DCGAN_Exercises.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Convolutional GANs\n",
    "\n",
    "In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a Deep Convolutional GAN, or DCGAN for short. The DCGAN architecture was first explored last year and has seen impressive results in generating new images, you can read the [original paper here](https://arxiv.org/pdf/1511.06434.pdf).\n",
    "\n",
    "You'll be training DCGAN on the [Street View House Numbers](http://ufldl.stanford.edu/housenumbers/) (SVHN) dataset. These are color images of house numbers collected from Google street view. SVHN images are in color and much more variable than MNIST. \n",
    "\n",
    "![SVHN Examples](assets/SVHN_examples.png)\n",
    "\n",
    "So, we'll need a deeper and more powerful network. This is accomplished through using convolutional layers in the discriminator and generator. It's also necessary to use batch normalization to get the convolutional networks to train. The only real changes compared to what [you saw previously](https://github.com/udacity/deep-learning/tree/master/gan_mnist) are in the generator and discriminator, otherwise the rest of the implementation is the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pickle as pkl\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "子目录或文件 data 已经存在。\n"
     ]
    }
   ],
   "source": [
    "!mkdir data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the data\n",
    "\n",
    "Here you can download the SVHN dataset. Run the cell above and it'll download to your machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from urllib.request import urlretrieve\n",
    "from os.path import isfile, isdir\n",
    "from tqdm import tqdm\n",
    "\n",
    "data_dir = 'data/'\n",
    "\n",
    "if not isdir(data_dir):\n",
    "    raise Exception(\"Data directory doesn't exist!\")\n",
    "\n",
    "class DLProgress(tqdm):\n",
    "    last_block = 0\n",
    "\n",
    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
    "        self.total = total_size\n",
    "        self.update((block_num - self.last_block) * block_size)\n",
    "        self.last_block = block_num\n",
    "\n",
    "if not isfile(data_dir + \"train_32x32.mat\"):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='SVHN Training Set') as pbar:\n",
    "        urlretrieve(\n",
    "            'http://ufldl.stanford.edu/housenumbers/train_32x32.mat',\n",
    "            data_dir + 'train_32x32.mat',\n",
    "            pbar.hook)\n",
    "\n",
    "if not isfile(data_dir + \"test_32x32.mat\"):\n",
    "    with DLProgress(unit='B', unit_scale=True, miniters=1, desc='SVHN Training Set') as pbar:\n",
    "        urlretrieve(\n",
    "            'http://ufldl.stanford.edu/housenumbers/test_32x32.mat',\n",
    "            data_dir + 'test_32x32.mat',\n",
    "            pbar.hook)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These SVHN files are `.mat` files typically used with Matlab. However, we can load them in with `scipy.io.loadmat` which we imported above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainset = loadmat(data_dir + 'train_32x32.mat')\n",
    "testset = loadmat(data_dir + 'test_32x32.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I'm showing a small sample of the images. Each of these is 32x32 with 3 color channels (RGB). These are the real images we'll pass to the discriminator and what the generator will eventually fake."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Zt9kiSUiUJkYOBCP/tt10uJJ72l16gOVaDyf89v/9GgDw6y+/hL3awJnyDnQfeVxUQH9w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Y5Ow458a8kqlJ+HNql1THJNKNVcezw6kE1VBEYQYD3eyJos/RmAlrfbHV870PP6/ILYHYn3OYEz\nm+jevmtIPYqXSXgtKWwafBibFdeQhPRJ0TpHGGpq44NglvEF7SFwejuTTpJRXHHfsy5YDoQBrQAG\nlNRMYMMf9uxNxPm8D6qQUalaiuBs8ZNu7LbtWE69DR4cRIGP2Wl+SMGBvFdnGS6sYN+8F1UOcAOk\nBoWxlYz7XcCsdl1y3bFmsU+7I6UyfE/Wa0aOVZWDfFN1aOcWAJCinRvhWep3VSd9s1kj1Q2rDmoj\n5wMjCkyiEHZhZjADIYC61R0+ePVdbJ01uWet/KHPV61Kd0rRqcBhrQB3lQkGAVIOG26dQo55oG5V\n6eTZoqK1pWp6Z0QMX8S6LOt/7e9eu8LkTBJZyjlVhRHK8Hxlg8iwlQ7eUsp6+4IlDwwYrt9wx0Mb\n2DkE7xBEtZAdps5Wn1ww+Q6OqFV2VUIppH9nNtkY772qKU7T3GgYi9efhSI1MdbieYykiFpUxtcf\nfkCtWRe0lBN8lofOq23YlnfkkjWaYLCaa9y2G146e/7561csfUdv+Cqnppzruqp0Tc47ymBXxgNN\nhgCM9mISbTKNSq4ycWy7NtUT0roL9ea7zoUK1H4+NVVTg92z/juRa5GnLkyHCqW9llqlchMt4jtK\n/g3H6n/e73dcOhUG0wliNFoxqE64AHJBW+6lFG3FO6K20MGgFEAr2G9bUZkbrhWldnMDZJWaKaVx\n4UTihuBB4f9n781hbUvWNKHvj2FNe5/pjpkvX9WrQhRCoKKFh4eJEEhlIGGCwGgDJAwaBywwwGgD\ncDBowMFFLSGQEBIGtNdWSxRNd1M8qt5QOd3hTHtYe62YMOKPP2LfzFcvz0MYKZ2QMu+55+5hrVhr\nRfzDNxTF18zty78nrCWq9nwCVL/vDNhwhhlo+/JNfStBCuNAQrPOcn2sTlSRhwHsGYMDDb0tNQjx\nEIxcZ6VMzURI4etv7s+wTYSmBtXMeRbBrE0HIErNKc8lw4ioMTT2Xv4DgKC1LNakSIKBFCOvpAVi\nH4WCQqmqxkJRpj3hk2n8LeO5hvU8nsfz+NGM3znCAiDLnYoQayqjCcZUoiqgJaVJIOnwORfFpimm\n3L1xa9FzqrUErap9UddZdH0FrxlTZVZtrzAEticKg0R8lIDH219inufKizIajndh0oTEkaJPHiFF\nkK+pzcocunU5yc8urNkeHbnbtupFIqwQvNTvhnHAyNZixhjWs4ry2ZDukTmr+RUS+KexcpsWESC1\ntlQ2tLKp+UZVw1VVjeBDjbA0g/3OdK/42EJCLOmdylkZNZFFdWZuopxEAkUpHdrD8YjLNZ9/miaA\nSvqSZM4TDBK0pCExpjP+WyktWGvRMS9RdcB81