and relu activation function\n",
    "        for i in range(2):\n",
    "            filters = 32 * 2 ** (2 - i)\n",
    "            x = Conv2D(filters=filters,\n",
    "                       kernel_size=3,\n",
    "                       activation='relu',\n",
    "                       strides=2,\n",
    "                       padding='same')(x)\n",
    "\n",
    "        # shape info needed to build decoder model\n",
    "        shape = K.int_shape(x)\n",
    "\n",
    "        # generate latent vector Q(z|X)\n",
    "        x = Flatten()(x)\n",
    "\n",
    "        # mu and sigma for latent vector z\n",
    "        self.mu = Dense(self.latent_dim, name='latent_mu')(x)\n",
    "        self.log_var = Dense(self.latent_dim, name='latent_log_var')(x)\n",
    "\n",
    "        # use reparameterization trick to push the sampling out as input\n",
    "        z = Lambda(self.sampling, output_shape=(self.latent_dim,), name='z')([self.mu, self.log_var])\n",
    "\n",
    "        # instantiate encoder model\n",
    "        self.encoder = Model(inputs, [self.mu, self.log_var, z], name='encoder')\n",
    "        self.encoder.summary()\n",
    "\n",
    "        # build decoder model\n",
    "\n",
    "        # latent inputs to reconstruct inputs from latent space\n",
    "        latent_inputs = Input(shape=(self.latent_dim,), name='z_sampling')\n",
    "\n",
    "        # reshape to (7, 7, 32) which is the shape of the last conv layer in encoder\n",
    "        x = Dense(shape[1] * shape[2] * shape[3], activation='relu')(latent_inputs)\n",
    "\n",
    "        # reshape to (7, 7, 32)\n",
    "        x = Reshape((shape[1], shape[2], shape[3]))(x)\n",
    "\n",
    "        # use Conv2DTranspose to reverse the conv layers from the encoder\n",
    "        for i in range(2):\n",
    "            filters = 32 * 2 ** i\n",
    "            x = Conv2DTranspose(filters=filters,\n",
    "                                kernel_size=3,\n",
    "                                activation='relu',\n",
    "                                strides=2,\n",
    "                                padding='same')(x)\n",
    "\n",
    "        outputs = Conv2DTranspose(filters=1,\n",
    "                                  kernel_size=3,\n",
    "                                  activation='sigmoid',\n",
    "                                  padding='same',\n",
    "                                  name='decoder_output')(x)\n",
    "\n",
    "        # instantiate decoder model\n",
    "        self.decoder = Model(latent_inputs, outputs, name='decoder')\n",
    "        self.decoder.summary()\n",
    "\n",
    "        # instantiate VAE model\n",
    "        outputs = self.decoder(self.encoder(inputs)[2])\n",
    "        self.model = Model(inputs, outputs, name='vae_mlp')\n",
    "\n",
    "        reconstruction_loss = mse(K.flatten(inputs), K.flatten(outputs))\n",
    "        reconstruction_loss *= self.input_dim * self.input_dim\n",
    "        kl_loss = 1 + self.log_var - K.square(self.mu) - K.exp(self.log_var)\n",
    "        kl_loss = K.sum(kl_loss, axis=-1)\n",
    "        kl_loss *= -0.5\n",
    "        vae_loss = K.mean(reconstruction_loss + kl_loss)\n",
    "        self.model.add_loss(vae_loss)\n",
    "        self.model.compile(optimizer='adam')\n",
    "        self.model.summary()\n",
    "        \n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VAE class with 4 encoding layers + 1 latent layer + 4 decoding layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  build a VAE ( Variational Autoencoder) with latent dimension = 5\n",
    "\n",
    "\n",
    "class VAE2:\n",
    "\n",
    "    def __init__(self, input_dim, latent_dim):\n",
    "        \n",
    "        self.input_dim = input_dim\n",
    "        self.latent_dim = latent_dim\n",
    "        self.encoder = None\n",
    "        self.decoder = None\n",
    "        self.model = None\n",
    "        \n",
    "        self._build()\n",
    "\n",
    "    def sampling(self, args):\n",
    "        z_mean, z_log_var = args\n",
    "        batch = K.shape(z_mean)[0]\n",
    "        dim = K.int_shape(z_mean)[1]\n",
    "        epsilon = K.random_normal(shape=(batch, dim))\n",
    "        return z_mean + K.exp(0.5 * z_log_var) * epsilon\n",
    "\n",
    "\n",
    "    def _build(self):\n",
    "        # encoder\n",
    "        inputs = Input(shape=(self.input_dim, self.input_dim, 1), name='encoder_input')\n",
    "        x = inputs\n",
    "\n",
    "        # 64 filters of 3x3 each with stride 2 and relu activation function\n",
    "        x = Conv2D(filters = 128, kernel_size = 3, activation='relu', strides=2, padding='same')(x)\n",
    "        \n",
    "        \n",
    "        # decrerase filter \n",
    "        x = Conv2D(filters = 64, kernel_size = 3, activation='relu', strides=2, padding='same')(x)\n",
    "        \n",
    "        \n",
    "        # decrerase filter\n",
    "        x = Conv2D(32, 3, activation='relu', strides=2, padding='same')(x)\n",
    "        \n",
    "        \n",
    "        # decrerase filter\n",
    "        x = Conv2D(16, 3, activation='relu', strides=2, padding='same')(x)\n",
    "        \n",
    "        \n",
    "\n",
    "        # for i in range(2):\n",
    "\n",
    "        #     filters = 32 * 2 ** (2 - i)\n",
    "        #     x = Conv2D(filters=filters,\n",
    "        #                kernel_size=3,\n",
    "        #                activation='relu',\n",
    "        #                strides=2,\n",
    "        #                padding='same')(x)\n",
    "\n",
    "        \n",
    "\n",
    "        # shape info needed to build decoder model\n",
    "        shape = K.int_shape(x)\n",
    "\n",
    "        # generate latent vector Q(z|X)\n",
    "        x = Flatten()(x)\n",
    "\n",
    "        # mu and sigma for latent vector z\n",
    "        self.mu = Dense(self.latent_dim, name='latent_mu')(x)\n",
    "        self.log_var = Dense(self.latent_dim, name='latent_log_var')(x)\n",
    "\n",
    "        # use reparameterization trick to push the sampling out as input\n",
    "        z = Lambda(self.sampling, output_shape=(self.latent_dim,), name='z')([self.mu, self.log_var])\n",
    "\n",
    "        # instantiate encoder model\n",
    "        self.encoder = Model(inputs, [self.mu, self.log_var, z], name='encoder')\n",
    "        self.encoder.summary()\n",
    "\n",
    "        # build decoder model\n",
    "\n",
    "        # latent inputs to reconstruct inputs from latent space\n",
    "        latent_inputs = Input(shape=(self.latent_dim,), name='z_sampling')\n",
    "\n",
    "        # reshape to (7, 7, 32) which is the shape of the last conv layer in encoder\n",
    "        x = Dense(shape[1] * shape[2] * shape[3], activation='relu')(latent_inputs)\n",
    "\n",
    "        # reshape to (7, 7, 32)\n",
    "        x = Reshape((shape[1], shape[2], shape[3]))(x)\n",
    "\n",
    "        # # use Conv2DTranspose to reverse the conv layers from the encoder\n",
    "        x = Conv2DTranspose(16, 3, activation='relu', strides=2, padding='same')(x)\n",
    "       \n",