FKHJJUQE0dVCDIvwQPOVylvqzNJHoBYf+XzSDjM\nRTmjdMgqT1YuAzXRLnJpQWSqUaNQrUlKJDpWtYzvZD5UI6x8XZsoNs9KmXK5d2MCqqZcw+clwtff\n3J99fNOLPfu5psY12hfYiqpxF1GTQiOrdhSnHcQg15uSPl8WqK4HSbXzolCg95kUXWvTP3Q8WcCv\npoT1JiaV28FALmDaTqErC1aqC1bwlYBcNKiBHDF6F4Wm4JyXelarg26sQdcZuVE7awTzE0nXAmiq\nGDFFhJ//I2C/3wvuxEdfD15BUkDSBJhaPI4xSrs316aKoUJt60ZWGqiTH8VObBoHbIrRxJgVTytE\noxYpbGdEnzxEj4XhE17wOrWV3mZd8p0xK1iUzw4xysaQf19bzLWgSogUaz0qndOr6vlYaF3xWyEo\neRhD8KjlxGbB4u9+eNjhkj0eN5sE1ZcHrdKKFBRUUnUzQSYF54MIoJ4xaaPFoDi9tBkhbwr621DF\nfjXpk3cBKURYwQsmKEn9HBzfYy4GzHOe61IXa5Uy6rQogY7k+7OK+ymQACCI6j2bn8/mca6lvtxm\nkmurqxos453KgqWRBEpCqGYqSllZvOpiV58rgM7SzLowJJl7IKfbBfMVoymkA5BS4uNoTBZorPXk\nmp6pZhPLjSEjJ6lUkGZO0BGBg4GgI34Xas5zSvg8nsfz+NGMpwNHpX3aOP1SNaU0SWVZlqKjjVpc\ndt6LO8yivdTlSifLS6fNCVwhxgRra5G9lRbO3p1lJVcVqU2Qon8p2OdieN49j8dj/QxNULyLm8HA\n9Ba2K6lLrF2R4CtIL7Upa5bdSCUqswYYWRtIKYmwxnFAP1hwDV+iRQDw3gi4NqUohc5IGfkXS6Sl\nSAqUCrUVXzTHRVcfgO6K7dgiuyxZI+3xGDwDTDmSJULtzEL00jMsIkhDRBuF5Goo34J6a2E+H8/d\n3SMMAzrH7SW219kANKYFSrh7Oa0suk7x5FBag5EiPMMzPBwMp0+2UyBYdHzPKcuyPgBSCFh2nN6d\nFiB4DHz/XF9M2PC1ic5j/8j3oo9wvkaHGTLSdDQb6EadozbeBYAK5M2d7SZVLNZqBbDTBlwF3qEq\n7EOzhVh5QM7TrRptpRQkz/vtWVV9AbH4ntalAaHkGQMSQqznWEjpnTGwRsGUVLcBh+uG/IyUywil\nuB4VIekSvUX4Al+Jv1uE9TvDGlpJ9vywcieQDHw0Z1ZZ5eGOa8BScBiqEQrzkRU08+fFkKSTknFd\ndcHKPoW8OJo2LlVnebjotPNkzfN8lhqVlE5ZDcMaSeN2xJSmmupq1BZvqrQYTSQ5+qdUCKWVnHtn\nLSbWPhrGHl1n5RizsmlOHWPyEnoDVZUgo9VTvRs5DcmvqnNUaBKFHlVSZiDPvS6GHrHpUsoD9z1F\nSZDQpoC8iFZidTV/yJ+Z5M+SIBV4RQwRM+vbf7x7xHSZF4vNRssGl6JDWr0QxrUiNgypJidAJvoW\n/SatFfrtIN1npTSr+gHJpaqIGgIoOkl3DMWmLlS7fJosFLMgKHi03buEULFMsc4RQUElNEKHUVLi\nLFdS3tSokipiknr5RarpHaWmWxbzuRa2QvP/iCgEbaWN3Bv0PclSTi3bn+vmrnTFSsZohLajla7p\nuWrVeM1ZIGKMaZ6xukADgE6EWFQYY5JudooRvmyYMT6pdlXGc0r4PJ7H8/jRjCcW3duwshanNRqE\nsAL6aNCLAmHFVK1+kdUbSJJiJQbnSXHdKNHdSYgSrva9QdcbsWTqOi27EFQSTuBZhMU74NLIZqzr\nKiA6bTV6N/CxJ1irRLZEQws/TKFia5KpkjRAzEqTgvsK4i7cd71EC/3QM9E6H8/kJlysmb2bN50k\nx144lUk6UL9hqLpjGmvRDzUKKmltP4ZG8TFUFyCR8KmdqXp5SbBp+d9q0T2lqr0PJDnG2ACxRL0z\nBhxZW+nju3fohnwcn//kBbabkV+TuZeeu7Zd10vTYlkdHDdi1nWViHwcNxinHinm1C/FJLLNp5PD\nwtpOfnUgJInErFGCHaSUGrlfC62ZZ8pdaeFYJi2RQJYz4/ni0216hjKXeVZJ3iNRFChzVdvOiWrj\np7ajhcpJRQWvZuWyEnk1AEjh5Z23ZiovVZ2R+tuf89HysxdDPXdVCc7GaJjO1ojLZLxl+exPVUOj\ngOkgz1CKSpphv2uE9TukhPVmlWmhxhqeNGzQ6GxZMEgWLArdGXBRQtTALrpNi7ezBXkcoLmDNk0D\nprFH3xeZW2qypSAPcAvOKyFk13UyQb7xLcziY/lnrRSsNuiL/bfWMsGUkojJOaWFmmN1tnKvrWAI\n2NE2DtZaq1yn4hu0Hy2m7cTvikjFEdfqphbEN19T5xC0d4LMK6yFJZKFwnRWoAythVNKUW7UajdW\n8npVreRT1QHLtYbQIJzbBat5kFItC8hCmiJODLZ17gTSrJTQ186x7fPCWUQKoc5ZE+WaORcQGFnd\n9QkpqWp24SNWrqvNy0m6wXlqGg2n3qKzJdWrztHWGFhd1BVyG688qLFJyRMg8s8ZKFot2qkhuqNZ\nyFQu/ObrQrmXWVO/urgQL2YABFJSxlkalFA1u2qFQD4nHzed/Q44X6BKCUG1C07ZjGMFupKqz7XS\nWR65fI7WqlkM6zMu31FOMia0xHYRGNStOv4PH88p4fN4Hs/jRzP+P0VYaAqxZeVMyCuurN6oBUFr\njdganXUZQwSgRY7VKC1dvpiCWDh1fQfbdRL1aKq7UaQ2ZaWzwwRyhFVCbJcCoi+UnqawaDSsMehK\nhGUUvLAXqjOOpowdyhNY7FX4D6pRi9JN96Qp5AOA7rSktsF34rCrVO3+pUj8eeWzGz0ihQogTAlk\ns2orAJZm5oZGc50UCIYKEFOffR4SIYWaHv6QCIsoNTtr3c1LF8tqiCTMaT6gSH5N2w00R9CXlxfo\nRyt/B5GY4/roa5qvSSId7xxOp0UAot57LJwSLssi0aXWCp0xmDj9HAYLy9c2IKLj+6EzSrqUSZSj\nK7WmSqzgrOGQr1c9fWlMtNNBQBswUaNdQ6hu5bGJxPjiitYYgc64zUKDC01K/31RW/PFn2pZEVGl\nO2lUQ1xKEuFSA7bKTR0t89Lw5puE8ntGe/IKUNXh93eKsOhJKFNjUtGUOot7m/F9n1cnqllHGmt1\ngG8QeXiaalmS/7Wf+D3f+/3fCQDb7YjXb97Ip2QbMEZGxyidxK7rESNJx+p0chV9n1LtxBAqj1IB\nhHOb+PrdqnlPuZHqotAy8drjFe32lIDwgLdv38q/VcRzfY/i3KDVLpIjaMUIQ2xqiCi5c/mLdH2r\nekauRVBbF0npDJhZakzencM+YIBhc3Wup9QqOzQdq1YM7jsaTO1igfr7sza9zG+ZwDpHMcYzkm8M\noXlfU2dqPmu5/4jrBpbzG4Xmzlam7/3H7+6dVBfDAtOR8y6vY/CreFiGtrtb0zdqtLZAhNla9P3U\nlADamnPzHApsAvK6lofcLkT1sudrIeyK5rkk1IBFFsbmWZbjBX2yPtQSxF9++auUpM36m8eTIqy+\n6/FP/hP/NJ9YY4CeqlqBL/IUkqpq9EOxra5Mf++92JinmGCsbeoGtV2eQl0MpF7QbGvCFEj1xmjF\n6IgIF5uA/+K/+ltYuf7w8W6P/+fPfwUA2M8O08UNAOCzL/4At/czfvnLWwDAL/7iIx7uWdExaKlz\n+PUIo3PkMI4B27GTGsxyWuGKVyL10GriYx4BshJ9xERi3hnhUQwMoAKWNRuOLssR7/7hf4P//D/7\nT/l1ASsvFvvF4fHxEUAWA+z7DhtWRpjGHsOw4eMbcWJ2wbsPd/jm3bv82c4DWkskRUbh+jLjpN68\neonLq7HMMFSsUV90EfMuF9I/fHOHr3/9bZ7Tb7/FvMvHTcHj73/5f+JP/rW/UeV01lV+VsrAFjHF\nrkffd2y4kRU9qoCfOxN8EzkUFn8T+ouukA5FNWpZ1xW73Q4PDw8AgMfHRxzYCzKGINGmJoIpUTGA\nv/u3/xb+7S/+EOUXrUGt3GVK5c2gRPhoBilQWfCsQSgqFUZBdwbTNl+bq8tLDKx5r42RKOc0n7C7\nf8DhPl/f+X4Hxff+MAywHDGaaYRijR0yGv/tr36Nf/Vf+TdxYpOK5XSSOdNaoeurgQRRwnHO18u7\nAMtkfmWs1KnaIGIJGRt5PBbDYC8bXGeUeCX2nYXWVuqYIUWM/G+d6hG5vqi0QREdDDHh3/33/vrf\nww8YzzWs5/E8nsePZjwpwlJaYdrm1d17L92YZXVYfSEJ5520REFdZ2FjMVxUYrNtu05qViUqa/lk\nBTaQFAkqOrJcctWmhkRySjVdRqXOQm0gnJk4Bl8FxghKoA8uBDzuDrh/zJ2tefEivattX9u45NHZ\n/FnbC+DqciPigOvR4f4hRyDrqYqtKRC07UCs9Z3lYnPkEynXy4Bs9mlTQa7rvMGVdDNFzLxjPhwO\n+PNf5ijx9sMHvHnzEj/5/E2e2/41NhwNhgjsGfn94f0dfv3rLwEA+2UGlMqRFrKl+s31Vb6ey4Kf\nqc8AAJeX2xzGn6UPpWtW4+zcjJQX5e/Yz2fRUgHhWpMqSj8SEBIcizK2tZaUIFCUeZ6xFGE+7zJ8\nprgLDY2Di9bSlFLqE2ZEk/vFRmMqAoifCvdJK76WG2JqUx1Okwu8pUmDSGsQR/iq72AZIGw2A+zY\nY3OR4SxXV1fiLUBEAs1QuwNWF0QeOvRezqkfR/Sb/P7uYgKxSzmsBn71axiroZySQ2xt4GORFk8B\nSTBQODUAACAASURBVEUcmfDtVo9p4LpjqrVCl2o98TCvOM4nSfuVztFUnjMjcA8XAnyCyD0nBXSR\nj9FI2RXO+ypd84Ri1tMWLKUwFtda50BLPkgfQpNLg/EbJVzXgo7Nof8gnxX6KlS3LKuw6s/0cUgJ\n/sa7gBS9UFBSqoVDo1sVB3u2YAV/yguEaApVsjJpLYtr8BFuraoRMUWx1s6Qh3xgr9+8xptXeR5e\nv8j252Ux3N3N+ParnFJ+++0DHh+5xe5XbIdLCYN3hyMedh8BALYnvHxzDQC4erGF7XJqlqLHV3+a\npE60xIQje+x9+HCPj7c51TmcFmyXVbS7Vu8x83vSEnB/n8P4D+/vcDwwdkklrm8VBVSPwz4vCPeP\nO7w45jS5Hwf0GfLPc14ZCUifmmIUfBCrNRxOAtfwzski4G1A4AU/dRHR+LNFqi3slnT2cNiLouzq\nViitENnzUAECg1HGSDqidW3L589Osil670Vhg2IStVapNDb9mzoarBVpgHSF7CiIZ6HqDEwpg2x6\nKIavmM0IuxnRbfOCYy4vQJzKppiEPB2NQSAlGPuoSNRBtLXoSvq12YK2ZcFiBVy3YHVL8zPj26ir\n6X9SoKQR2BE7Bsgz2hkjjaZ1ddjzova4P+DATtRALjtYtqeH0uJgHb3PGxNvwNSmotFW9dsQRF+/\n6L/9kPGcEj6P5/E8fjTjaeRnpTDw6p5lXwoJk6QbpilrVBUt82EYsNnUAnBBsBMpaZU752C1ljQt\n0bl6ZrH1Sn5FiEFCW4Ck2Dr29Xv6vhfJW1IKH95/OINenKcGgPelSBvgnMfCWuvHwyOmKZ/X9dWE\nN69yFPTTL17hzes8D5cXGl2vBCh33K3Y8g6qzbdAytHW4+MKvy7V2iyeYFiT6eJyxGef53Tss5/e\n4OIiR2/WKPxvfztJs2BdI+4/5nT1y199g/u7HDmNFxOGzSVMl9+XlBLg/cPDI778Khfab+8eRZL3\n8uoFtldXmE95N/7w4U7kfeY54pEL6For3Gwn2ILHaKDbMaJGrQ0/s7T31+NJUpJlrj9bY+Q+8P2A\nvuuFHaCIxCIqxGyLBWR37CM7IS/rkvmlfP8ondAPhRhdvQKys1D+L09MkObIuqzw3ClGTOiKo/mn\nnUOqTXtCA4KmrIQrXVijJBo3g4Xdchp4MQFcJFfbEXY7QvHf49jB87xGF7Dydy5IWBGbpky+pgCg\nrIXhQn232YA2bLfEkJbTOuO0sPfAMsNxhKW1Ecd0IgVN9e8+RjEWVkphPeT33z0e8PCQ77HjfILz\nEZahOBhGcaEahiGzPQCclph9DcRFKghcJoxJ3KZMo5P1FBL001JCoibtMtWLUBlYpjasNtNvahrY\nY8Ohe9/3gnkiIsRUtHYyorzY0ydI2QYx5poRkO9Pt0YU1TgiElzNOIyyUEzTlDsVyBfgw/tS14L8\nrvomxtqJWh1CDHBcW5rnHSa+Oa6vevzBH+a6zh/+wVvc3BR6UIBRSZDv/gWw3WQNqBQJy1w04d/D\nLQcUIJZWHpcX+TPevr3Az372EgDws3/sM1zf5Pdbm0X/Aj8kIQIH9tW7vzvgkWtTZhxB2gJFl5y0\ndK32+wPef8iL5v3DXnBwL2yPy6sbmC6f6+39Afvb3JXa7Y7YseHGOHZwg4FhM4m2NR1CkgUrwx/K\n4FLBUnXrl3kRH0avNULHXdFc8ACVjUxp2ciWdcGJO8nL8YiVu18rL1gnRqr3vYHnUkXqugZRUATo\najorWuNulY0JISE2HUM04nbctOd/UwK8IqXPpK1JZ+oKAHRDj44XJbudQBfcKd5O0JtBalqq74Vd\nEMjBu/xZ3ipEq5EKNs10MJx+9dMWHYtC6mEEiqY7L1jLcRGc4jhaIX533QDNvofLEnD/+BG7XS4p\nXF9u5HnVibDnTuru7hGJn8mNNdCjESGCodPohAVSKXDjuIX1Hnd3t/xdC5YDX/dNwNVl3piH6QIb\nrr89BVr1nBI+j+fxPH4042kpIVEjXWIFO9N3HsPIoTZ3nUTqxFr0vHsaW1UVgSQaS8QEaVOAchHi\n3BwjSYQVfLaqcg0q3PIxDOOEi4tcrJ6mqUEqV4CiIMZbsmZMiLyLuNUDEYJoj2FF2eSurka8fZt3\nh89/conLi5LCeKTooUuHMhlsxhwhzfsVH97nXezdu4847I8oe0Q/GGwv8y78xWc3+IPfyx2+P/jD\nt7i4rK6quZTNuzAMPBdK1zXhwCRfvT9iN894GQuWS0lj4rR4HNge63G3g+k5BQkRRhtYwxGST9ix\nPtTt+IAXry55sgixBXM2JgXF+ilfp6bLW0HitbsYUpWRjoSAokLpEbRHKmYMRiG4mkY6TllTiIL8\nNqSy81GJ7nwQpoB3vtGyCmeNAUqNWW8ISIW/GNJ3eXwiIiY1/CwD3UDYCaopuldysbU15e3GHlRS\nws0ImnpoLqugr82aECNCcZrqDGJvxRGH+h6Km1V6HKHk/RaxIPSZDWKthbHl/I04SxORNDAeHk+4\nv39EZOOOrr8WsnzwQXBng+3Qc1E/Z0cGzs08TwGxdLk9CWE6UgZml3kxxorWm9FGVE4zqJcZCY3B\nyG8bT16wCvFYUdPKdAHW5ovfrdnKp4TKxlQ9K6WogZtWIqRWhGQIRYguJZL+Z0oGxAuW9xHeR9EM\nV6Qw9PnijcMgALVh6OXYPj3+chy1q9mEozGBEsHyw7MZetxc5TTz+nrEdsM3pAZ0w5IPIciFUArg\nQ8LV1QYvX+RF7vJihFuc5OvbyeLtm7wo/OTzG7x+za3uCwttua3M3m0FQH46Odxxbelut8fumG8e\ns+kyrITTPW2VvMf7IO3yeZ6x7XI60RmNwdhM9gVgbNdosiesIifN5ybrexRrNR+cpFgxBnGYLuuV\nIiVwD2OMdAkVSH5PMSE1CPRIVFPH1ckDrZUS4rLWAGkS+hbFXAMCgLA62ZgiRYFSlFE1ymOFLoSY\ntccBRGRCvTgWpaYPegawJ0SdKon+DMkPWbVJaQHJqmFA6nsQd82SNYjl3rZUF5+uB7pO0mR0XtLD\naA0CzwMZjVhIy6peR67aALByHs4nnHjxP+x3WE8LLF/7vuvEgyF4L1AGOxi8fPkCAHB5eQmFiMdd\n1o1/PD7Ch/x5NmqxOIshIXontW4iEqrbph+kY0iUqiJI+P+phkVEUmgzppOimVEBGrxbxTUXz6XO\n0eiIU0LVhK5efLm2EJpNrdYGlLKSHwefED2kiEekMPRcgLR9VRkFwX9CT0gpnVUlKq1F1cUGChST\naIBfbie8fpEL7a9uLrGdOM+nlNUsAbj1COecLJxqUKK4ud0MeP0yL0ovXkw47vfiS7jdGrx9nT/7\ns7c3uGFkudVJbKp8cEgJmA/5u95/fMCXX38NAHj38RaOC/jamizIZovYWlVeUKjqq1YbdMKbzPSi\n8m9jP+DiIjctLjaTqDlYbaBNhal4FyTC8sFh5WNdgxfF2BKRGG0q7MSYaiFfiRB8P6RqjbYuUigO\n3sumZm0HUlzz4BMr8kZGG1nw1nWpBheKbcNCXUiLXYJK1MieRHxaHZFlKsUGg5b/JX+AQorVVKRo\nvMsrvpde1Jq9I0evRWnWaES+NsFoeKMRWKKJpgG6RFibEYrldzD0QFf4gCUjCSAqzJLalAoh1qZT\nyo2EUv811lSd/uTg2RzG6A6by7zB3by4ApKH40VqN9/LZkUYZcHTKEyUmtlUpdi+ouipimB+N7T4\nzeO5hvU8nsfz+NGMJ0ZYkFy3Mx0gkUlAYkPTdY0s+FbrCzUNjChARa2yHVj+XBKVh/xvFbWulZGa\nkjEeRhtpgWcNrdp1LGt1axpZHWm9pEklQgA4bVFl1dcAO0UDwHbs8fpFrke9fnmJq22JgoBjaf3e\nfsTxcMT1TY6W1AsNw92YzdTh5auc6r242eDDe8J6ynNxMRm84TrRm5dXuNxw9BCrCoFGAmLCiTlf\n7999g3fvMndv9/iI6TpHRNvNhHG06LoSNVaSqlHAyGn82Fn0Ba2fIqJfkdjFxBJh4l38YnuBa+a7\nXVxMsNo0ROHK1fMhCNth9TXaMiXVN0auqW305CkkgRc4t8p/ABt2llpmShIhDP0AU1IhpZAoSQqK\nRpfKr162YdKss1UiuwRJRTVVsjdQd25Cvs/LvelDC4g974NmEG3tlLXg6VQUJ9YFkaEZ1JnsdF06\n5YYkpYM1CJrrwEQ4IXdTAWCcBnRTvg+7q0tYLlOozYjAEVbkm3Z1K3qWxNamcjYJgGPXImtO7JTD\nnfxukJQuIYl+FSmgK65Fg4WiTjTEtCFQLLUpqoKd1sAHYC0puq/zktkQhQ1Qn7tzMcG/ejw5JbSi\nG97VOlOIUmhNaUEIsVpq+aYwmzzAObvW+SbOn5X1ogtVRykLhVLc1yiWUDEQYqiQh+xMy8oBjYmF\nKZQWVDxN9EG0vmOoH0LUpC2kOG3IF6zXhM2Qj+N6M+CKsTUg4NuHvIj84udf4v2H9/icaTHqjxTe\nsLrCOHW44PdsNjZ3oPnYNxuLa8ZbXW16DPyALCmIFRVIIQGY50L1OQqkYxgsNtweH7oOvTWCxEes\nC1FvDS44hRi7DrbUfVLi9KukRU6UOafeYuKUcLKGlVZrEVpgDTHAiZv2KulubEwhS/Oj6xrvRR/g\nSvrkc0HfN+T5qvBJYqultMmE2TwtGe4hda+E9pav+oIZMlFSwtR4HhJUYxoBQZKXckFhP6gYz6zu\nW4hEil58GWOESBaFVQNsLelnAMUsQ7FnYdGit6opVhNCIQwrIGiqRXhFiLzpxL5DLDWw3iKyOi6v\nHZhPC3iaQDTI/I/jAO9KI+wIrYyIQRpTnwFltGAoCUowkOvqYLSWBcfqDmC2QnZ65+vcd9Cu1l0j\n1SI+KBbpfVijm/Twhyd6zynh83gez+NHM54m4Eckq6+xBqnAEnRAa8jpQxI+nvO1iBeTB4HlLiyB\nWJzNIrv4Vp6hBVIpFirQWnb0BBciXCjEZRIYhXdeuIhIkPRBJGsaW6EYvaQQrZCWJnUmRqZQ0xur\nCb0p8ALgxG4w33z1Dr/89a8EIPnm7St8xhFW1wN24EhnIGw2GgOH5S9vNrjiCGvsDRQLw5EPVdOd\nU9sTt4+VAV5cMdxAKVBX0rsMXrUcyhuK0trfjh2u+D2Xt1v4mDuLp90e91rDcSqPEHDFnLeXN5e4\n4Ha2IQJSbYjEpqOXISbl5yhpXXV8ikilW6arqKPzXjpEIeUduKSwshuDi7elMdOwHxAiQgpyH4AA\nnUqMpVD2YUoRiBlSAXBwWlISUgJFSQ1XT1JfkaVWsquniIbH6pGSquUOCnB8P5OKUMQc0uQqYRgJ\nvdWwnGalzkh3L1Q+eLbGMgqppKWksPLPJ41ingxlSOAMLal49QwWDV4A3VprAX2Pw4CjMZJ9eF+l\nYqztQTwvwSXsmT/YmSOsUcIy6ewAYoPUtsieU+UkGUyKtVObQnWITqnKRj0hwHp6SljQvMYaROnW\nVQxOCBHeRaG7uDXAcS6WkpOThFbSLzFWYdxMGMcNf54Rh9h1TphRulIJiwtYlurArC0Tg52vtSlF\nsA0ivwwRE4hNryZVOoZSKv+c5ImTtnr0CUWySlugWB7PxyPu7u5xw7Uut64CeVBUUfq2C9hedVz7\nA96+vcEl47CMQcW0hLVic5JGiBHzXGgyhKvLCz5Hjdnl3ye3QiGJcIBSEYaR/phGXDED4GK7xTum\n9ny4vcd+XTNCHoC1E14x9ej3fvIZbq7zcfadRmB4BZDT/zMn6cLsj3X+y4PnGmvzeNYfqzXGyPrx\nhZgbUmw6uGjbiZLfRxBiIikNgKpiZlH0APKik9DCWZq6CSk0h/PdUXT122NAQiWBB3jUDnOiiKKO\nmiggcT0vrgsiU4BMiEi2y909MEar1OWoseRSOSUVnXUoJF1SP5KfyWpQUZkt2mDG1mviPTTV+7+k\nYCklOO/RcTcxn38xehmwZTzj7naPPWufLUeHqe9Q3AuINDpG35tOiy7dfFigGrf2FGPjwp7gmZhN\niIJ580/oEz6nhM/jeTyPH814MpewIN2NsQV5BZAX/RvvA9zqsRZ7JucFaAiKRXUCmq2pAMD2Pfph\nwMguyUQGxfgkhgDF3ZNExCj4/G8JOUUEsv63ANEoCSapII6VUhL25rCfO0cqCRJXKYMQq24QBQ3O\nZuHWgMC/HwcSnNI0Dhh7I1HM1cUFCnA3JI9EjEbvEq5fjthuM5D07U9f4uqGZUcssIbSpAjVYDYR\nYgjY73JUNHRbvHlZpGc+YuZoaTkeGRFeuqla9N3VoCv5fNrg/leMer/9gGEzoOfd/s3LN/j9n34O\nAHh5s0UBU+/2d+i0baLTgNLphUrVhWec4Diadaxh5WLIelfIBfogPzcRVYzyH5BTQinCUuPGkmrk\nm5AxUFEi4VgCXviQqjUbR2VKJIiMRJ6KdMVKxdAEWfU78l9btGhbdE9ZDVcUSAGsJWoMIt0SVo3A\n82EJWVqGtb+US1AF5KkAU3CNIBjUDqJqpIrztJTunEYRyi9Rl1EaWg6dJBoOvrp3F4utosprOlv1\nwEiLW7fzwMwSRtFHTF2PCy4bbC97dIyBtFZXh+4YEAKwCE7RIenqMCVNsFiv7VPcvp6s1lBucK26\nxleQELhDtKwO87JgYRqADysi3+DG1jZ1P/TiijxNE7qhF7txra20wL0LMF3pLBqQVtKCbRX8YiJZ\nNCMgwFNhoWstooBaaalVtFC+iCRpBJCpOm4pxOv6XW6tVl5v3rwA1B/hiy9y3arvDQIvPqQDyHDO\nPypsr3pcX+cLfvViBKvSwoUFMysRJIqC+NekAYKk4X3fI6XWUTsfz9B16LWBKnWcoFAerBCSLAak\nNIZtTil9b0CW4HiR38977PeZ/Hw6HTBNG/5sm298ceE6NzMo85zhLiUt48UnRERdU8Ka3tUHPyIg\nplDTxVYPHA0sJUXpyPmYFQGK/heQpNsGTVChKkukNqdrxAHPjjNGoT8RCv2mdLBSTfVSrbEpUabg\nrxWidc4aBfqRavcatkdaPDQzNboI8PQggKSzZn1uwJU0y0bAlD0iJAG/GmWkAJSk1mpKUzIfFZdt\nnKuLet4gkgQMXT9KFSQmgjVFkcXg8ZCJ0PPxiIO2Ur/sJyuQCdsZwVUklrUum24MvmrK6WotRkqh\nCG+l5pr8tvGcEj6P5/E8fjTjyUX3skKG4HHiUPc4zziwDMhpWeGKEQW4CCo7LyD0BSLRrDLWwljT\nqHtalLXUdqESSfsOprNIKncuYohnLrUl8lJGSxqgpOBXTUhb2dwENOlIymql/Lp5PolCp1slAcZ6\nciLb8ZMv3uInX7xGPxTz1QjPpFJEA2J+3zBZKD3h6potpyaNk+c5O8yYWTpl2raaYRYKwHYqRqAG\np7kQjxcUvvBmM2EcNkiMA1qOi8jkHnYOdw85dTy6BZcMcL3sXiBpwvGYC/fRLbhjU4uPd/fYTLUI\nG31AUIXTp3Pkh5xWiQOMUghislmKu0FoSi0dCo19VEJERJS/q4ZQnFCldl0M0kl0q8PqVlHWhAIi\nMXfNKJjCAyQC85/z58U2qqr4oJiiNAqkR0w1wqpRWS0lFKdras4fTae8NBsQa8dM+QgTEpgqii4p\naG7vhUhwHJWYEKFDgua/60j15xChheLUdONK46jRfdO6drxTE+HGRDnC0sWQ2MC7ov7rBR+53W6x\nsjxSDBFuXTCzJI93FS+XUDt9WitOp5tub9v0KD+DZF344bDR3wHpXm6g/f6Iu1uW3v34gHsGUi5u\nyScggmwV4BdSBGdY6HyjqZ0Szq2CalvZWIuOeXrDNMHuDiL055zDMBa3D4Ou8N+6XtDEJaHXpKCo\nINw/SZpLKYRidiLmY9/vcwcQAB4edlj44KfJ4OqKu3UUELEKOjjb3JfjWxF48Ro3Pa5fXOLygt+n\nIz58yBLJD3cPwkmz/WfoulLLy+nMppyj1ThxxyliQSpVRK7trcw5DMcE8OL18XaHbz7cAQAe51XI\np9ubK9hpwI61j24/vMcjqzp8vHsUbqNWk8A98lSRiBWqJCZmeQMqD2+Z+yZNyplebX1/egGEsdBs\nJoqUXKmsgZ/n9bQsWNdVfASUIoGxhIaTCmQVAeFZtAyMVNPIRFVtoIAbipS1UrUzSFQB0pIalg1R\n19Z+QH1ONEHAr702GLRGz3+3pSuNnG6qstD6APIexOerYv03FSPAKWVcfAXFUlk8qmWXomqflv8s\nGwEzFBr7Lc+QlOW0CDj0+uoKVhcoxIi7249y/s7n0g8AqA5IJfWnCNJV5CDFKPc2KAmUxGotUIwQ\n6+L228aTFqwYEw77vDA9POxwf5937ofHHY58s2dpCZKbllJCovJgobamU5IbcHUei3PQXJOwWlW1\nBdKNQUO2ypaaU6p4I630Wb2qHnPdbdsNvhqI4nyn0CSfv7BNFADc39/h/uGeP39Cx5SIFzcXIBMR\nImuoI4pnYcYJMWq+73OtjuVwgg9C+A3RC90oX1SuR5hcrShYGlJa7MAinEQYh8OM/W5GX3YsSij7\n1vvbe7y7+5DnGYSX2ywUeHExYdheIPJN/OHDHe548Xp//4DPjzkSu9iO+Xha6/GCRE9RaFPthqPp\nu0vSeW2q1pUy4lwJeTdbwTXvF1XKJAKP67piWRZB2WujKqSFqtW60gTykOsZGmmcBMgCS0Z/pzZS\n7r+UWqI8UDFe5XT4PBrpGUqQvgSprHoAAL21sFozc4DvwzIjLUk6RP5P4OLSSNAAdIFtrEFeIzXA\nkIT8nVVTarQqRsIx4OQWKZSHGKQxtq6rFN23FxtcbBnDt5lgCTie8j3iwyp1136jpHHl4wokkr9r\nrSVj0anSoUhVhHt0P3zBeq5hPY/n8Tx+NONJEZZzDu/e591693jE4y7XXebTLNESkDsBSiIGLdwm\nUIA2teXsGbtwmmfsjBH3jGGQTC7DDNJ5Kic7qKm8rCw1wjtUqq65Z7v1mfha+cgqEQIWeIvSwUpS\nW7q9vcW7bzPxWOkbXHMtapwMTEfwBejq1nocMQk51pgOuoEHxBBheSfbTFupGxijzyKEGCtrILmE\n/aHoda/YH9gIMxKutluEA3dd+w6aQX27/QG7Exucdp3MV9dZGG2lM7U/Lnh8zJ/9Yj9jWYtUjGFI\nQWUGSOeuafPrJrJJ6nsirMaerQV3JmQdtCp+ZxuybKoQlmYXLqwFuW5QZ3USkSbS+swVCYokUCRF\nUALabLp++RQFwgKdgbj5deksSm8dqGNMEpWFVO21IgidKhy+DtqYBhCKCq1IFREeY8wwhCIZ3qTe\nhrR0FpWr1nXlOrTk9BBzVJO/KzXgbgcfVuHhhhiEqRFjkC7jZtOj547h5WiBuODdO4bfBCcWbDEl\nUKqSQylS0w00IsbZZhw5InwCnqGc/1NeHGPEgR+S43ySlCZm4XUAWV3BaFOZ9ZoAJjITRSiebWOq\nAFtM2ZpKUkIbRDerVV7IyOqIwm7Nta58bPnPWg+oi1d5bZTPzKWh0kYHWkJr9krj4qc1knbc3d/i\ny68YN9YFWHPD5zvAJ8BzPcX7IDWLEJLQl1TqgGAEkwICNhcZk3VxUWESSic5thA93Orx1V++z38H\n4d3HXPe6f7+XGiLpIzamR7zhz7vcgo2fcZgXaf9bo+AZge38CuV9Q24NcMXswgMrPyzLukLbqsyR\nEl8wABSbulRLGBb4iJKNITYPRW57VzyTUiTE6K6rFm2Z4cEbnKpF98UZhBTAbCYYa0RFxFpbF39r\nEGL1HtC6mkYoU9+TFMGcaVlV66mEJL6YWlc9N/ADV9fxhvAb0biUVxdybSxAqjFYqdiFmFL9fTpf\n1EE1fTLqPKWUvZZf2vcdZnkuSQIHpa3AbVJw0FRpZ53thFbVjz0uWIO+HxIU8oZN64IXVwPCmmuw\nt7f3VUF2DXUj7A12uyMOjMMKa5Jyx7J4rGWDU62QgigO/tbxnBI+j+fxPH4040kRVsNp5B2Ed1Kr\nq26z6WFNJ7uXsRqGd1xlkkRYSiVJ+5TJKWSR+1BaS0iJCJStNCaPmFyWqeG/J+ng4BM9rbY7ch6+\nt2BR3ifzd1FC8PUzu95I0Hpyq6SHPgTMbKW