    "        # decrerase filter\n",
    "        x = Conv2DTranspose(filters = 32, kernel_size = 3, activation='relu', strides=2, padding='same')(x)\n",
    "        \n",
    "        \n",
    "        # decrerase filter\n",
    "        x = Conv2DTranspose(filters = 62, kernel_size = 3, activation='relu', strides=2, padding='same')(x)\n",
    "        \n",
    "        \n",
    "        # decrerase filter\n",
    "        x = Conv2DTranspose(128, 3, activation='relu', strides=2, padding='same')(x)\n",
    "        \n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        # for i in range(2):\n",
    "        #     filters = 32 * 2 ** i\n",
    "        #     x = Conv2DTranspose(filters=filters,\n",
    "        #                         kernel_size=3,\n",
    "        #                         activation='relu',\n",
    "        #                         strides=2,\n",
    "        #                         padding='same')(x)\n",
    "\n",
    "        outputs = Conv2DTranspose(filters=1,\n",
    "                                  kernel_size=3,\n",
    "                                  activation='sigmoid',\n",
    "                                  padding='same',\n",
    "                                  name='decoder_output')(x)\n",
    "\n",
    "        # instantiate decoder model\n",
    "        self.decoder = Model(latent_inputs, outputs, name='decoder')\n",
    "        self.decoder.summary()\n",
    "\n",
    "        # instantiate VAE model\n",
    "        outputs = self.decoder(self.encoder(inputs)[2])\n",
    "        self.model = Model(inputs, outputs, name='vae_mlp')\n",
    "\n",
    "        reconstruction_loss = mse(K.flatten(inputs), K.flatten(outputs))\n",
    "        reconstruction_loss *= self.input_dim * self.input_dim\n",
    "        kl_loss = 1 + self.log_var - K.square(self.mu) - K.exp(self.log_var)\n",
    "        kl_loss = K.sum(kl_loss, axis=-1)\n",
    "        kl_loss *= -0.5\n",
    "        vae_loss = K.mean(reconstruction_loss + kl_loss)\n",
    "        self.model.add_loss(vae_loss)\n",
    "        self.model.compile(optimizer='adam')\n",
    "        self.model.summary()\n",
    "        \n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VAE with latent dimension 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On VAE with 2 + 1 + 2 layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " encoder_input (InputLayer)     [(None, 100, 100, 1  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 50, 50, 32)   320         ['encoder_input[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 25, 25, 64)   18496       ['conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 40000)        0           ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " latent_mu (Dense)              (None, 5)            200005      ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " latent_log_var (Dense)         (None, 5)            200005      ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " z (Lambda)                     (None, 5)            0           ['latent_mu[0][0]',              \n",
      "                                                                  'latent_log_var[0][0]']         \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 418,826\n",
      "Trainable params: 418,826\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " z_sampling (InputLayer)     [(None, 5)]               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 40000)             240000    \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 25, 25, 64)        0         \n",
      "                                                                 \n",
      " conv2d_transpose (Conv2DTra  (None, 50, 50, 128)      73856     \n",
      " nspose)                                                         \n",
      "                                                                 \n",
      " conv2d_transpose_1 (Conv2DT  (None, 100, 100, 64)     73792     \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " decoder_output (Conv2DTrans  (None, 100, 100, 1)      577       \n",
      " pose)                                                           \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 388,225\n",
      "Trainable params: 388,225\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"vae_mlp\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " encoder_input (InputLayer)     [(None, 100, 100, 1  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " encoder (Functional)           [(None, 5),          418826      ['encoder_input[0][0]']          \n",
      "                                 (None, 5),                                                       \n",
      "                                 (None, 5)]                                                       \n",
      "                                                                                                  \n",
      " decoder (Functional)           (None, 100, 100, 1)  388225      ['encoder[0][2]']                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 50, 50, 32)   320         ['encoder_input[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 25, 25, 64)   18496       ['conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 40000)        0           ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " latent_log_var (Dense)         (None, 5)            200005      ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " latent_mu (Dense)              (None, 5)            200005      ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " tf.reshape_1 (TFOpLambda)      (None,)              0           ['decoder[0][0]']                \n",
      "                                                                                                  \n",
      " tf.reshape (TFOpLambda)        (None,)              0           ['encoder_input[0][0]']          \n",
      "                                                                                                  \n",
      " tf.__operators__.add (TFOpLamb  (None, 5)           0           ['latent_log_var[0][0]']         \n",
      " da)                                                                                              \n",
      "                                                                                                  \n",
      " tf.math.square (TFOpLambda)    (None, 5)            0           ['latent_mu[0][0]']              \n",
      "                                                                                                  \n",
      " tf.convert_to_tensor (TFOpLamb  (None,)             0           ['tf.reshape_1[0][0]']           \n",
      " da)                                                                                              \n",
      "                                                                                                  \n",
      " tf.cast (TFOpLambda)           (None,)              0           ['tf.reshape[0][0]']             \n",
      "                                                                                                  \n",
      " tf.math.subtract (TFOpLambda)  (None, 5)            0           ['tf.__operators__.add[0][0]',   \n",
      "                                                                  'tf.math.square[0][0]']         \n",
      "                                                                                                  \n",
      " tf.math.exp (TFOpLambda)       (None, 5)            0           ['latent_log_var[0][0]']         \n",
      "                                                                                                  \n",
      " tf.math.squared_difference (TF  (None,)             0           ['tf.convert_to_tensor[0][0]',   \n",
      " OpLambda)                                                        'tf.cast[0][0]']                \n",
      "                                                                                                  \n",
      " tf.math.subtract_1 (TFOpLambda  (None, 5)           0           ['tf.math.subtract[0][0]',       \n",
      " )                                                                'tf.math.exp[0][0]']            \n",
      "                                                                                                  \n",
      " tf.math.reduce_mean (TFOpLambd  ()                  0           ['tf.math.squared_difference[0][0\n",
      " a)                                                              ]']                              \n",
      "                                                                                                  \n",
      " tf.math.reduce_sum (TFOpLambda  (None,)             0           ['tf.math.subtract_1[0][0]']     \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " tf.math.multiply (TFOpLambda)  ()                   0           ['tf.math.reduce_mean[0][0]']    \n",
      "                                                                                                  \n",
      " tf.math.multiply_1 (TFOpLambda  (None,)             0           ['tf.math.reduce_sum[0][0]']     \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " tf.__operators__.add_1 (TFOpLa  (None,)             0           ['tf.math.multiply[0][0]',       \n",
      " mbda)                                                            'tf.math.multiply_1[0][0]']     \n",
      "                                                                                                  \n",
      " tf.math.reduce_mean_1 (TFOpLam  ()                  0           ['tf.__operators__.add_1[0][0]'] \n",
      " bda)                                                                                             \n",
      "                                                                                                  \n",
      " add_loss (AddLoss)             ()                   0           ['tf.math.reduce_mean_1[0][0]']  \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 807,051\n",
      "Trainable params: 807,051\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#  build a VAE ( Variational Autoencoder) with latent dimension = 5\n",
    "vae = VAE(128, 5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On VAE with 4 + 1 + 4 layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae2 = VAE2(128, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VAE with latent dimention 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On VAE with 2 + 1 + 2 layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae3 = VAE(128, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On VAE with 4 + 1 + 4 layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae4 = VAE2(128, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traing VAE model (latent dimension 5) for 50 epoches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "40/40 [==============================] - 218s 5s/step - loss: 801.9382\n",
      "Epoch 2/50\n",
      "40/40 [==============================] - 189s 5s/step - loss: 177.4070\n",
      "Epoch 3/50\n",
      "40/40 [==============================] - 180s 4s/step - loss: 173.8675\n",
      "Epoch 4/50\n",
      "40/40 [==============================] - 171s 4s/step - loss: 172.6710\n",
      "Epoch 5/50\n",
      "40/40 [==============================] - 200s 5s/step - loss: 170.4948\n",
      "Epoch 6/50\n",
      "40/40 [==============================] - 202s 5s/step - loss: 168.2906\n",
      "Epoch 7/50\n",
      "40/40 [==============================] - 202s 5s/step - loss: 166.4616\n",
      "Epoch 8/50\n",
      "40/40 [==============================] - 179s 4s/step - loss: 166.0556\n",
      "Epoch 9/50\n",
      "40/40 [==============================] - 180s 4s/step - loss: 166.1990\n",
      "Epoch 10/50\n",
      "40/40 [==============================] - 174s 4s/step - loss: 165.3586\n",
      "Epoch 11/50\n",
      "40/40 [==============================] - 189s 5s/step - loss: 165.2150\n",
      "Epoch 12/50\n",
      "40/40 [==============================] - 182s 5s/step - loss: 165.0382\n",
      "Epoch 13/50\n",
      "40/40 [==============================] - 184s 5s/step - loss: 164.6458\n",
      "Epoch 14/50\n",
      "26/40 [==================>...........] - ETA: 1:12 - loss: 163.8917"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [24], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[39m#  train the VAE model\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m vae\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mfit(X_train, epochs\u001b[39m=\u001b[39;49m\u001b[39m50\u001b[39;49m, batch_size\u001b[39m=\u001b[39;49m\u001b[39m128\u001b[39;49m)\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m     66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\keras\\engine\\training.py:1650\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1642\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[0;32m   1643\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   1644\u001b[0m     epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1647\u001b[0m     _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[0;32m   1648\u001b[0m ):\n\u001b[0;32m   1649\u001b[0m     callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1650\u001b[0m     tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[0;32m   1651\u001b[0m     \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[0;32m   1652\u001b[0m         context\u001b[39m.\u001b[39masync_wait()\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m    151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:880\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    877\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    879\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 880\u001b[0m   result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m    882\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    883\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:912\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    909\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[0;32m    910\u001b[0m   \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    911\u001b[0m   \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 912\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_no_variable_creation_fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)  \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    913\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_variable_creation_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    914\u001b[0m   \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    915\u001b[0m   \u001b[39m# in parallel.