03u0yOJYnxtgOW46cQiCRHCEYdHaLsegnJ2BNvHvB\nwxQSK/nabvcLTvMJ/+hP/yJ/niI8HHMT4PGwx54hF9po3E6P6IssddcjamYarDWa0bEW+pdlAWkn\nukURCoqYf0gKK5/P7nhCf2EayZYo4MEQQk1JUoL6VCK5Sb1DiKDURlgFJpLBuqVJY21VmM1fV6Li\nKlPUs06PC6WTbMRI1VpTIxqlc0RS3J0apydrDQoaNqn6IJTCQulgK0ThhlJK9efSs0k1UhSnp1A1\n8AHVpKR5hkpXLMQoxfSIs6w7R1xlDohqU8LU81FaC4eyRFp9P8DJgSikIpSlKnB6mkZcbCaBz3Bb\nM5+XASRVSxXKk+CRZZ7q+ZdKTUoKMRQAa37F3V3uTK9Hj3iVXziOI3qBLjUwI/3D46YnOz9PTJ9J\nkYSR3tskeBtjsgV90bbqOoOuL6mUyjblKKlPaXfmGRBDib6Xh2f1scnzA2LjeZavUrmsDbm2oWO0\n4mCt1+E58bXWXJyvJF5jDXrWkpqmDTZsET6OA5yrqeJ+v0PHTr83Ny+x2ZZFsnWtTugMoStkY62r\nLjyc1ACASiiOKWFdPf78578CHxDWInyXvLSylVbwLsKJcxZBi/1WRcQDEFnhRLneVnS+19khFUoS\nKfEvDIhwMYE4/Pc+iCpGaO3Gg6u0i4L0L/kV/7uI2gXXtLrZVbvIO3daCMotYhyKpLY4xiGnkTwX\nWcOJKVjanLl+t+j27ELOdmxWA5FVEzQJelwW2GZzk/KESlXLShFiDKIEgRTlzaRIsGowRhQrQgJ8\nSvAFEhEiqNwfRChlM58IAUr0saImgemYzojag+o0PNvZC8k8ENySf3bBy3PZ2Xp8Rmv0XS/Es3AT\nMAAAIABJREFU7WVe5Ty0Uih4A6WMpJ9eR0DF+rQ1z41zSSA2UUcEl2SrUlrVTcKYJp3VKBy2J0i6\nP6eEz+N5PI8fz3hShGWswetXWVlz6o84zWxA4GqqmGWLq4yH7SzGsVhxdRiGQp4mcUEuu3ApEFrT\nV96TO6Gii5l31lYaxUigFkBBNRWs8jIN2TWlplh5LnXiWjMFrXF1Vay9foqf/v4XAICbF1vs9rkQ\nfv+4w273gFc2u4v0/SCmoymSdPg+fnzE/a3D5TbP2YvrS3R92cms7DKRHKi1RdIa/ZgjO7d6AWL2\nxmBgBPz2ZoPLmwsMHAGafuBCeZZdKSjmbhollyGjYIdBOpWaNKjgb8YtJia5QqvMACgmBwRxVQkh\nSKqYZXnyz13j4lx+F0N1ho6hpncE7uyV66Ur0j0moSCzpHYx2TCZW6gaBLWp6UXbGc7Be3ldiw+q\nEUwqWDDUlLCAGpOqELREEBmXpHJzpODQ0ICdW+nuZFSVewkezgch7OtUTTFyn5KbUNCIUNXejZK4\nQIcYxGiWkjpvJAE4HVa4pXTsAhYm30efRBWUAPTGoiupMin4csIxQXFXL5d38jE5n6CWAOLMCdbK\n8a0nJxg50gF+8ZL4KKqg3q7r5DuVaZ7NJ4RNT1uwlMY1O7d2usNp4AVrTSKP60MW5iqXwRiVyZEA\nhr4TWV/bmYqGRYI6I6ZqQZUjJSFZA6WjVOhBDW0iVZ3w4FpqUENwbeoNVfP9vIaVEMUzL1JCxynh\n1c0NXr16DQCYth0Wn7ul/TgBRLi8fgUA2F6+QMeW8esa8LjLc/QXv/ga837BJQPxfvrFZ7i6YBTx\nqNEVq7kpoTdFZ6jHtJnwx3/tjwEAj3d7PDB9ZnGLOJq8fPsCly+3mJhYvbnYSHoXv0niTTdEwPaF\nGrRB343VM1IZmStNddEAEaKK1Scy1RpWiq3TEWQxbcnL5QUx+MbvMdbuLS9WrY9krQsFqWHFmKre\nVgwIqS6An/Cbz+SxqbmvGjBL3usaGEwLeGk13UlBhCYTRRGjpJCQYqzwDILUmRS0tK8jEXxZnFcH\ns6zofdHsV7CFHqMMTBHEI5U5AmWxjwHLkuudj7t7BAZi99Moemal9HE8HsUmDYTGGShIKgofoQgi\nlz30nUCMYlhrN9dF6Xj7CLiYEEp6rEnoTD54cYjWNiH5hCMDzH1IWC+5azmMshEoXcGmT5DDerpa\nwzSM/B1KELHrErGwnMyyODhX/epSgCwkKUahIhgikbRAQ+sA8kIkIna+FnY1KfTWYOR6kfdedMxT\nCFiZKnCaZynCFupCjFFazTnCKj83JqFIWZaFd8rjsmAtD6dSErUo02Hiwvrr+DkSAjYsL2PHCcTI\n9MXNuL1n7fdffIXH+z0u2MZ+nhdMrEa62Rhcv8iLze//7A3GqXIJp82Ev/bP/lMAgK++fI8v//Ir\nAMDt/S1GNui8ubnBy89f4pINL4ZpwMzCa6RIYA1u9QKziOD2Ot+ARlfzy7636Efmbw4mc/VCnbtW\nbK+du1oraxaG8mdMZzgjWbCQFxXRiS+hNf9csUJV0XRxK1bn5DiyL0KtZZ7DV+TjcuTUqD+USDr5\nIPVUYimXUuvLdbBSGI/N5hmawnrhQP6GVZOf7BTLPJRZUg0vs5qMUiAkD4SVa1EqYOaP2ynI8SQC\n+okjU64BBufleVNKgUTts+IZvVuwLjOW9cTzvEBztuNXh5mxflprGK4/H+cjjodZ6DghBpiusjPk\nyieVz1d8QZPMhVKQaxNjlGv7lAXruYb1PJ7H8/jRjKdpujdOJZQgO4KmqtGDFBCDgy8o2KBE0M4Y\nwHL7PobuHHWslITAwUecmJu4zCcEDlc1ZT7WxOpy3nsJ3YEAx6v/fDhKTcJ2az38BkxaI4OqfxVT\nyooQvFvNpxl3j5k/+PH+Fvf7nBJ2m2tMnNptLkcQVWR9TAqB4979Ycb797cAgK+//oDDYcE8lx2a\n4Jec3vUD8NPfy5/95s1LIBXbcIWus/j89z4DkDuYD/t8PPvTTuo2/djj5sUNrl5f8DlbGJ70absV\n2zFKBuCuUfSAg5d+eN9b9Px5lxcTBiZJ656gQpK0KKfNNaoSEC8qSFSy9BiazZOacKuJdHjeS7RD\nonGVX9MS3YvV+jwfsTp3ltL5UB2NRLDPmGzGqmrtpgraeUmdkq+eBFUnre0+c/SVAqh0tlFPpzlD\n+VO4iJEqil93sKqHZSC1jgrE0IwYE1BEK11CWgMCE92XtIKYN6qDFzPccRwqnoFHjEFSrexUxHWw\n4GuX1p/g3AluZWiOO0mEmZLHkXXcI5J0Jw+nGctywoH5gz569IrvEaug+J63SsE7J+apRBFWgLO6\n6eDWCSwg3R8ynrRgeR/weM8KhKmpW7laJ1lPM1Zm09f3MQp8XWRR6axpipSESo7INY6SYh6PCxxf\nOAVgsB0uGFrhvIMqhbuYROUwxSg3o24wOMQSMTEEeahabA6QFzDN8Ip+mvDNt7m4/g/+4f8lRXKy\nf4S3bzPSfTA9ErykgSpWY4cvv/mIn//FXwIAvn53h96MuNxyOhsV9izdcVoDLh7zTbI7OlxxW9rG\nfNJm4IJyT4gMC1mjzwsQAGUNhrFHXxYZQxi5IP/i1Qvc3mbsljJWRP6iSwjBCYr7Yjtgy/XFi8tR\nCrTBZ913kqL7uR6+9DmogU9QsS2vC1ZqUe9tKsk0m1I3ycj3GvgXUu6yrjixv9/pNGP1rqYamiTd\nSZTOyPLuDNZS0xDfLFjRh4rSL/CGJqNrx9mz1awVnz5yjb9zbfGDoCOh9JqST0iuHnfke56pBkhF\nPDI6sWNbkOBY+DKcFoSBa2olKqAqD9MPnaRgxigRYdxOPfrhJcapuH5XKMPpFAURP58OUvfan+aM\nn+P6rrIknoXGasS11BcTECsyX9taq85uzwXiEeDRvOcHjueU8Hk8j+fxoxlPirCCD7i7zSlJ0WAC\n2OKJd4fT6YR1XcVIFUhi4OiWBcucd0ljtCDTgRpC53dAdsJl+ZR3lqMzgLlU4ikV4UtB0K2iKy6i\nfqptXdeiqCYlIXvuWGmxGxvGCY/3OW376pv32Px5LmpPFxtBK19dTiCKMhfrGrE75O/+5ttbHPZF\ns6mDsSPA6ZlPBhEl2lqx51Tx63d3IIZFjP2EGIOkgfeHB9yx3M2Hx0dseI4P8wGn+YjNWsCyHXqW\nNHnz9jXuH/KcP+yOmI/5PY+3jzBKIzDy/WI74oa5iNN2KFJJiC4AuqvaUY3wWmqiqjaCKqUCv661\nS9ik4aCaJml2WDnjJH7Pz2jIshkZb6qefD+g5zJB3/cSMRMRF3eZL7nUKG1dnaSHaJswYKgFyii/\nyfAHNNElmns2ohrEtd1ISlVjHiEgeofAkV1YV9DCQFYo+MK/W06AczU99g6JhSC9AjxbzPnjCb4/\nyeEAQIgOWg7RyZFYo2EZXtQPLxGCQ88Rte6UAL2v6AL2ZPicFE6clXRdQNdbiXgVJYxjcYHXOHGE\nPC8rDseD5MddZysyX6kzXnA5arkOP2A8MSX0+Miic6pxgQ0+wrmqp+O9E5wNUI0KlFKSBuYFpF7Y\n0BBpM0G15N6xESRTfHJcU1C1lR5SqjWJmLByGlk7FA1dR2vpStbbMYfVylhRQlW6Y08v4LgEfPtt\nphv82Z/9EjPfNK9eXsPa6pC72824u2Nhw/c7GMPuztcvgagRuEu3ugRSjNcijcOcz+lXv3yHwz7P\n5Thu4L3Hh9uskHH78IjdkVVKnQf4Br+9u8OHj1uMF3kuuv4Kw5QX15tXr/DyjlOp9R1mXkzh79F3\nJncBAVxebvCCbb42F5PUPmJUWTm0MYmIZ53B0qeuD3mpZ/oQ6mKxVvqStVY88rquy92olqDcINUF\natGg1Ht06AERbNxsNrhko42pH6R+VxarwkpYlgUnpletzglUg1JRyfxuapdH2RRVg+WLZ3iKnHm1\nTHxe5Igk7YuLg59PWPesuDH00IU1QARX1F9PR2BdoPiZIueldpYSEHo2eOiPgl3UHDBM20FAkW6d\nxfqtM6MojHZdj5h6KLadD7H6F/bTgFj8FDzERIWshu2saL0RJenEIwDHfZ7X3eGA/f6AIwcmpEnm\n/3RaGof4eJb6/9DxnBI+j+fxPH4042kpYQh4fMwpidamibCqdIhzDiGG2ntTtQCXQYENduVMRqTi\ngDprpQgaY8VxaKVz56jgZ1KSiA2sIVSOE026Wb6rEqIbi6FQHWhVIqyLwywI/ijk0eAV9hydfPX1\nR6wFwf7+AdZqaTrsdkchJXuvsPjCQ7NYTg6rLwTqKBgyrbV0D7/88gPu7/MOPAwjkBJ8LJFTj1dv\nM9Og31wIeXy8GBDiCX4tZOpJuHnX1xf4/IvcZYxJYTnk19iU0PUKG5ZCvnl1gxevciNhs+lByOca\njMpuz0VKJFSRdIValNZaCXmdOIUfxrHiqogEcNp1HQZO4YZhwDAM507dggVOwpfsUw/iqM94A1JA\nz82RcZpyxwyZ1Ny6fWcGRE1FReufVLWFS/I/1Iip9vzSGdS9Jr75ZyWvJxT9+gz8zBMGBE6r6HgC\naQUl8t0EzRrpQREW7s75+YC0nkCMOyPnqrJrJASTX7eog2QfmrGJm4sNVvZXWOZZVEEP8wHjWJzG\ne5i+Q1dkn7QSTFskiJsOSDjRoBQxDoOg7GNywnJwIWLhaHD3eMDhcBI13L7X0uVf1lN9XglVLrmg\n53/AeLIeVkmFDFXgnHMOrlmwEpLQKJRW2SoJvDCd1aOqkkKI6QztXH4OvnaUSg5cH4I6ualplaf0\nnW5vfnmTjpbZ9tEDvNAZlG4lh/NJieVR3w8Y+px2eEe4+5BvhOPDiq7rZBE9LQ7eFWBlZgEAgPcE\nH6sQW4iVagIoWWxPp4AQOO07eSit8YKNI7pxg1c/yXZipK0IrVFcYIxHP5aHnpC4Rz5uJ3zBkIlp\nMyIe2DItZpjJsMnvmS43GDd5AYjKV9BiTICPgmROPjYg0gYGkNDIJeezvLq+lmvVL4uALjvbia/j\nMAw5LbSljljTQO+9MPm10RgCy0tHDyKCtdX+rC96WKZ2BTMoFaKBlWWq8zUMnikkyODi2KShhJrW\nZmAxp8eJpJuW/zFfu3LOhHo923KHm4s21IqUAkpJI6QANTOFSikxdXHHGfF0ROINCIuX58Yrh1lY\n0gE9d+ANL1iJktSjMFSPyON8FP1+A0IySjYJ23fYsqO40R1I3KKrdpdSOgs+xoKiD+Ieocig/GVe\nPZbVY8t2cpeXG0xcK+usPYcyFd38ppP728ZzSvg8nsfz+NGMp+lhAVUKmUg6K94FeFe1jvK/5z80\n6qaUUpLUIqUqlp/pHg0JOcQKKgs1kiuUjsLNUkpBi4hQG2GlBu7fpoZtJ7JQQaLQF4hyqltAbwpK\nOobbzaXwAGNwgidbjgv6Ho3srRXgXvBBFD5DyL01SUm0lR2GEEEoeJoa4Tmfw+UXL3Oqdp08iOkQ\nZEw1ewgeMZxKfwC2t4ipFNeTUKheXfdQzD/sdY/OdIgsC+LCCSunD9oSbHHA6S0SIrxnMq8nuJnf\nMwf4gr9xvuAeRWGy6zuJsBIqNaeztlq32Q5d10kBGKqRF04RGjXyUoxv01GdRVim6RgKB7IMqj07\n1WhjaaVr6onvDrlTUuX0tZ1B+s4ra+E+9wtL9hAR+dmILkIZBeJoElaDinmJrlF2XFckvyL5WnQv\n6p6RAjxf91UrKFsAqtz0WjwGvt696VHsNbQx2LFa8Gk+YTkesJQmFSWRUTK6z80m5MipiBgM/YBp\nM6DrCvaqUZhVHda1SAfl0s6Lm8ytfflyiw1H7m26T03BnVL7jP7Vg9qa0m8b24uL9PLF69/wrw1Q\njoDvIwil85d9ciTf/55Pf/PDjpfOfjzsH1isrXavzkpc5+WLKhoHEuiFIo3xIqcTV9fXVbNJvqd0\n1aLUnM64cCki+ApcdK6qQiBmsAAArrnUfHaZ97i+yikhqdo6Pz/w705rbRWf++qdLf5A7XrhfG7L\ng5/rfRWWfLb8t6oI1G4GBLcusF1/JqRYuXZV2q+k8iLE2CgqnPlHxniWKoZQ3aK1aqApdIa5z6Rf\n19RXi6oAQSS60VrRxYCTi7jmB+5sZhuPQsUCdKqpyZwhHuRtjYoI8xTbIUd6BuGo7y1/pLP5b+e9\nvvrh/g7L6Xh+LzSLbb0N2BmqfHzzYJ7z1tv7K/2Vz65qeMHfJZyfg3GbL5BDfNztU0rpt2Z8T4qw\nXr58jX//P/iP85en2F4VqQmBEpSmpj5V7Y8K+TMfZBL9voiMVi5+gKH5XKNU/eiYxGZbvrLc0OdP\nEtpJ+q//y7+JP/7Hv0CxgL+9fYDtSSYgFAREp5AoYt5xwVBbTFwb6Lpr/DP//D8HAPiX/+Rfwnab\n5WQcJSDVgv5+OeGei6d3t4+42x349wse7nZ45IL6N9/c4t1DxnjRaUUXOGI7zVI8V5Twd//X/x7/\nxr/+1/PxdFZqNClVg9qEekPnuSDMhx3KpGtdNPWVEKGP8xE+pkb6pEIQiEgE8YY+S4KUJopqbMi0\nrg9sjlw4slQK//P/+D/gX/gX/wQb9mEcpkGOQ5keVucdPfiE02kRj8F+sLgsqhPbEYYfBLeuOB7z\n3H38+BH3D/eiMHC52eCSazBj14u8jYbC6fGAj+/e5Tn/6hs83mZoSmcJ11eXfE4Ru0NmcNzu7vB3\n/vc/w7/17/xH+fiiQyrobh3RcXQ0Tj3GocPAJHGttWi6hxiFBRJiatDx5UGu92zRv7fWyAKqFYHS\nudJIEjmeCiXJCiflk4G/+Z/8h5j6WFWBUxJVUIRYBQaMyhGvr9e+rEZaA0VpNEYv91gIgY+nfGEF\nBCmt0ffF3l7DGAvLiiNW9RJNtQuWptrsigD+p//l7/w9/IDxNC4hcL5yni2ZNa3K8hjcqUmosiCh\nPiAxVkWGmCKUrmqk2lQXlVZTKbBWFenvP+zWzfnT1fz24V5wN5q0pLZLCCBONTb9iM52CKykahQh\nFHlm6/FwyAvMn/7FVzi4/BC8f3eH+bDiMOeC5jffvkfkTtxymHE4MWVpXkExQClOmcMixq5dTNKY\nMCnigtOj5BfubH034mijnkyWaSKcxt0l49g4tWjO24eYsUipNB/qDWSMkZRtGicYpRCEOuMENFsM\nJACWNym8wuJ+NG2x4TTadlY0k9zqsSbujjlgWRd2G8pqpJY9H/uuk5RnWRxOXLw+Hk84HmZRO+2M\nwbByc0R3IFEFNSCtBfQqVXhkoKcXh+gk3TBAYZ5n/P3/4x/k40tOXJ/GyYpBw4ubK4SrrSxmxpjK\nhY2VthZCXbBCCIz/Kou/krS263vp7GrK2LcCxg7eicpulr4paa2SInS5+g8PDw0IW0mDRJNupHqy\nd2hpkqSQ4Lk0kBCh+J43GlJ+OS0nxBgrKNcYmdfeNgrDfY9pu8U05mxEBV315RRqoynGltn0g8cT\nyc+ocIFIxew4d3vLKsohfgGOhgA54BCq2/NyOskN42OA0Qp9V1rTHfqioWVs7SIoAsW6UJ7xtT5Z\noapbTv792E1wvHiYxgB0bYTgjncneNK4YE7ekhI8Az2v+gFf/9+/AgD8+c//OyzcsVoOJ/SkQbzI\nrW6BNYxuDyuGUvJzEckkaFUW5QTNnz32HQwvUvvVyfl2g81dLuFc1vMJTZ0ubx5196NUQ/QYGw3x\nRAhFxChpKJXkciafkEoHqNFtz8dqpevq1sbinQArqTBl2WtAQI7DMGHkrhxpBceQDrcutZMaEtvO\nl1ofVfmgzSgg0OCjdPW8CwgBIvJ4Wh1WTvvCmCAwfVJAI8WrlGlAwZUXmFC7hKbvcTot+LM/+3n+\nvLiiH/L7r262eP2ahRo7g3Hs4PsWjtFmNKWeWssAp9OK/f4gaakxRgC0KSakwNGaIgTvBZLglqVK\nxhAJnMNaI9CY0pWc50U2LwWLEvX6xvVauu5Nmln15FPR68lQIMlUDEhFUKnvqjqvxvais2a7AX0/\niMO4DgqKLfA0MxTKvNSe3w8vSz13CZ/H83geP5rx5JSwjITGlJIgjOwAIMvQcKoRIlYObVcfpCC9\n2x8QJM8P0JrQmfy6ruswDexVNk7o+5org0g+O393kwZ+z3GW36XTir68NkSRT+nMIFKvJ7+gtyNU\nUUQ4zdCl0OtWdBwRdesJivP/kbhIXMLttBfXkqnXGLhuMySL0+lY5aKnC9lYKFWg6xRNTediFsUr\nnRmlqqIF6DyqjDHCct0pxQTPHMF3334Uqk9IGhuu9bx8dQk7GEybnOJo04kA4seP7/H1t18DAD68\n/4Dt5kIUMxbn5BwuL7fYXtyUGQaVkJsPy/Q9bFfECOs1cc5hZaBtSoTQGFvEmERGZjmtQiehRPBy\n3QnGVMPVGCD/lqAkCkCi3Kgo0T8bqwIcxTS4PcXv6fsR67LiF7/4JQDAhwXTJp/D2+WlRP6vX91k\n4HJRJdBaaqRRQX4GPAIXSb1zOO6P0lHr+066iX2XI3AgF1e891jnIkh5hOdnSCsFTCWl7EWxtJyj\nc0G6tEkpqNI6bp6Z/KySdOkVAFOwV5SElmWsgiomIyZ354UDqqrHozFW6pPGZu5gqT2SVjAFsEVR\npKZSrGDRRD88OXwyrAECoktnqNUSUnqW6ijFumVZMRdnFh8kp56Pa9OdSwg+YWUHkLH3AldQIKmN\nDH0HIiU2X61SZXFIyR/33RBTRSCU9xkC4yIRVi8LYFIe3izYcepox6q8GZGwlOq8DjXPHyy20wYj\nLxbTosGnAauiuPdGH9ENtXh93M+ine39AhfzZ2/HDToOr6Hzq2MB3sYo3bWs2VUKoh7e+eqs7D0O\n+9LCXhFcKbYa9H1eoK6uXuLq5Rbby5yyGd2JJJCxFnsucD/c73B3/yjXStH/y96b7MiSZFli58qg\nqja4+5siMjIyqyorq0mgAJIgwD8gGuCSH9CLXvNXyB1/gBuCK4I7Ar1gL7jisCCBLpIFokFUVVdW\nZkXEe/58MDMdZOJCrtwrFhmJDF/0IhouQGb4czdTUxNVFbnDGYw4sZRCgnB2Xh2Xm06XtSQ3aM1t\n648xJ4TYVGSrrZcR/l+86uq1n0vWjcq5AceDkdQ0pk0WiIq254c3plpLza0BZOR1BUXuRRhdsAbW\nYJ9Zjy2EC3gbxrYGrQlZB+8HqUF572WhLEXJ+6UULAsXt7eA8/mii3UGBnboRoZoSpkCIBVV0L3M\nsul7Z2XRpDIIBKctUmQdDPNXvR/g2vG3JPfHlqrbUSvCO2ek4O9MkYzaeiu1y1wA23Vm+86gM05S\nUkcW3igfFF19lkjLSLlPn38o0vgD4zUlfB2v43X8ZMbLqDkpYeXUwDqrVIyigv65VDXHVgRd1kVs\nq8MWpfUbttjBImq3qQEh47Zh7rpPzcQiOgtjuhJdKVdpUa8T3oYUqYNifYy1OLER6uBHBRtmC1hC\nbAz1UrBu3HZ2e/EeHEdIx9HvDD5+/A5v39ZI5fmygoUcQUU7IZksbu4O2LUUyUzIqbmkLDCGxeRm\ngvfskkMtuvh9PE4pRjpHKUbEbUPkb55DwuNDhTWczzMAlhUZJxwZS/bzr3+GD1+9xcSAQcCIu844\n7mQW/83f/wM+f34U6svgHaZmJDJapKZiuSYUlsvx/N8+D+zNQ8goTqd1hNsuHKK27ftrm1KWNry3\nQ+UO8nvO8wVuaLw4q1CNsFUQ55UjjqZd2kvrAL2+glBvbyv27XIxmFjXfLc74MCqEDc3NzjeHLFn\nft4wDiKil3OR+38LQaWSlg3z6SLRkrNePAesMWLdbggIRAI43ZZVoi0MHiXt5T2Tb/ckG5fYUYDC\n3k9a1LdJzUOCqaqmrTjvvHgdOmtF1NEZIDe8YQGIgvJ4c5YyhiWDkVPC0Xk4Z6VZQlSkIddfUyuh\n9/cgSX9kvGzBKkXShgEORQB+Gq7nnKuc7dK1oBmXtK1RQuplWUQGg1DxVk1KtQya7piurU9k4bzq\nMZnSLU5detjLVrTfjRPEYHJLhNiI29sKL1gii9PyDM8mECYNaAbFS1wRltZaHhFZATKWgD/78/f4\nP/9V7Sr58Q3e7ZiXZSxiblZGDjmsWBqy2ZDoZlmbcHuoFzyui5B8t2ZO2QwSuiu7rovUzVKICGuQ\ndDHGjDPP+bIG0YqadiPeva8wgy+/+gI//8UXMoHLEsWSjMhibliyz89YloybY30wjzd7DFO7Hknq\nM1tUq6dmpAoqYn7aAxeds8BQj+G5JS4OKmvudNL0msYYkDk184PD7rATDFMqRd8PBf7GXOAKKZQG\nUNI7lDHhiOAaIJQI1ljhHMawqqu5dVIzGpzH4Lw4VhtrlfxNgOc9xhorT2QMCZfLLMq44zAiNQmZ\njCsz15KKdLXXy4KNDSOQs7JNCgnMYGyp6TBILdOPmrLCeVVlXTdkk+X+cYMXTq61BJsbFMLI4jV4\nqgYTzKAISUs6hlRPyzkHbx28VZ5w+5yqWsu/75Hb9OOXoZfVsHIW8J61B92tCqmTSk4InQbRtkWx\n7FqXVWoS56ezEGoJGd57KYTvsJN2+wwjC2MmYJwmDNzWrUFOw/5kLcB3kZeoPpiCgQvMy3nDjr3+\nrMuwlvFBOSLNAeDFyBaD0moeW8JNwyYNE8Ro3UTEtODCi88uzciHukA8nc6wmXfg6YAtRty95cVs\n2mHH55NyEM30cXTynU4PKypjvi1YElTUHS63qCRiWzv2fIiNz41USFr5N7c3ePOOaT5vb2G9wZnp\nGpdLEGWKadxJhHHY32A+Znzgdv7PvnqHac/1EhNxuVT82efPn2tdEuiaIgpvAWUpyo+jx8CLjbMj\njPEwvEjHsgBC2UpIvMCHuMmXd85WwrRri97ApOJroCZZh5SiUFBSLlIcTinogw+CbUzyVEn4E99j\nJyLZWEvqoAG5XKHva8TBG3jnhmONq7ZfAHIsmM8zZtY0242TWGDFmAQQbVCQQuw2/Rkf1M5EAAAg\nAElEQVQbqzqUXBBWlXZuo93n4zTBuaY15iQ7KSlrk8wOME5FC8dhlMYOAcitwEtGoA8cvypEJich\nppOxEl164+CM7aLkIvLaPb0kfZ9C9SPHaw3rdbyO1/GTGS9OCc/nuqMejjvlbJWOcsM7f89Zax6F\nKUbJy+fnk/K6kDGOHoXBmH4w0lrFRtjWFpLX7kMzW+DEGnyQ9n9Xo9XZnJ+QWKOHKGGZOVJ0BtY0\ngm7GcbzByue1309MW6jR14V3vIdosDOcsw8Dti3jZ19UjqW1I3b7GkWVNKOsDVm9wQ8eZzZW3R5P\nOHCEhZKxMTUHVne+ZhihAFndJZ0zSKGlOhlbDFIPWZegdT5DotS5O+xwYEfnaVejkhNTeO7vLyD+\nvPdvPmC/byngHUIivP9QuXVf/+JnOBxbjSrg8bHKN29bwLLUn5sEsLFofhSw1gp6Pk0FJbYOVa0h\nlrWBKS2Imt5ZutI4a6Bjz0R59UfU6LI3ofDZYsub8gQ7+lFKCcphIaG+tJilDwokQTUaJDTCWXsG\n+hpUKSS12hILEn+35bzg6eFJZJrfvXkr0ZcllR4SIn/7TqlI3ctaRayDzBULos6fgxWdNeXChpil\n++ccd3p50vxgJKrNsYjRxFX9zzqYDBh+VgyipLDe1M5g+3znbOcy3XEkDWQySwezeMl4cUo484N+\nenzWQt/gFYcVA+bLLHWrEqLUB5AzZk4pS4qiCEDGwFCGMa3VnWFtq0dlFF5o4mIRiXApTTb3+7kz\nP0hI6OJfAPVmsbYulj//8g73D3XhNTSIkFumiLQCmR/cDVo3ceRQmLtGwx7k28V2uDydEVjiuPiC\nT99UXtrgBlks1rDCD17TImfBpQbspz1SqQtJJOCJOYaNFtRqNTFltCchF5L0et3qQ7lxzSOEhB1T\nI443A6ZdPbYfncA0CmWEnLHwg/D58yO8qdfzuL/Fnk1a726PeD7NOB5qCn17d4vbW1548iqOxMMw\nSIrQHq7a5GhpHMGaxmkklNjQ554hC/W7eGuVv1YyAtfDYu6NSwsANSStBOpWDy3ywJK3qIiO9sIk\nqWOthHbNjLZg8Tmn3FyfkrT8B2/gBQVe/9e4ndMwCFMjpYJtqan2Nq84P9d77fH+AR+/+STfw/2Z\nw4GNiUfnBWaQU4I1FjvGIg7jAJbyR4qa1no/SPG6LUzD4BVuhIJSOH0sG5psinMOZFzHHVUb+zVE\n4Whap0aqmQ1gtZ5n4doiaSyGdk5ugPVe4DsWRup8sErTMz8kWPcjxmtK+Dpex+v4yYwXIt2LtNK3\nZVHiJUFX9UKsB6RgsRZeW5Sq8gBgsFbcb8gUWAdMTHr1HnCNl4Yk7exMK6I1Gm4WIxywahCgoaxI\nV3AIu8aAgXejh/Ms6USICwyD6wxZEGUcuEUek5qzTt5KCnFZz9j4nN7+/Et8fDhJJEDOqirB4NCc\n+ch5oCTh02Vr8fhdjcQO0wTLn1k6eEHeqvdeixiKI2TmZZWoRPJSaqyQJA3XNrf1g3g3Wu/hOZ0m\na7BtG5aGYF9XFCbfZoKkb+NU/Q4bHGK/26PZOIZlFvORGBNKbioCimIWlQdjVDOsGJTGD80Gsaha\ngKHe2DZL5JX7HZlq6ieOPJ0kN5F2zowD6AQFFHfaWARtsbuuuFzYzaXJ/ViDLsUyIktNqHzU9gwY\naHoYUxZk+jqvmJkMf36+4PnxpFI4UF7gMFR9MgCICHDWSXPJ+1GArWTcFVi1gXXb3DqrOWxB1nnr\n5pjIwNoeWqIRWm8nT0ZBwKYU5ESi1GGMkYjaGCORl3UGjowcwxgjKXp9LEv3ORoJ/tjxYmpOe0iW\ndVXJYWelLUogWGfEnTbELA+SKYVliAE7Kfaj3ggFA4ef1lm5Aaurb/t5rVIWTQ4W5goL1kbpalst\nM9zd3inRF1noFvN5ltpPpkpXKLktUjtZoMO64XBT0yJQxbUAQCozLnEBNbneSbXNQ5n1ofAe5/NF\n2OoEginNxWQPw23+uG2wrTYTAosKtsfCiaNJKBGUVNyukBEyajEkgmwZqvAwjqPIgJCxADlYEWuz\nqoFlFTNlXTXMbJLGhkikdj9+/A6/+c1vAQD3333G8yOTdQUeoQ+FMabD3hhZyFtHOXSdvD7uVzn2\nDm8HXsBaZzBtKghnIHLLjmoNR/5ktKZTS54N3U2waFIn7SFWeor1Ko/SHriUElIMAkuIWwTxfbOu\nQRapZZ4F2R62gLAFpeDkgkaY9m7ExAyHaAw2P3R4qkEcgsZhwuCa2J6m4SBdcNqoz2orjSjR/Yp9\ngOuF2FjSAp5VsnKTrrZsJWdSpxXX6ZFZvs79oiewJKMwFfcDRPEfM15TwtfxOl7HT2a8rOheVL42\nbJvswj76boWvfCGLxltKyE2KNW4iCesHJxFVNVXNilamnosFAW/mlOF8uJJClm5kJ59RV3RpGwGo\nmkumC4kvTA52xcjOs5aEYTdIOoCQcGB/v9N8EaBhmlcR3LP5BuGcQFR3R4IHNdTvYOB5h5tPK9Yl\nILjWh9owMmDucjph42NbQFpV3tkKuGw7WWeCUEwBtbTZWtjBw7Vw0hQMDBYNSfFplr3l6hw5GEqw\n1AToxk61E2gmFrlEhG3Fyk4sp6cnXLbaFHh6esL5iTmHn084n1e5LkDDjbWIRh1rrC1ILC+zbRHz\nZcbGPnYZHcHWOjFlNSZJGF27h1F0vmIMEgn0BFtLhqM8yPdqDQwiA2pzTirPQh1+COAUxynnrpf6\nKSjdOWWRB187w9Z5XrEuik9zbpBUz7kRvskZjxMzDGqUMowb/NAkW3YY+OeKP+Oo2DptwwqCvCPI\nlyQBlr3SVaOr9NgaK/PnXIF1/Bx5Dzeo9lZJCWsroHdsBWONRKTWWhhrriOsVsIh6tJhzdZ+v7f/\nh8fLU8JGsDXqqJtTFORqSgUlJnX5CAGBtY7CtiFyx8+PFu27e19TGuFLdmappRgVPDNUuxVtwcr5\nBxev+ov6H+lWGBL7qaoJx+G285JjkylY44