\u001b[39;00m\n\u001b[0;32m    916\u001b[0m   \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compiler.py:134\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    131\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[0;32m    132\u001b[0m   (concrete_function,\n\u001b[0;32m    133\u001b[0m    filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m--> 134\u001b[0m \u001b[39mreturn\u001b[39;00m concrete_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[0;32m    135\u001b[0m     filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mconcrete_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\monomorphic_function.py:1745\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1741\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1742\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1743\u001b[0m     \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1744\u001b[0m   \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1745\u001b[0m   \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[0;32m   1746\u001b[0m       ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[0;32m   1747\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1748\u001b[0m     args,\n\u001b[0;32m   1749\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1750\u001b[0m     executing_eagerly)\n\u001b[0;32m   1751\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\monomorphic_function.py:378\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    376\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[0;32m    377\u001b[0m   \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 378\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[0;32m    379\u001b[0m         \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[0;32m    380\u001b[0m         num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[0;32m    381\u001b[0m         inputs\u001b[39m=\u001b[39;49margs,\n\u001b[0;32m    382\u001b[0m         attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[0;32m    383\u001b[0m         ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[0;32m    384\u001b[0m   \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    385\u001b[0m     outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m    386\u001b[0m         \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[0;32m    387\u001b[0m         num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    390\u001b[0m         ctx\u001b[39m=\u001b[39mctx,\n\u001b[0;32m    391\u001b[0m         cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
      "File \u001b[1;32mc:\\Users\\ishikaj\\Desktop\\ML assignment 7 auto encoders\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     51\u001b[0m   ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 52\u001b[0m   tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[0;32m     53\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[0;32m     54\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m     55\u001b[0m   \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#  train the VAE model\n",
    "vae.model.fit(X_train, epochs=50, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### train VAE with latent dimension 10 for 50 epoches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  train the VAE model\n",
    "vae2.model.fit(X_train, epochs=50, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### generating images with latent dimension 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 42ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x247f645ee90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hc9kY069tG3727E2Mtllv+yymLbMX4ONkr5Udp7DWjdd227Zteftk5xO2A0CFRS1W36yqqirvvvbcWFtrry2nbduGz4Bta88v/GyfHWazPY43xux+YveEHSf7OcaDYtuy8y3Vcxf7G2NLvIcwe71xCvu15xajy1dTU5N1nK6urkxplnyUfAJau3Yttm3bhsbGxqzvGxsb8fLLL/fYv6WlBVdffXWP7ysqKjIDzm4kO2BsX7utVPuWS9tStCvXtt64FNrWs5ftW8x1jfkPDWvr3f99bZw8m9i2vvbcxfZZ6BiX8p5g91uu43jnlPo6oFmzZuGyyy7LfO7o6MCYMWN6/E+ut7D6JHY2tz8CrC3735X3VhZu9/4nz/otlb3WpveqbTHj5LUN+7X/a7Y2hccq5T1h/xcaHtvab9W5w7bhG7/X1p7r5s2badvQJnuu7H+73v/Ww7ce760+5hlg/do3LUbs/RSORTH2lrItw95PYVtvnMJ+Y8cp3J7Lk+VR8glo9913R//+/XtI8Le2tuYsClZdXY3q6upSmyGEEKLMKXkadlVVFSZMmIBFixZlvuvu7saiRYswadKkUncnhBCij7JDXHCXXXYZpk6disMPPxxHHnkkbrrpJnR2duKss87aEd0VjBcUD2Gvpr193SzEJubCKtTeNNv2dlsx+zKXm3fcmHP1XE3sHrH7MheW15btG+Mu9Y7F2pbqOnttY8690GenGIq5p2PdX6WiVP0UYv8OmYA+85nP4K233sKcOXOwevVqfOADH8ADDzzQIzFBCCHErkvZacF1dHSgoaFhhxzby/5g/7uKycRjbWPfLljbQu3N1c971ZYdJ2acWL82QB5zi8ckIdgEAPuZvdXYfsK2NhmAtbXH8RIYYu4nlhQSm0zD2sZcn5i3mvfqme3tcWzbGHtj+2Ep2zHHiWkb3sNJkqC7uxvt7e0YPHhw3jbSghNCCJEKqadhlxMxKcP52hWyne3LbCpV/v6ObBtzHNbWe6thCxZZW++4bIEfW/wK8Gtn28ak6odtvfRo9vYUc11jFkl622JiNTFvs8U8ZzHb2fkUY++Oeu6KOW4MhcTF9QYkhBAiFTQBCSGESAVNQEIIIVJhl4oBeXpcpcrGYft6WVbsWMXYywQhY2MzMW1ZzMHCsrs8wv094Ux2PTzB2hAbxwnFGAGeQWT7DdVArJyOlVIJ21ob1q1bR9uG2HPzZHzy2QBkS8HEXGfPRtbWtivV+jS7fzFZcMWca6lix95vDDtXm1XJCO/FJEl61VZvQEIIIVJBE5AQQohU2KldcLEy5IVKmse4HGIXebL9Y1w8MW4Ez/3F1HO9fhmhmzD2OOHrv3U32mMxdx1z39lt1uVmRXVZ3SG7b3h+nZ2deW2wdjBJn1zbmUoyS/8u5eJeC1PhjnXFhuyohagxzx3DSzmPgT0vntuvmBTufNuUhi2EEKKs0QQkhBAiFTQBCSGESIWdLgbE4i3WD299+mFbT+IkxkfMZGKs/9v6a1lbC5MEYWnYnr3MD+xVBQ3b2pRh1q+1wcZb7LiF2629dXV1WZ/DlOHa2tq89gI8PZqJLNrtGzdupDZt2rQpZ59Az5hQfX193m0jRoygbcNjW5tsv+H52mtnxz8cR1ud07Zl6dP2uOy+tfuytl68xauMm88GgFfjZYKpLL0e4PFV77nLZ1+uftk42XuexYvC+ydJkh73QS70BiSEECIVNAEJIYRIBU1AQgghUmGniwGx2Iy3PoStr2C+3ZhSAd7aC2aTbWv9vqHNdhvzRVsbvFgNk/lgZQaqqqrocZkv2h7Xfg79z3acbJwnjL94PvtwHD3ZHnbdbczHth04cGDm7/b29qxtNv4V2uHFCG3bcNzY+hsg+zrb+FBXV1fW5/BYXqyVSbR49yKT/LFtw37tubL4r/3sraNhMaAYKSQvVhba7D0foU1erImV97CwMQ/HWOuAhBBClDWagIQQQqSCJiAhhBCpUNYxoO3+xUJ1p7y1MNavzfLsWTwppqSC9T3H6DTZGAqLOXilpZn/34t/xWiEMb+1JYwN2D69eEW+Pj28tQrhOghrg11HE6M9xuJ39rg2ZhLeB9Z+G5ux22PuW6btZe1nsRlvjQ27tmzNindPMC0+S4wOHvttiLnO3u+Tfd5D2LUCeLl4FqOzMcO33nor73Fsv+F4KwYkhBCirNEEJIQQIhXK2gVXamLKDHjSHSzNMaZ0g5f2WKpyDEym30tnZdIpMeUlrMuESfzYPq0rKabMA5OG8aRfWMqwl9obuu+YK8Zut/bafcN+vaqmFnbtmDvSu0/DcWKu4VzEpO+yMgPsmbXbYiqTWthYeLJcrJSJV/ogptwBc4fFlKGJCX+w35R86A1ICCFEKmgCEkIIkQqagIQQQqRC2caAKioqSp6GzaQ6gGxfdEw5Bgtr6/lfmc84Rurdk/jp7XE8m719w35tzIS1LWV5YhaD8GRjwlRYdm1ytQ1TWr0SF+H2sNwCwNOYbSzDpu56cSpmU0zqe4j37LB+Y5Y0eHFOhheLDcfJk/QqtES9d67s/LxlIqwMynvx3CVJ0qvroTcgIYQQqaAJSAghRCpoAhJCCJEKZRsDion7hLDYjCepwShVW2/tAvPPxrSNWcvjjXVMHIG19XzerByGt+6HlbxgckHWn2+lbJi8UYxckCdHE5bkjokNWHtZWQEgOx4Q2zaExfO8uCAbp5g4SAyxzx1ry2KKnjRViBcvKtXvk/e8MgmyGLQOSAghRJ9BE5AQQohUKFsXXKHEVCe0atghXrp02Na6Mizh6yir8AjwVGumQGzxUoRZ6mhMNVj7qs1kb2KqLdp0Yi8NPry2tq2V0wnHgilNW5s9RXG7PXSrWfvtPRO2tePNXD620qqXKs7Sc0N7rc2eCzFfH4Cv1ByOhbfUIGzrSWAxNWzPxcueWeaK9aqcMmknaxNLqffSpcO2MUs/rBp2DDHyWNvRG5AQQohU0AQkhBAiFTQBCSGESIWyjgEVmoqdr31MZUNLTOpxTKVSz1/OZHws4XZPAp/h+ZdjUktjSjeEY+zFfDxpG0a4r1cmIcYG25ZJ4tt9w9iA9cNbG1mVWS+VutASBd6zU6gMVK79Q9h1jpGB8mDX1ntG2f3Ezs2TBospL1HM88BifTHELO3I9F1wb0IIIUQRaAISQgiRCpqAhBBCpEJZx4AKKccQ+j5j4isWT/KD9RMjKe/FIELfrrU3Zm0Pi1fElp6Ikdxg607YuXq+dNbW83+H605sPyxm4pUFtzaF+3vlyEO8eCOLU9m1bXbdSUx5jNB+LwbE1ud4a3sKjUHYcWLrgmLuabvde5ZiYmdMMspry0o5xJRMKUZai2FjkyrHIIQQomzRBCSEECIVytoFV+o0bE+Vl6VLs9fYYhStPZmMQpVqi1HDjkk7jUl1t64L5p7wpIQs7NrFjJu9J0LXkufaK0ZJOOzX6yc8P+sWjHGJsrRxi71WzFXm3dOsHy+VOmxbTKXVmH5jKrzGPHcWT/k7lNeJeWa9e6JUadhs2UHevgvuTQghhCgCTUBCCCFSQROQEEKIVCjrGFAhadgx5RiY3Lntk1WP9PyxTNLfq97JziemAqdNgWa+dE/mI8YPH/br7ctKKsTEymIk5W0MxaYxh/2w+yVXv5s3b8787d3DrESEHbfQZnttWIkOu91LQQ9t9koFxDyjdpw6Ozvz2mCfDyZDZGHPjn2eWdzQK8PBYrz2uWMlFby2MSVUYuI64fbBgwfTfUuN3oCEEEKkgiYgIYQQqaAJSAghRCqUdQyokHVAMdLurD9vHUpMSYIYqQtP9oMR2uTJn8Ss5SlU2t2DxXFipfZtTKK3eCUVWAzLWy/FShQUUr44lx3emidmsxcbYGUGLGxtTzFSL+z+98awmJIkMW2Z5E+MLJfXJ3u2innuSiXFo3IMQggh+gyagIQQQqSCJiAhhBCpULYxoIqKioyvMkZjK/SFej57Vs4gps8YuXnPR8y0o7w4QkzMjGldefEWptkWE+tgunh2TL0YXGiH5w8PYx9e2YdwXZCn68U+27U9LGbFyiJYvBIdTO/QYtfnhDbHlC63fdrP7Ln0xjTEW+cXc5/GlGNga+osLKbr/caw0iAx+nR2bVsxmoUMq8eocgxCCCHKFk1AQgghUqFsXXDFlmLIhZdyGxIjC+8dN+a1m72yx5Ry8OR1mIvBc08wWL9e2myMi4GNcYzbyXPthf1648LGyStFESODHx7LuueY2waIS8Nm4xaTXuxdu3z25WobPgMxLugYe22/3pKAmDRs9tx50jwxqdYxLmnrUiyUmErJ29EbkBBCiFTQBCSEECIVoiaglpYWHHHEERg0aBBGjBiBU045BUuXLs3aZ/PmzZg2bRqGDRuG+vp6nHrqqWhtbS2p0UIIIfo+UTGgRx55BNOmTcMRRxyBrVu34stf/jJOOOEEvPTSS6irqwMAXHrppfj1r3+NhQsXoqGhAdOnT8cnP/lJPPbYYzvkBCzM92i3MUl5Ly4StvV8uUza3UsPDfHSKVm8IkYWnsnRANlxBy8uwvzLNsYQHsumBMekGzMZe3sse0/YMWb3hHeuoY2eHz68n7yYGytbwUoF2O227caNG/O29eJq4Wcv1mf7De8nL3YZtvViomEaeaxEDntmWZzQbrNt2bnGlIuxsFIO3rMTfh4+fHjePnYEURPQAw88kPX5jjvuwIgRI7BkyRL8n//zf9De3o7vfe97uPvuu3HccccBABYsWID3v//9ePzxx/HBD36wxzG7urqyanN0dHQUch5CCCH6GEXFgNrb2wEAu+22GwBgyZIleOedd9DU1JTZZ/z48Rg7diwWL16c8xgtLS1oaGjI/BszZkwxJgkhhOgjFDwBdXd3Y8aMGZg8eTIOOuggAMDq1atRVVWFIUOGZO3b2NiI1atX5zzOrFmz0N7envm3cuXKQk0SQgjRhyh4HdC0adPw4osv4tFHHy3KgOrq6h5+9+2Uei1QTB69hUmWe+sEYiTlPX8ta8vKMTCKkfjx7O/tNoCfa6ysUm/7tXEbtqbLW2MTI8HkyciExMjle/dieKyYdUwx5+pdG3YtvdhlDGxdGdsXyL4+XqyGrQPyPufrE4j7/WPxSW8NWkz8sdQU9Is8ffp03H///Xj44Yex5557Zr4fOXIktmzZgra2tqz9W1tbMXLkyKIMFUIIsXMRNQElSYLp06fj5z//OR566CGMGzcua/uECRMwYMAALFq0KPPd0qVLsWLFCkyaNKk0FgshhNgpiHLBTZs2DXfffTd++ctfYtCgQZm4TkNDA2pra9HQ0IAvfOELuOyyy7Dbbrth8ODBuOiiizBp0qScGXAe218rC1V5jnXbhK/WnsRJeCxP0ZrhqRezNGDPNcAoRtHaqt72dl9Psig8H89twCrWxqRse67KcF97T3guk9C17KVHs/vW9stcPtYm2y9TfLf3D0ttt/2GafOe+jUjRiLHEuNCtCnOMZI5xbioWRo5W5Zg949RsI9xY3qp7excw+csSZJeyUtFTUDz588HABx77LFZ3y9YsABnnnkmAOCb3/wm+vXrh1NPPRVdXV1obm7Gd77znZhuhBBC7AJETUC9eROpqanBvHnzMG/evIKNEkIIsfMjLTghhBCpULblGIDC0rBjUjqZH9VLn2S+dBYXiZHesdu9GA+rKBpzrsWUY2BxkZiYXMy52s82ZmL9/TESOcx+Ly7FYlqWmBIFrPSEdz+FsHijJab6qPfcsthATKwmZvxj4y0sXd3Cnjsmr+Mt14i5tiwe7EkJsXP1rmW+3yeVYxBCCFHWaAISQgiRCpqAhBBCpEJZx4AKWQcUUwJ64MCBWZ83b96c+TtGPt/K2FuYP9bzw4eS8kwC33629lu5o1CB3PbpSe8zPzyTz7f+fBbHseUYvNhZeD6ejH1os40XsZIXdhyYrH2uY7NtMbIx4Rjbcw3vl1yE97i31oo9P/b6hOfjxTK8cWM2hf16ZduZvXacWDzJxo7Dew3gcVoW+/PGu7a2Nusz+52xYxHeF/aZZLHw+vr6vH3koli5NL0BCSGESAVNQEIIIVKhrF1wvYFJUHhp2OyV3UuRjHn1ZK/hMSmeTH7G4rm72DbPrcZS3a1NMbIxzIUYI/kTq3wcEiPxY8fYwqpfxqhh27bhfRxz/wBxytqhjTFyLjEK6XZ7zP3kwVSeY5Y/lBLmvvauDRunmN8y1m+Mir4lPDelYQshhChrNAEJIYRIBU1AQgghUqFsY0AVFRU507A932zoz7Q+ei+NNvSN2jRNJktufaoxJRU8SXwm088kNjy/b6ESP0Cc7EqIJ4Ef2uilq/dG6j1fv2FbLxU2TMu25+rJHYX729RdS2iTTRkuRrrGpnsz2ZWYfi3huHlp/JbQZm9ZArtXvXRvZlOhpUy8tuz58FLBLeH5sBR/eywvthReOy+uybC/C70ZU70BCSGESAVNQEIIIVJBE5AQQohUKNsYUKESD6wEtF3LYPuIiaHE+OWZD9Y7T7a+xR433M62ecSUv2bSO7atV86AHdezkdnL9vX8+Wz9i23ryeCwtkz6xV7LcHuszE24v7WXyfR7MTmG15attWLj5K0ZCtt6pabZsWLKqbMy2tYmCyupYI/txR9j1huFz2zMOjGLyjEIIYToM2gCEkIIkQqagIQQQqRC2caA+vXrl/F5xpQrDj9bX7r15dp1D6Hf0vowrQ3hsWO0rmyMxK4PYZpV1l62jonJswPZ0u5ebMb2y9YK2HEL28aso7F9sPVRtq21194H4Wc7/uyesKUa7P1kbd60aVNeGyx1dXV5j8N08ay9XgwltMk+O+E2i712tp+Ojo7M314JaLbeiMVlbb/2HmexGttnWJYC4CUkvDIbbG2ivWdi1gTG6O/Z8wn79WKi4fntscceve6zFOgNSAghRCpoAhJCCJEKZeuCy5fG50nxxKTcWliKZzHlGFiaqee+i3H1sTRsr5xBiFcpM9weI1vvpbYXUz2StbUuLZbuzY4bkyJs97fuFGavt2/MtWNLD7wU7pj04hgJFyZrFYOXhh2TUhxTtoKNf6HlLgB/DAvt1xuX0F1XTBkKFsLIh96AhBBCpIImICGEEKmgCUgIIUQqlG0MqLKyMuOPZLIfzGcf4zu3MGn9XLayfYuJSzFYiQKbdmoJ/b6xci6sTLgncRJi01nDcWNyIblsZnEpFgfx7A0/W3ttHMTaxNK9WRq5Jw8Uk/bLxthLzw2leuz423s85lyZPI13rkyyiKVh2+N68kAs3Zs9714MKDyW7dOTy2LPLIuRemXNw7GJkeyy2HIMvTmW3oCEEEKkgiYgIYQQqaAJSAghRCqUbQyou7s7Z0numHUzLN8d6OlrZ6UPmM/eiwGxMsje+iLW1hJT0pqtTfJKZ7O4FTsfL94VXjtPtiemrb3uIV7piRg5FOaXtzaw62P7ZLJEnr32cxif8eSmmEx/MWuevPIGjJgyKKx0vBerYeUY2D3uPTthXM177li/MaXK7fWw8eHwczHlGLQOSAghRJ9BE5AQQohUKFsX3IABA3K64CxM2sa+atpX5/r6+qzPYcqqrRbJXk09mZiQUPUY8F0oTCGa2eEpWIeuGPv6bm1iCtGeiyrs16vMGPbL3GYAT7G3bQcOHJj1ObQjVHHOZVO+drmwYxzjWgrbei6sUPnYc+0x95fnLgo/e5VvmeK7xd5PNm07xKaRh+fruU/Z8+C52MNxKmU15FCF3tpQW1tL24bPqb1H7O8Vc6vZ5yE8ltSwhRBC7BJoAhJCCJEKmoCEEEKkQtnGgMI07BBPOoWlYXs+ViZfYX3tLO3REh4rRlYllx1sW+h/tn5e20/ox46Vx2clCthnLw2e2ev51pkEE5MW8uIV7Lj2WrJ7wosfsaUG1v7wHveeh5jUZAuzmUkwxZTZALJjQF6JCyaj5MW/Qrw4W4xEFitbwWS57DjZZ5aVCvHks9g2VvE4ZtmBRWnYQggh+gyagIQQQqSCJiAhhBCpULYxoKqqqozPk5XKZn5U61O1/n4bEwr9n16Mh0nMMJl1r1RATDyGlRmIkVVn6xoA7jP2SlozeaAYuR17Ldn5eecerqOxMN+6bWftt23D+82u02AlCux9atsyG+wY2rbhZ7vGxhKejxf/Cu8JT7qGlWOIKVthn197PmFbVj4il01MAovFgz15o7Bfay9bhwXwGFBMyRdrY6G/G9YmW+q+N3FyvQEJIYRIBU1AQgghUqFsXXBbt24tSA07dAV4kiwxqs7MzealzbI07JiUVea6sMfyXAxMOoVVTAS4C46lzcYomVv3hJceGlPVMbwenhskxJN+sTA3LRu3mBR/T0aJ9eOpUrMlAsxGrxKxbRtzj4TXy6v6y6rZskrKdjurdGv39VSqWeVe+7vBXK+e2j3b1/4uhufjjT+DuSLzoTcgIYQQqaAJSAghRCpoAhJCCJEKZRsDCtOwYyROwnjGoEGDaB82jZPFNixMFp7ZaP3W1s9r+w3TUr0SC6Ev126zft+wX+uL9kpRhMe2bTs7O2lbti2MT9hz9dLXw36ZjD3A42xe2xAvFZbJxrCYhHfc0Ca7jZU2sNtZOq6FpTgDcaniTK7Jk+JhcR+Wgu5dV1ZygUl22X7ttbP2spIKFtaWleiwba29TH6qoaGB2mTJJ7+jGJAQQoiyRhOQEEKIVNAEJIQQIhXKNgbU23VA1r8ZxjrYOplcbZnP3vprmUyJ1++Osin07dr4Fhsnb02EJfSJsxgcwK9djOS9N6ahTV6JC7YWg10rFrPK9ZlJ73tte2uTJ90UsyaNydV4scqYNU8M7zqzOAMrw+HFodi96MlNsTIuMePklXxh4xgTa7X9htdZ5RiEEELsEmgCEkIIkQqagIQQQqRC2caAwnVArHw0i4t4pQ4soW/U+uRZ+WWWr2+Pa7Exk5hSu9ZnHOL5rUNdPLvGw7a1/n+msWVh6x68NSshdoztODFJebZmxVt3Ep6rZ6+97mEcztNdC+239wQ7H+9es9eHxcrYWhlPz42VMvHiVOF97GnzsZgoW4tk8dZlsfVf9vqwdVmszIO9J+y+bK2YF5MLYXE0IL4EQ2/6SZKkV8fVG5AQQohU0AQkhBAiFcrWBbdly5acFVG918nQFWBdVF5qYNgPS1+1+1rXkrWRlRnwZD5Cmz1ZeGYDk2uPlWBnbhCvSmgIS9f1ShIw15nt03N/hbDyBp7LxLpFQrehVyYhPLbnugi3e2UG7LGYbAxz6Xr3SEwqsnVlxqTFh+frudTDtrHPXdjWqxjM7LD9Mje//RxTkiTGpctkrjz5MobSsIUQQvQZNAEJIYRIhaImoBtuuAEVFRWYMWNG5rvNmzdj2rRpGDZsGOrr63HqqaeitbW1WDuFEELsZBQcA3rqqafw3e9+F4ccckjW95deeil+/etfY+HChWhoaMD06dPxyU9+Eo899ljU8fv375/xTYY+Si/FNvxs/bxMshwA6uvrM39bPzUrb2B9s6wctldGmLX1YlrhuW/atClrm42ZhNut/9iOE0tZjSlBbH3pLI7jlRS3hDbbe8I7nxB7Phs2bMj8bX3yXvwlvGe8mBxLsbVt7bVlx7WENsfYZGGS/l7Mx8LOx7Zl5a8tLKbrydyw1Go2TvZes8cNz9VuY/Eia5Mnd8RKpliZrpCxY8fm3bYjKOgNaMOGDZgyZQpuv/12DB06NPN9e3s7vve97+Eb3/gGjjvuOEyYMAELFizAn/70Jzz++OM5j9XV1YWOjo6sf0IIIXZ+CpqApk2bhpNOOglNTU1Z3y9ZsgTvvPNO1vfjx4/H2LFjsXjx4pzHamlpQUNDQ+bfmDFjCjFJCCFEHyN6ArrnnnvwzDPPoKWlpce21atXo6qqCkOGDMn6vrGxEatXr855vFmzZqG9vT3zb+XKlbEmCSGE6INExYBWrlyJSy65BA8++KAby+gt1dXVOdcfvPPOOwWVY2BSPEy+xcLKUNt+rT+WrSnwYlistIMXBwn79aRTwuN6EvhsXYonZcOuHZOJ964zs9mLS4X3hSdZFPrlY0oo2H6982HHZfeXV1Y7pswAW2dmr5Xdl63VszCZK2+c2JhbG5kd3jix+8kSE1Nk41RMGRT7Wxcey/5Ws3WL3tqqUhP1BrRkyRKsWbMGhx12GCorK1FZWYlHHnkEN998MyorK9HY2IgtW7agra0tq11raytGjhxZSruFEEL0caLegI4//ni88MILWd+dddZZGD9+PL70pS9hzJgxGDBgABYtWoRTTz0VALB06VKsWLECkyZNKp3VQggh+jxRE9CgQYNw0EEHZX1XV1eHYcOGZb7/whe+gMsuuwy77bYbBg8ejIsuugiTJk3CBz/4wSjDQjXs8LU1Rg3bvnoyVeftfW6ns7OT7hu6Dax7jilEexUrmZvNU6lmKbbMreOpYTO5I8+tyVwOTPnbq2Bp03OZjI8d4/B8PfXo0H6WEpzL5nA7k/ix+zL5GQtLv/X69VSr2f3E7lObiuypPDO5KUtov00n9tzbIV6aP5PMYfex5/5lEj9e5dIwVMHcyvZYnmsvPL8YRXEgfygiSRI3ZADsAC24b37zm+jXrx9OPfVUdHV1obm5Gd/5zndK3Y0QQog+TtET0B/+8IeszzU1NZg3bx7mzZtX7KGFEELsxEgLTgghRCqUbTmGMA079FF6EhShn9T6w71KkyFeiYXws5deHNroybew2E1vfKr5juPFIBjMP+7F5FhVTeaj90ofWNgYMxl7e9yYfu35sPiFV4Uypkpr2I8n28PSpb2qmuy5s21ZTNHCYrHefRm2ZZIyAI8t2d8ClpYds3TCS1dn8Rh7n3rp+GxbeD7WJrvshVUt9shXiVjlGIQQQpQ1moCEEEKkgiYgIYQQqVC2MaBwHVDoo/TWh4S+3bq6uqxtdm2P9YWGn9k6DduPJxMTrh/x1njYY7G2bI2BJWatEov5WLw4SNjWrqNh9lq/9fr16/PuC2THBuxxWZzEW3cSjoUXn7D+cxbX2bhxY16b7H1qYaUnPDV5VhrBG+MQT34qxJM7YuXIWfzFG6cQez95sTO2zo/FPb1YU/jc2W3WBvv7xNbU2fOxv33suKHNw4YNy9vOQzEgIYQQfQZNQEIIIVKhbF1whaphh/I71k1gX8OZ0i6TtrD7WpiitVd1kqV4eu7H8LOXysvkjbxUd7Yv65fJJnn9eG4clsrL0llZ1UnPJk/1OSaFOyYtnvURk+pu8aRhWL9sX09hORzjmHvRS49madgx95N3ruy5Y+5sFj7w8K4zS8NmStphVehYwj7lghNCCFHWaAISQgiRCpqAhBBCpELZxoBqamoyvkoWA4qpNOmVKAg/2zTZgQMHZn0OU2Ft2QcrRx8e1/qimSwGwKtfMun9GL87k1XJBUtjZr5pJvdv+/Wuq20bI1MUpiIz2SQg2z/OSkDksoGViGB48caYqqAs/uXFNsK2XhyBlQrw4gHhPeRJ1zApIRYT8iSK7P0Unq8XrwuP5Uk7hce1fdrfAhbz9Z479rvBJJhYLM/DVpHtzT2vNyAhhBCpoAlICCFEKmgCEkIIkQplGwPasmVLTikeb41K6MP0pC6YL9fC/PAxpQK89QiW0Gbru2UlI+xxrb+ZyZ94ckHhGHvxiU2bNmX+9q4dK6lgj8vG3JNZYTEUG+dhMlBeHIFdDyZHE1M2xNpgJVhYnCemnLq1145xzD3B+u3t+hHPXoDHTLx1Vywuxdav2fH27kWGbRuWn/DGicWDWRl6G4eKQeuAhBBC9Bk0AQkhhEgFTUBCCCFSoWxjQJWVlRnfZeij9MrUhr5ouz7HwiTlrS+Xrc/x1j2E9nuy8MzX7kmw5+szl02hlH1MyWEAaG9vz9uWnY93rmy9V4yWl9c2xNMLDNd02evsldIIx8aLOYQ223VkDGu/bctKadtztWvf2Po1e9wNGzZk/vZiZZ5eWogdUxbDsoTbvXO1sNIH7Lp78eAwxmjPzV47azMrpRHGWm1be1wWn4ktyV0segMSQgiRCpqAhBBCpELZuuDCcgwxkiChZI59nfRe/cNXa7tvTJkEVnbAe0VnEjSey4HJaHhuEWaDJy8SElNWwCsZwfqMkWBiLkbbjrlevXNjacHFlEVg8lOxsjcx6dLMBiZD5O1r+2GuV3tPs9ImnkwXs4Gl6tt9mU0erPSEVy4mxDvXcGzYcg372XNNlhq9AQkhhEgFTUBCCCFSQROQEEKIVCjbGFB1dXXGN8l86eyz56tlMiXWFxrKYADZPtaYVFjrT2aSLAD36XtpwSHM787a5YLJ+DC8NHLms48pM8DSuwE/lpYPNoa5toew0sxeP/b6sFTZmBR0D5bGHBOX8uItofQQk9aybb1zLUb2hsFirV55D/Zb4MWHw3Hyrmt47vb+Yencb7/9Nj0uIzz3JEl6NaZ6AxJCCJEKmoCEEEKkgiYgIYQQqVC2MaB864A8/z5b4+GVqS503UZMOQavDC9bm8FK6dp9vXhLTIloFtvwpJGYLDyLI9jj2hIFTCLfK1fMSn/HxOC8Mg/sfGysIEaOhq1B82xkJQoY1l5WTt27zqxMtfeMhvdxTIkF1mcumLyOV1YkhJWtsLAyCQD/bbOwuC773Vi/fj09LiM8jsoxCCGEKGs0AQkhhEiFsnXBDRgwIPPKydSwLUy2x8KUkG2qIkvxtK/ZMWnNNmXbvlqHKrfWlcFUkz2XT4jnbrTur7BfT4YkdDEy9XEg+7Xdq2bLzseqoLMx9lwkzE1l3QzW5tB1492L4Th50kehu9FbasDS/L3UaibbY1PBY+SZ7Dgx2JIGzyXNlOQtbLunqs9Snq2LnUkW2eseM8Z2nEJJMrukhKVHx8gKWVQRVQghRJ9BE5AQQohU0AQkhBAiFco2BtTbNGwm6+GlYbOU4RjpEZYKbvHkXCyhjZ7ESbivlyrKbLQ+ejbmxcSaPNkS1ta7lgwm19TbdrlssLAqrWz8Y+Jq3jjEXLuYcgxsuzculpjlDyyGwtLgvfIqrDRCzO+IV5qFpavbmA8bR+95ZmNaTJyHoRiQEEKIPoMmICGEEKmgCUgIIUQqlG0MqLa2Nmc5Bs+XyyRNrI+VlQOw64BsrCDMu/dKKjD5kxgpmJh1Jx6sTLhXUpnFEVisI1ybkKtteO6efz9WWiWErQ9h52PXeHh9Wnn6fMe1eLEAFlvySowz2BqoYsrOe+MU9sPWPNl+vfuJ2etJIbE4iR3zmHV/MZJRrOy5/X2KKYti+2Wx4xhsLKw3NukNSAghRCpoAhJCCJEKZeuC27JlS+aVNHz1tK/krCqoJ0FhX2PDz57LJ+zHvs57qeIM5m7xXGXhvjFuQc89EePuYqrVnsswxlVpbWDuLguzn6UmezYUo4bNJHLs5zAN256rfT5YWnaMq8w7VyYPZO8v67JirhomR2NdcPZ8wrHxZG6szcxV5qW657PX9mOPY22yv0/s+fauD7Mp7OfNN9/M284jHGOlYQshhChrNAEJIYRIBU1AQgghUqFsY0CVlZUZH3roo/RiA21tbZm/6+rqsrZ51f7CGBErdQBk+zhLWSKC9WN9xJaYiqIxUvW1tbVZn0MfvvX1Wr92fX193n7svmHpCStj7xEe25P7j4k1sRRbL64Q3resCiXQc4yZTeG42T69ezG0mZUjAfgSALYcwhvD8J4AgA0bNuTtx55feF/YMgm23/C4MfcEkD2O9tqwcgbWfmsjk/jx0vzZ74i1P7TZHtfav27duszfr7/+OgpFUjxCCCH6DJqAhBBCpIImICGEEKlQtjGgfOUYvBgKK5Ud4x/3JNhjcvLZOgFvfQjzpTK/vPXRs9INniw8i0HYtqyUQ2x5jEJt8kofhGPM1o7Ytp5UiidLFGLHKTyWd/+wkiMWJs3j3XtM8of1E7tGJdxu+7GxQFZKw7ZlpbTtvcbKl8TITRVTosPC5I+svSzG5ckzhf2Ecdj3Ar0BCSGESAVNQEIIIVJBE5AQQohUKNsYUE1NTcZHymT67edQ/82uA/Kk6cM4g40fWZ9raJP1NcesE2DaaQBf28PWEbBy47atp/Vm24Y2e7pe4flam2xbFu9i/vxcx2Ztw/P1xik8V08Cn8XkPOn9fH3masvWW9hzZWPM9Nxsv17ZCnZPeOU9wjUr7Nmx/XqlTBi2LSvN4mnBsfuJ3SP2frLYezGMh3nxbWa/bRvei+HaqVhsOYbeXA+9AQkhhEgFTUBCCCFSoWxdcJs3b868GjJXgH3dD18h7euwfeW1r7jsldErzxDCUjqtDTHS7p4Lkbkn2Cu6l5LKqkV6aafMLchs9CpUMles51Jk9rPrY/u0+1rXUvjZkyEKz9dLL2bnatOWrTsvtNk+Oywd325jLuqYyp4AT8NmFY+9+5YtyfAq1Ib3oueCYzB3pPc8Mzkw29Y+32Fbexy2fGDVqlXUJoakeIQQQvQZoiegVatW4XOf+xyGDRuG2tpaHHzwwXj66acz25MkwZw5czBq1CjU1taiqakJy5YtK6nRQggh+j5RE9C6deswefJkDBgwAL/97W/x0ksv4etf/zqGDh2a2efGG2/EzTffjFtvvRVPPPEE6urq0Nzc7KpLCyGE2LWIigHNnTsXY8aMwYIFCzLfjRs3LvN3kiS46aab8J//+Z/4+Mc/DgD4n//5HzQ2NuIXv/gFTj/99F73VVFRkfElh/5E68u1/vLVq1dn/ra+W1ui2/rLQ78qK8ENcF+u9X+yOIiXihm2tb51Vj7ai6EwP3aMzAdLg7fbPSmkcNw8+XyWqszkWzybbOp+TBo8K3nhwSRaYpYAWPttanVYkoTdpxb7PDApIWu//c8ni3EVI2XDni07DuxcgexrZ687W3rgxV7DcfTiynYsWNyZlXIIyy0APa9d+FsRvky8F0S9Ad133304/PDD8elPfxojRozAoYceittvvz2zffny5Vi9ejWampoy3zU0NGDixIlYvHhxzmN2dXWho6Mj658QQoidn6gJ6PXXX8f8+fOx//7743e/+x0uuOACXHzxxbjzzjsB/P+3j8bGxqx2jY2NWW8mIS0tLWhoaMj8GzNmTCHnIYQQoo8RNQF1d3fjsMMOw/XXX49DDz0U5557Ls455xzceuutBRswa9YstLe3Z/6tXLmy4GMJIYToO0TFgEaNGoUDDjgg67v3v//9+OlPfwoAGDlyJACgtbUVo0aNyuzT2tqKD3zgAzmPWV1dnbP8cldXV85yDGyNjcWLzcSs7WFyIl4+f0w5BrZvjPy/Fy+KkXZnvmivpAKL+7A1UJ6kf8xaDCbx78WlQpjvHOjph2driNj18WIbrPy1tZHFpay9Xtwwn732c0ypA4CXWGD3vBcvCo/lxXws7NheGXcG+43xyibExMpYPJtdVy8mXWqi3oAmT56MpUuXZn33yiuvYK+99gLwbkLCyJEjsWjRosz2jo4OPPHEE5g0aVIJzBVCCLGzEPUGdOmll+Koo47C9ddfj9NOOw1PPvkkbrvtNtx2220A3p2VZ8yYgWuvvRb7778/xo0bh9mzZ2P06NE45ZRTdoT9Qggh+ihRE9ARRxyBn//855g1axa++tWvYty4cbjpppswZcqUzD5XXHEFOjs7ce6556KtrQ1HH300HnjgAVfNuIdhlZU5pXg8WDqofW1llTK9V3bmdmOuGFblMNdx2as2SyP3zj1MS2USMrlsCrd7itZhW3sPeC5SBrs+3nVmbgamLm3TiT3JmfB+8pYPhGNhU4bZvcYUuAE+Tp4LMZS18lLMmVKztdE+z6EatufOZksymGq4J4Fl24Z2eLI3oR12nJiL1/tdY88DsxfIvue9dPtw32JccOE9nSSJey2BArTgTj75ZJx88sl5t1dUVOCrX/0qvvrVr8YeWgghxC6EtOCEEEKkgiYgIYQQqVC25Ri2bNmSMw3bi5mEfvr29na3j3xtrZ+U7Wv9+cy3yyov5mrLqmqydEpvnGL8y0x63zsuK6VhYdU6Y0pGeG3DGIvnSw9tstfZq5DKUt3ZWNgYUMy5ejEhti+LI3htmUSOtyQgJk4Vjrkne8NiQKz6q21r73+vGmlIjLyRd3+F273lA2G/rCIwkD0Wa9eupcdlhNdK5RiEEEKUNZqAhBBCpIImICGEEKlQtjGgfOUYPD916MP08uytdD0rY8tiA+F6CbvNfvb81kxex4sXhVj/MVvHYX309nxi5I9Y/IWtkwGyx9hbT8Ekf7wy52xtlW3LZPk3bdqU9ZnFXzzpILYui8WAvPU5bHtMiXTPpvBetM+Kty6ora0N+WBrVmLKe1i8OA5bw9LZ2Zl3X6+keDhO3ppAC7sX2bNlKwzEyFjFoJLcQggh+gyagIQQQqRC2brgtm7dmtMF58mfsPRJz60To1Qb9hOjjuspWnv9smOF+1r3loWpL1uYO8JzObBrxxSuPTkd5oLz0oDD+yBGMT3WfRraaM+VtfVcM8yt6V0PltZvCd2CnnuIKaTHKDfH3IveOMWo0FvCtjHuOe+eiJGbsoT9eufK3GFs3GJkz0qB3oCEEEKkgiYgIYQQqaAJSAghRCqUbQyof//+OaV4PFg6qJdKGvrpPV9oaJMXW4qp9FmMnEtMtchwX68ypoWNE4tBWPmTmOtqsWnm4fl6JS3C2KBXKiD87Mn/2+0DBw7M/O2dK4uDsHO12H7scgIWa7LlPWJkZEL7PXvZUoqYGIQnpxPixcrsubJnlqXFe3FCJmPlxS7D82PLNXK1zXcc27aYGJCNTfamHIPegIQQQqSCJiAhhBCpoAlICCFEKpRtDChcB8TWYljfbuiHt5Iy3hoi5mO1MF96TBle63dnvl2vrDCLAdn4RG/tBbjP2PNFMz+wtTH0w9vj2jgb8+lbe2Ouh42ZxMgOxcTv7LiwOAK7v+xxYmKX7J6w/XolFFgJa69MeHhsL6bIJKRsv2Gs0otzMps9Wa6Y+yu0yRsn+7yz68HGycLGaf369XnbebDfxHzoDUgIIUQqaAISQgiRCpqAhBBCpELZxoDy4cUrQunx2trarG3WLzl48OCszywGxHzT1lfLYhCe1pUl9F3bdQ8bN27Ma5O13643YtLuMZLyXrnl0GbrS7c+7/B8vOPGlLRmcvTWJhYn9NaV2X7D/e02VubcK/XNJP1taQMWk2MxOHtsr3xBOI69Wf8REl73mPIkXjkMe23Ztpg1aay8hKdPx547O/6sRHdMW2+cwuPa38wdjd6AhBBCpIImICGEEKlQti647u7uXpVjsLAyCZ5bjaUOsqqgXsXH0OYYaXT7maX5evZamMvBG2OWXsxKLMRI0Xv7xpS8iKk8yfotRkrIjhO7n2Kqj3pSQtbmmDID3n3A2oZ4qfkxLjv2fMdcVy9dnbmzY6677ZeVkyimZAcbY8/emBIdDFVEFUII0WfQBCSEECIVNAEJIYRIhbKNAVVUVGR8ujG+aOaH9FJ5Q1kcT6YkPFZMyrbt0/p9me/US48O7fBiQKG0DZMLAXqOv5UP6q2Nth8mj2KPY6V4LMzPzeSaPF91XV1dTvtytWX+c0/ih7VlZRG8eJftNxxXLzbG+mVxG09Oh8WaYuKynk3snvGka8JnwLOh0PL2XsyNPcNePDi0w5MOiomxM8J4o8oxCCGEKGs0AQkhhEgFTUBCCCFSoWxjQO/FOiBbriH0mzKpFIDLYth+Q1+09at7ZQbC82V+amuHV6443B5bjiFfn0BPH3goAxJTQtluC8tbA9wvb+Metm2Id+1YW4sXI2L7hvcFWztitzP5nFyfWSl2VgaClYe2n+02r8wAaxtTZsDCnmdmv8XbN7w+9plkbb1rxWKkXgyIyYqxMuEx42tROQYhhBB9Bk1AQgghUqFsXXBhGnaMxAN7xe3s7Mz6zKRImIsE4Oms7FWauagA/ipt21o1bJaG7VXkZFi3Qtiv5y4KbbYuT2aD7dOqPMe4H22/4XbrcrDjH14ftg3oeT72fgth6caeK4+lq8csH7BtmXq0Z1PYL3NRAT3HMezXe5by9eltj3l2rI22LVMrtzYxJXmWDg3wZzbGreZdu/D3yVvuUGr0BiSEECIVNAEJIYRIBU1AQgghUqFsY0AxEhy9hVW7BHgaoSfrwwj9wJ4NFhYbYHL0XpomiwXEVERlqbvW5hj5fC892sKkR9gYe2n9LMXWaxsj0cKknWLGyYsjsH0tYb8xzw47Tq62Mc86e0ZjxskbN3a9ipGyKeZeLKYtI7ZKcz6Uhi2EEKLPoAlICCFEKmgCEkIIkQplGwPKtw4o1375Plvfpt2X+fRtHMHuG8JiJPa4MXEDCztX29brh52PZ1O41sFb48FKG8fEK2xbu2YlPB97HNs2XMfhxRHCa8nWyeTqJzx3u3bEwtZiFFMmmUn1eH56tn6NreHy5JnYs+VJ8bA4J4udxayxsW29Mgls/Z21P9zuxfpYv94Yh5+9Ut8sRu3F78Lt4X2bJEmv7lu9AQkhhEgFTUBCCCFSoWxdcEmS9CqVj+3jpVYy2QzPXRS6J+wrLpOj8Vx7rK33SsvSQb2qiGxfpiTsuTbCfWPcHp57xRKThs3ck57ieAg7V4C7RJkMVExasnWxWRkilvJsXYq2bWiTp4bN0sgtTKqH3f+2rZcKzlx7nouaueNZv/aeiHGBes8ss5m1LeW5Mgq5h/UGJIQQIhU0AQkhhEgFTUBCCCFSoWxjQMD/900W6of0/NY1NTV5t7PUXXtsuy2mHIPn5w3tsPEI619maZrWvx/a7Pmebb9MPp+VgYgpW2H7jEmXtudqyyaw2ACraOldu+rq6rzbrb2svAe714Ds8bf2em1ZiQLW1o6hJWwbU+rDtvVif4XGab14Hbu23jKLsF+vHAOTq7H3BHsGvGeWxUQt7NmJkVGKSfHP2NmrvYQQQogSowlICCFEKmgCEkIIkQplHQMqpCQDk9+IkZRnJW7tds/Hyta3WGKkOxhM1gaIkzeKWZPDfMQx0iPetbf+8ZiSF+zY1r9fW1ubt08PFgNiMQl7P9l9mR3e2jdWSj5GooWtY7KwOEiuzyFev2zfYkpRsBIF7H4q5t6LKakQUz4i5lx3VKmMvH33+uhCCCFECdEEJIQQIhU0AQkhhEiFso4BFbIOKFf7fFgfcUyZgRDPb8pKRFjY2gavVHaMfzw8Py/eFRNHiCmPwa6r3ddbF8Sw15Kty7LnHkrMe+tOWD92fQhbx+St7YkpoWxtilmjY20OsbGycNy8NTZsHL34F9vGYoxemYGYfi0x92Ix5RgYMTpx3n3M2rJ9wzVEKscghBCirNEEJIQQIhXK2gVXqOutt+2t9EWMnDhzZTC3lCc3H5MizF6PPRdDjCw8e5X23Hdhv3YbSyWNSTm3xLiAmPSOxRsX67JiLl3mUvTcj+F2r3wES622bWPcTkyOxnt2mAsoZvkDq2hsP3vlSFi/McssPHcjcwvGuNQ9tz9LiWbXJ7YcQ74xVhq2EEKIsiZqAtq2bRtmz56NcePGoba2Fvvuuy+uueaaHv/7mTNnDkaNGoXa2lo0NTVh2bJlJTdcCCFE3yZqApo7dy7mz5+Pb3/72/jrX/+KuXPn4sYbb8Qtt9yS2efGG2/EzTffjFtvvRVPPPEE6urq0Nzc3KP6ohBCiF2biiQi0HLyySejsbER3/ve9zLfnXrqqaitrcUPf/hDJEmC0aNH4z/+4z9w+eWXAwDa29vR2NiIO+64A6effrrbR0dHBxoaGt41roA07DAV0PpUrdR4KLMCZMeEPF90+JmV3bWfY1JobT9e2mnYj/WPs1IOnqyHVwaCtQ3H3JOyCc/PXitb6oClndp9GfY41sa6urrM3/bcNm7cmPXZbu/s7Mz8zcpF289ebCOmRAcr5RAT62MxHwBYv3595m8vpdmO04YNGzJ/s5hVrmMxm1g7e+1YHNSOsS1NEbNUgpUU92yOiTvHlGMI97XP3bp162jbkPC5S5IEW7ZsQXt7OwYPHpy/714fHcBRRx2FRYsW4ZVXXgEAPP/883j00UfxkY98BACwfPlyrF69Gk1NTZk2DQ0NmDhxIhYvXpzzmF1dXejo6Mj6J4QQYucnKgtu5syZ6OjowPjx49G/f39s27YN1113HaZMmQIAWL16NQCgsbExq11jY2Nmm6WlpQVXX311IbYLIYTow0S9Ad1777246667cPfdd+OZZ57BnXfeia997Wu48847CzZg1qxZaG9vz/xbuXJlwccSQgjRd4h6A/riF7+ImTNnZmI5Bx98MP7+97+jpaUFU6dOxciRIwEAra2tGDVqVKZda2srPvCBD+Q8ZnV1dV6ffbHrgCze2hi2r/XHxpQOKEZSg/XD1gl4saaYdUDe9ph+Q1hczSNGHojFnmLkjbx92TgxiR9LTFnkGCkke2xPKiU8Py8eEY6Tt74lRvqFxY9iYhuxvyUxclMh3j0c88yyGK8Xt40535hyMYwdXpJ748aNOYOC240eN24cRo4ciUWLFmW2d3R04IknnsCkSZNiuhJCCLGTE/UG9NGPfhTXXXcdxo4diwMPPBDPPvssvvGNb+Dss88G8O5MOmPGDFx77bXYf//9MW7cOMyePRujR4/GKaecsiPsF0II0UeJmoBuueUWzJ49GxdeeCHWrFmD0aNH47zzzsOcOXMy+1xxxRXo7OzEueeei7a2Nhx99NF44IEHUFNTE21cIWnYMe4u+zYXI5nD3CtMSdtzkcSkcLNX9kIrGeaCuXG8dHWWXsz68Vx51n1k3VYhAwcOzPrMJENYajJLPwd6jkXoWvbkgcJ+7bNi2zIlcwtTtPZclWG/nlstbNsbFeSQ8NrFPHeeGnlok2d/zDNrx4lVvmUp9DEq+nZ7jNs/pqqsp9DNbAzdykmSuM8LELkO6L2g2HVA4SDYwbN+dxt7YrLwtm14bPuQl3ICYn54pmdl7Y0pPeG1ZT7jYiYgtubJTjB2nMLttq1dhxAzAbE+LezBtj/K7H6qr6+nbUP77bWz+7IfAa/kSIwGYLh8wrbzSmmwe5Hde0zL0fZrz817Zllbdj2YvUD22jAvjhMzAcU8o96atJBwjVaufsN+wjVzSZKgs7OztOuAhBBCiFKhCUgIIUQq7HTlGGLasPIGXtXAsG0x0u5eiYLe2uu1ZWmznhvKKyUQUoykfEhsmYEQb5zY9bBuHVYiwp6rtTl08ca4Xr1U5NAOr+Ipuxe9OFq4r7XJytEUU6WVYc+dSdnElEHxKLSUg3f/xzx3bLv3O8fcdTG/EzGoHIMQQog+gyYgIYQQqaAJSAghRCqUdQyokDTsmLLUMVLptp4RiyOw9QleKrUltClGnt3GMrw0ZtbWS6NlNsVIv4RtY0pl2+32XG1sJrzuNpbBZPtLKblkbQrTslkcyra1fXqpyex+8mKZIXaMw/Ri9lx5272UZyaRw+RorL1enDDcbo/LUtu9MucsNuMtaWD2WmJKirNz9Sh2FY/egIQQQqSCJiAhhBCpUNYuuB2ths36i1n17qU5xqg8M2LGI0apOTYdNKbyZIgn8xF+9lbTF6OkzdqxdPuYleoAT8+NUVxg6d+e+zdmXGJUxL0KqSExKs+WGBkZSzHPHXP1xchlxdgbc494bWNgvwUxvzlKwxZCCNFn0AQkhBAiFTQBCSGESIWyjgGVuhyDJ3ESI/MREuP3jYlDefuzOI93rsWMaYwkCPOls9iGp2Qe41uPURVmx/XkgSysbAVL1bf72rRfJsXjpUCHNnsxubCtPVcWr7B9erGYcLt3Xdm+LN3Ye56LkaCJiWGF4+jF61iafEw81bO3VLF2e269KcegNyAhhBCpoAlICCFEKmgCEkIIkQplHQPa0euA2BqKmHVAXg5+oTLq9tgx0iNeyeF89nnH9YiRN2J+7JgYj+3X84+za2fjPDHlpdm+no8+7NeTyAmvpZWI8q5deCwmB2Tx5Ghi4iDFxDljYjWspEKMrFKhfeZqG7P+Lma9lKXQ0t/F/OayZz0fegMSQgiRCpqAhBBCpIImICGEEKlQ1jGgQmD+/ZjS0p4/lsVmmE1eaQO2ZsJKyjP/sleOIcSLYTGbrf1srUyMLLxXjoFtD0thA0BXV1fW53Cc7DY2FkwnLpdN4XW395ON3YRtbYkIC4s12fOxNofbrb3sfrLb7BiH+3oxH3svWptD2JqimPIeXokOS2hzzDhZ7PMcPpfeWiQWC7RtvRIqzF52rjsavQEJIYRIBU1AQgghUmGnc8ExipF2Z9Ij3nGKKX3AXHLsWN5xQ+y52T5ZaqmXXhwjS8Ts99qyFOKY6xyTru5JzDDXK5O2iUn9jk3VjXHNhDbFVPK195Pn4o05Vsz9FNocez8xm9m+3vPAro+3TCHm3GNSx2PGiRGzjGU7egMSQgiRCpqAhBBCpIImICGEEKmw08WAiiljy6Ted5TceYxMSWxJaEZ4rl5cip2rjVcw/7iN07Bx8mxgKdBeCjcrs22ve2hzrGQ/SxmOSXdlMSFrr5eqz/z9zCYv1lRoDMsSM8betYuJ47D4iheXZTFRdqwYaS2vLXtevOOyfWOwvym9uQ/0BiSEECIVNAEJIYRIBU1AQgghUmGniwExismVt8Tk87M1Q6Us5ZCvXa5+WQzIwtYjxEjKM6kar0/Ptx6ej72uLH7hxdFCm2PXPIXxGBubYeXgvXU0YVsrY2P7YXGemLgUk/QB4tbyxMSIWOwvJl7hxbDYfexJ77AYSjGl4y2FlmOIOU4xv4nsdyEfegMSQgiRCpqAhBBCpMJO54Jj7hX7ehmTpslcM8UoWltsW6ZUaxVw87XL1W94fiytNFfb0A3iyfbU1NRk/vZSj8Pjeinbtt9Q3TjsE+ipPB1iz9W2Dc/dU1e3NoefvYqiYb9WqZm5U6y9nqJyuN22tTBXn20bXlvvfrJK2jHVhcO2dpxYldkY96/d7qW2M2V8Jrnkua9tvzFSPCwtnilpe/JSpUZvQEIIIVJBE5AQQohU0AQkhBAiFXa6GBDDi+swqQsWm/H6Yam8Fk+eg/UTti0mvdvzA7NjW791uG+MTL9XIsJuZ+UYYuJqdl8W27CxDGtTbW1t3rYszuOloMf492PuRbZEwKvky/DaxsRFYiT/w7Ze6nfMOLG07BhpLYv33LFn1pPp6i0xaeMWlWMQQgjRZ9AEJIQQIhU0AQkhhEiFso4BbfdjxvibWXlcCysz7PlCWbliFlPx1kjElNlma2W8mAkrPREjBePFj8LPnix86He3+9p1J6z8smd/GA/wJHLCOI4dJy9+VFdXl7ctiyvY6xqzLsieq23L1q9ZwrZeuegYiR92j3tre9g97j1LzKZSlXLwYq/h/RZ7P4Xnw6S1rB12zVmMjFgMYUxU5RiEEEKUNZqAhBBCpEJZu+AKeR2MSSNk1Ty91+NQmoS58mxbT22WtfXcBEx52vbLjmvdUvYVPtzOjmu3ezaFx7V9hq6wXG1Dt5WVjWEVUm0qtXUBDRo0KK9NnuQPa2tt6uzszPxtx9/aFG4P2+U6ru03PF9rL1PoZvc/wFOe7RjbtiytnNlkYTJEtl1MJVwv1T1s67kF2XFiVNy936fwWJ5rMhyLYtKww+uqNGwhhBBljSYgIYQQqaAJSAghRCpUJMXk3e0AOjo60NDQUJJjFSpHAcRJaBRDjJxIzLF2lL19AU8OJaZtqfaNuZ9iqnXuyH4LpVzsTastO06MhFep+n2vfuJz9dne3o7BgwfnbaM3ICGEEKmgCUgIIUQqaAISQgiRCmW9DqhYYn3RhfpKd6SPNQ2bysEXHRvHYf3E+N1jbIwpHRDTNsZ+77gx7KjrU8pxiiGttoUep5Rx5zRC+4X0qTcgIYQQqaAJSAghRCpoAhJCCJEKO3UMyFLK9Qk7M6U81/cqhlXoGqhi4hMxJd5j2r5XMcW0Ygy70rNUDH1tnAqJ9+oNSAghRCpoAhJCCJEKZeuCq6ioyLzSsYqD9nMoo24l/K3U+G677Zb1+c0338z87VUUHTJkSObv1tZWum/4ub6+PmvbunXraNtQZj2U9/fa2uNYOYy33367V33GtmX9rl27lu4b9mv7tOfKro+Vcnrrrbfy7mvP1d4TYVvbp207dOjQrM+rV6/O2WeutuH5MnuB7HIM4X0I9LwX2TjZMWb3sT2OvRfDe8K69rz7KbwvWKVb26/37BTz3IX9snO1NrOquMC70jS52gE9x8nexzHjFPYb9um1tdcmvIdzER6rsbEx83d3d3eP+ykXegMSQgiRCpqAhBBCpELZueC2Z0/ky6LwMo1Y1UBWydBu9/oJ25bSplK13VHnWkzbnW2c7L6s8qTXNmacwn3LcZwsO6ptudxPvbU3rbZeRlqh52rJdV+6CiZJmeX6/eMf/8CYMWPSNkMIIUSRrFy5EnvuuWfe7WU3AXV3d+ONN95AkiQYO3YsVq5cSetJ7Op0dHRgzJgxGicHjVPv0Dj1Do0TJ0kSrF+/HqNHj+6R4BFSdi64fv36Yc8990RHRweAd7MydIF9NE69Q+PUOzROvUPjlJ/eFBZVEoIQQohU0AQkhBAiFcp2AqqursaVV16J6urqtE0pazROvUPj1Ds0Tr1D41Qayi4JQQghxK5B2b4BCSGE2LnRBCSEECIVNAEJIYRIBU1AQgghUkETkBBCiFQo2wlo3rx52HvvvVFTU4OJEyfiySefTNuk1GhpacERRxyBQYMGYcSIETjllFOwdOnSrH02b96MadOmYdiwYaivr8epp57aq3ocOzM33HADKioqMGPGjMx3Gqd3WbVqFT73uc9h2LBhqK2txcEHH4ynn346sz1JEsyZMwejRo1CbW0tmpqasGzZshQtfu/Ztm0bZs+ejXHjxqG2thb77rsvrrnmmh4irbv6OBVFUobcc889SVVVVfL9738/+ctf/pKcc845yZAhQ5LW1ta0TUuF5ubmZMGCBcmLL76YPPfcc8mJJ56YjB07NtmwYUNmn/PPPz8ZM2ZMsmjRouTpp59OPvjBDyZHHXVUilany5NPPpnsvffeySGHHJJccsklme81Tkny9ttvJ3vttVdy5plnJk888UTy+uuvJ7/73e+SV199NbPPDTfckDQ0NCS/+MUvkueffz752Mc+lowbNy7ZtGlTipa/t1x33XXJsGHDkvvvvz9Zvnx5snDhwqS+vj751re+ldlH41QcZTkBHXnkkcm0adMyn7dt25aMHj06aWlpSdGq8mHNmjUJgOSRRx5JkiRJ2trakgEDBiQLFy7M7PPXv/41AZAsXrw4LTNTY/369cn++++fPPjgg8kxxxyTmYA0Tu/ypS99KTn66KPzbu/u7k5GjhyZ/Nd//Vfmu7a2tqS6ujr50Y9+9F6YWBacdNJJydlnn5313Sc/+clkypQpSZJonEpB2bngtmzZgiVLlqCpqSnzXb9+/dDU1ITFixenaFn5sL3E7vby0UuWLME777yTNWbjx4/H2LFjd8kxmzZtGk466aSs8QA0Ttu57777cPjhh+PTn/40RowYgUMPPRS33357Zvvy5cuxevXqrHFqaGjAxIkTd6lxOuqoo7Bo0SK88sorAIDnn38ejz76KD7ykY8A0DiVgrJTw167di22bduWVV8ceLfe+Msvv5ySVeVDd3c3ZsyYgcmTJ+Oggw4C8G7d9qqqKgwZMiRr38bGRrem+87GPffcg2eeeQZPPfVUj20ap3d5/fXXMX/+fFx22WX48pe/jKeeegoXX3wxqqqqMHXq1MxY5HoGd6VxmjlzJjo6OjB+/Hj0798f27Ztw3XXXYcpU6YAgMapBJTdBCQ406ZNw4svvohHH300bVPKjpUrV+KSSy7Bgw8+iJqamrTNKVu6u7tx+OGH4/rrrwcAHHrooXjxxRdx6623YurUqSlbVz7ce++9uOuuu3D33XfjwAMPxHPPPYcZM2Zg9OjRGqcSUXYuuN133x39+/fvkZnU2tqKkSNHpmRVeTB9+nTcf//9ePjhh7OqDI4cORJbtmxBW1tb1v672pgtWbIEa9aswWGHHYbKykpUVlbikUcewc0334zKyko0NjZqnACMGjUKBxxwQNZ373//+7FixQoAyIzFrv4MfvGLX8TMmTNx+umn4+CDD8bnP/95XHrppWhpaQGgcSoFZTcBVVVVYcKECVi0aFHmu+7ubixatAiTJk1K0bL0SJIE06dPx89//nM89NBDGDduXNb2CRMmYMCAAVljtnTpUqxYsWKXGrPjjz8eL7zwAp577rnMv8MPPxxTpkzJ/K1xAiZPntwjjf+VV17BXnvtBQAYN24cRo4cmTVOHR0deOKJJ3apcdq4cWOPap79+/dHd3c3AI1TSUg7CyIX99xzT1JdXZ3ccccdyUsvvZSce+65yZAhQ5LVq1enbVoqXHDBBUlDQ0Pyhz/8IXnzzTcz/zZu3JjZ5/zzz0/Gjh2bPPTQQ8nTTz+dTJo0KZk0aVKKVpcHYRZckmickuTdFPXKysrkuuuuS5YtW5bcddddycCBA5Mf/vCHmX1uuOGGZMiQIckvf/nL5M9//nPy8Y9/fJdLL546dWqyxx57ZNKwf/aznyW77757csUVV2T20TgVR1lOQEmSJLfccksyduzYpKqqKjnyyCOTxx9/PG2TUgNAzn8LFizI7LNp06bkwgsvTIYOHZoMHDgw+cQnPpG8+eab6RldJtgJSOP0Lr/61a+Sgw46KKmurk7Gjx+f3HbbbVnbu7u7k9mzZyeNjY1JdXV1cvzxxydLly5Nydp06OjoSC655JJk7NixSU1NTbLPPvskX/nKV5Kurq7MPhqn4lA9ICGEEKlQdjEgIYQQuwaagIQQQqSCJiAhhBCpoAlICCFEKmgCEkIIkQqagIQQQqSCJiAhhBCpoAlICCFEKmgCEkIIkQqagIQQQqSCJiAhhBCp8H8B8PAQnZV9TcsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  generate 1 image from the VAE model\n",
    "z = np.random.normal(size=(1, 5))\n",
    "gen_img = vae.decoder.predict(z)\n",
    "# image label \n",
    "plt.title('Generated Image')\n",
    "plt.imshow(gen_img.reshape(100, 100), cmap='gray')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 121ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  generate 10 images from the VAE model\n",
    "z = np.random.normal(size=(10, 5))\n",
    "gen_imgs = vae.decoder.predict(z)\n",
    "#  plot the generated images\n",
    "fig, axs = plt.subplots(2, 5, figsize=(15, 5))\n",
    "cnt = 0\n",
    "for i in range(2):\n",
    "    for j in range(5):\n",
    "        axs[i, j].imshow(gen_imgs[cnt].reshape(100, 100), cmap='gray')\n",
    "        axs[i, j].axis('off')\n",
    "        cnt += 1\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using TSNE, visualize your dataset and explain the 5 clusters produce\n",
    "from sklearn.manifold import TSNE\n",
    "#  generate 1000 images from the VAE model\n",
    "z = np.random.normal(size=(1000, 5))\n",
    "gen_imgs = vae.decoder.predict(z)\n",
    "\n",
    "#  tsne for the generated images and create 5 clusters\n",
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "X_2d = tsne.fit_transform(gen_imgs.reshape(1000, 10000))\n",
    "\n",
    "# plot the clusters , colors = ['red', 'green', 'blue', 'yellow', 'black']\n",
    "plt.scatter(X_2d[:, 0], X_2d[:, 1], c='red')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using TSNE to plot the latent space\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "colors = list(df['label'])\n",
    "# Encoder all 5 classes to a color\n",
    "colors = [colors.index(x) for x in colors]\n",
    "\n",
    "z_mean, _, _ = vae.encoder.predict(X)\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_results = tsne.fit_transform(z_mean)\n",
    "\n",
    "\n",
    "# Plot the TSNE results\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.scatter(tsne_results[:, 0], tsne_results[:, 1], c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel(\"z[0]\")\n",
    "plt.ylabel(\"z[1]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK-2\n",
    "\n",
    "Generate a doodle that is a mixture of categories using interpolation. Generate multiple images of various combinations (at least 5) such as \n",
    "\n",
    "a. Various combinations of bus and truck\n",
    "\n",
    "b. Various combinations of bus and aircraft (Imagine how this may look)\n",
    "\n",
    "c. Various combinations of bus and Bicycle (Imagine how this may look)\n",
    "\n",
    "\n",
    "\n",
    "How to generate a doodle that is a mixture of categories using interpolation?\n",
    "\n",
    "The strategy is as follows:\n",
    "\n",
    "We will use encoding of the training data. We will get the centroid of each category. For example, we will get the centroid of bus and truck. We will get the centroid of bus and aircraft. We will get the centroid of bus and bicycle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Encodings for all rows in our Data\n",
    "z_mean, _, _ = vae.encoder.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['z1'] = 0\n",
    "\n",
    "for i in range(0, len(z_mean)):\n",
    "    df['z1'][i] = np.array(z_mean[i], dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Getting Centroids for each class\n",
    "\n",
    "centroids = df.groupby('label').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = centroids['z1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# centroid of bus and truck\n",
    "bus_truck = (centroids[0] + centroids[1])/2\n",
    "\n",
    "# centroid of bus and airplane\n",
    "bus_airplane = (centroids[0] + centroids[2])/2\n",
    "\n",
    "# centroid of bus and bicycle \n",
    "bus_cycle = (centroids[0] + centroids[3])/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw Image for the centroids\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# a\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.imshow(vae.decoder.predict(np.array([bus_truck])).reshape(128, 128), cmap='gray')\n",
    "\n",
    "# b\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.imshow(vae.decoder.predict(np.array([bus_airplane])).reshape(128, 128), cmap='gray')\n",
    "\n",
    "# c\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.imshow(vae.decoder.predict(np.array([bus_cycle])).reshape(128, 128), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_user = input(\"Enter the category: \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = category_user.split(',')\n",
    "categories = [x.strip() for x in categories]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find the Encoding for this combination\n",
    "z = np.mean([centroids[x] for x in categories], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw Image for this Combination\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.imshow(vae.decoder.predict(np.array([z])).reshape(128, 128), cmap='gray')"
   ]
  }
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