o9dx5LzDiv9eE0o5c0xhLwH/zlXwAAbt9O+Nf/73d4\nfuYHLgLG82LoDaghTBNhP+wldY4pwgnC3qAwuHZ0I56X2hKqQnRFFVdrMsTfT9NeOzgM5GC5c5li\nkTQmo6M+kAIkCggxAaExFIoVU856Q/O8pq2mpmigzwFgA9hn6+XgBQaB2/fN9TulJE7BuVM2INLN\nZV0WnM5nrFwrHLyVxXbwg6D3l3mV94SwgWwR5HaImyzqV7x3lpuWehSVTmHEoO0MRFmkrCgTUBSK\nY4iEWFxb9vwAO8v/a6Rj04khRiwM9LzMs8BhciEcb94Iiv5wuMHITkzDeMDAP1vrsQsJu11NvXfT\nAeNUNwbvndatyEBPnFP4q1ohiZ9C7lypLFl27Qa/xwqJOnsg+AYFsrC8YHlrkLOVzaD6EOgCqIth\nOxWFG105eQu4FpCa2gsQDq8p4et4Ha/jJzNemBIWAX4WS4JpyTE2BQ/GthSRjs05IjH/MMcoC2/d\nxeXIvEqzumRSa3RTSDA2jZf0B6OqP3DOQCU/e6OYqNY5gyPMzFaOJYKsE1qFHwa4tqZbwpllQuzO\nI3OR/P58RqGqGtq+V2AM2u2X75CHVpTNsGZAYLDn89OCYeJiuCPsOFXLZROybW5gPtH2LoJ1RB9e\nUw27BaxYSCIWUFFSLrLOZa7dp/2e9dMPM3a7RvIdrjpAzqmtg3cO41T5iNsS8HDDmvXPKxamJzW+\naU5Fz0khWUAmZO6EruuKy6zyKdbt5XxbNFO/k6ZcKQekEqQDFkKQZkYpSaVEDd+H0mEsV8BP7aBx\nug1N9QSvRXQ1F31KCEMS2fVljC1EzDz/87IIxSYXwuF4hxvWuXd+Qmm0nQyRNjbWw/pRUkI3KN3G\nWCPzA9JGhm069kblxUFeUuCAoB1SssgldlrzRt7vYeAGTpW9Eblqbx1S2CSgqzlQX1hv88Dy3BJ5\nl06iWv0dQCrq3SWxf3S8eMFquXlFSrfOSkLuwkNCBz0wRUClzhqYnt3ecZFqHatOToy9bjjJ4gXU\n9rWg6s0f+KIEXQx54pZ1rQxooJqQ7loXc0FhZDXlBFs8Ls+1XnN7e4Sz9UYhN2DwzRYp4a/+r78F\nAGzzM54+B9wc3wMA9jCYDqpx1JyLp/0ECg6FF/zRUuP/YhpHeAYJxpyqvxfag1PkQTDGiEtuyaYD\naFYrqqFBIwoJQC/kKG6987zgnk0YdtMOh8MR796wSavxyKUZdUFS+pQy4hbxcP8JQE3fG/h0Go74\ni1//e/X949SZwdb5s3AiDGeK2sIhQ3SWtmXBcplFBSDuVD3DWKdS2zkhRn7wkZGKE+G3EDbhwpWc\ndYG6une4TnXVStf7R+ox35PfNpYE0Fn11/grsIy0glyLpMLruorh8DyvWLdmfWdxc3OLI+vcAw4L\ndxDPl0WMP+p30Q6uc6NqbaEufHxCwhNtQokoqlNlrBXgsoGVBboUIKSI1AxZrQYL3lh4vv+8d/J+\nbx2Cs9p9R0G3AnbzaCpzoZUU8L3RwNA9xOEFRawXL1jNdhrIKoZGWU4QRCAL2PZFvYffNUkVxeZP\nu50y3Y1ByVkwPesaRQqDSpSbaBoq+1wX7K7V3c9M185trxkmi8zo5d3uBktgJD4yPKsPpBkYne4c\n8/KEubHp3R0YAI/JLPj5FzUy+fjdGSdjBJtkPYkoWwwZRhYYIKUVl7lGJMaSNCMwWmyt/lMKEsMC\nHKrTdcNleed190SWhUgMKaQAXNTnL0Aijm1dcP/pvh7bWByPZykAj54E4Z/jJiKE2xKxzQGfPlXa\nzRo22Wi++PJLvGfPxD/505/j4aEe++mp1v2cHyQyIOMEPlJykXMvJQAlaLSegxKK0UEcSLFYMQcW\nceR/x9BF5FmwfshUv3vWeWnDEInsiSFtbMA0XBsj56loPS8nldNJETFFRW7nztItaQ23lhqb9vkA\naw1GRrdnGGzcfFjWiCm0yMaDrJUswDjXGa+obVjuClGtNpRSEpyWMUYWL++8BAExRiCpwkohKM6L\nDAbf7jcrKH9jDbu4y1lIPdAYUjl7MUfhbKQT7SOCLnIZUgN7SRHrtYb1Ol7H6/jJjBcj3X/AMQl9\npF1MBcM1SQk7OEFXW+sFVGm9k9W/5IxtyyIOuG1BVmxHRnS4nXfww7XVVW9seW0YqudcX5Bk11yW\nZ5Ho3bYV49R0wwvKtmJk7l0pEKfqMC+wLFy35YDhWOsQl9/NGIejuN4QQaK3YThIWz9sKywIw9DI\npBaeuWfnyxOGXRNAG4Rzt24JKEVQ05QJpWlAhdQZhhpQyRKJxZRkN606VPyemHD/qaZ2T4+PoFJw\n4BrWV1/9TBymx3EQR2dkwrptuL+v1+bz04PoXi1LxIE5hze3E46smbVr82k8bKu9kENm78EUAyzX\nOA+7AfntDdakHbst1JQypR1GTnWmacAN69HP8xmX+SzlCSBj4Ghk8E5qIjlFUBeJ5hhgxMrMXvEC\nG8dTNKOkTprld1XameWbY+Aoi8/b5q6mQ1oTGkZM+1aD9DXqtkpqjw0k3EUslahoFQVqjIpMoiBx\n6y9miCRN+++6LpJGT9NOUg/vRqklETKK0lIRQkbgSM97lYZxzgkMhjiCpw7130bOffe5ZjdGFoQM\n4vk3IA2mahFQ5vjHjhctWATCxHUgQ+Y6NWvhIdU0KzWkuoHCBizBUqs1FDH1XOYN67YIy7+UIvWY\nYRikPuG94+KehpAyUR3OpJ1tPRYvaH4EOEw/TF58056eTlrwHQkJBSeuweymgygqzCUgMzzD70n9\n2raMw24U40jrHYLchAE5KRo45ySF7ZyykMJ30wERqrQg1lYl86LZ6lEBkZoeUecRZywilHS8hSDK\nBluIkkbmnHE+1wbDPM9Y5hNujkc+h1HMSIdhlBrMsm3YYhQTg8tpEQ0tPw74i/BnfG1ucGCFh6HZ\np5PpHpJOT90kKQdMo0dMHnFVv8TQ6m8pYeIH483bd5K+ni9DZUO0hTgFIeISikAScqgYwFYvSymK\nSoC1pKkj1KOvd8yu/99j/ZIsXqkkxKywDQpGzCXWLWBtUJ6gulshVSXeloou2yYLYOb5qvNWQQMN\nsZdLV7guAmxBLkWt8lJrYizacOnUFrybpEC/ISGlUlV/AeS8oJGMxmlAM1Ld73ciDBhjxT+2gr91\nTsUVSxKhxpIzDBm0ykVN/7lWVbKStclI3bu36/tj4zUlfB2v43X8ZMbLIiyqLW8ArJDZdjWgr6zl\nUtTtuSThvxG0DRznjIUVKk+nE0JYBZxYNaEZATyMmKamN216v1BORX+/xdAXV9vf10sS1Hk0GU+B\nC8PWY2PZV+MHGO8QMxfGjSL2nUmw3E28GQiB05GcCszBIjLqfAubRA+FEhwX9G/2R+QAXBj1nEtG\nbBboKGAZeeSimleDd7Wt3smOJCZZF0A6OJWQajDzrr4tq1iNpaxwjt04isElckQKmpbnrMX9+aLd\nxMvlAuNUlmZ93LByJHe6XKR975zDkfXOdwz8JBjVqer0nQjAwDt3nnYIOWOOaiOWug5li8r2+53s\nzs6ztn9pUelFJUzISOu81dvbvwuMRkhZoQumFOle5VShNy1FpIp50BOX263y4qLYk22YGa3/fHrG\nw3O9v+4fH/D5oZGfN6xLwo5LDuNuxMoQkFKKFNAb6bvJB8Wk3gBkSCKSdNWo0thDMo0ClKyZRose\nY6gKtS0ip5iFQznPC8ah/nw87LvmQWtkKSC512dXuzMDa4yAoivCgSMsqPx10ZK7Ro8/YrxwwSIR\nV/N+wI4hCnZ0wurOxSDEIMJ3WzV945OEXKD7Tw8Ic+ty1S/SZGl3uwE3TR1g2snDYo3FMO5gm3EC\ndXZCILEHd8793kI2jKPk82tIiPyAvLnx2O/rw7PkhPkxCHE7xwAUXiyNESnl436Py6nehK5MKMjY\nsPCEethmUGE8IofdT+kJVIykAMuyAU3/KwORhf7GcRALuRzilfrAEhYEnj+YroJlDHLOmvqEDULZ\nyBBhuTd3d3j3rsIY1mXF8+kkG8h+t5cFa1vP+PhdrXUt64oPX37AF1/W933z7Vj1vAF4ZxBTQ7VH\nqdtQ19rL3eKBrOmsbT6Ak8GWMxw/7KWsClsp9RhA7V41rXIgI8ZN4BMpBemokbGS3hTUDl2rgcIY\n0TdLSZUM6kbZMIWVz9AerCudOQNFjlMVEGxQBpSIM9OXns9nPD1VtsLD0xM+P9QN8PlpwbJE7BkF\nvzuOeHu5AwDM24KNWSDGgt2wmzx0FM22Xs2iFD2/JJAFkpMsxUhJZNui4NgulwXzvGDjZ9RaAjXo\nBZLYuIUQpTZWGCvUmAcEUuWKDo9mrGHbsB6wpTWsVttKpfseL5BLfk0JX8freB0/mfGiCMsYwjg2\noJqDa6A0q6lF68WJNlMhAfhRIQG95RgF4Eemool9kwVxRjSIQL2mT9WJtFrtl3BfqZz1X7ozt5cW\nKYaXGHEcali+2x1ATEi2ISAuM0yDmlHRgmBn4DodR/zu72oEMtgJ22WG5SjwcDjC8j5gYaSAnkvF\nqjSZEA+DWJqhZkFhBLy3kxQ9c0kgMnDcNTS0SgfIGS06Gxg4svK5NbTgVNY6Qc4fjnuROvbWYVkW\n2XUHO4hUyvPzCZ++/VgPZQ1+9Sdf4KuvqwT0sDOCJasAUC6WxyRpQSPqFurA7ejuiZQFV+TcgMFn\nGHvhK3eWawcyguxfllUNOUGVvG0Ux0fdd5fUJyaASLqpRCTpRyzKQzUFcu7FVHBk64KlrOli11sC\nSkFOCYHBrCkVzBxhnS8XnC51jp6en/HAlmuPjxfM64YLMyvGg8PN55pJfPn0AftjkwSK1Xlqq8fb\nwiqRrCOn3URL4rBEDdNIHo6aqIBXF+hUBCiaYkEpJB3svlkVU4blRlFOWQHcKEixj0o7gKoxQlIn\nlm22klUlfUYVHglLCoAtLwibXpwSKqvbd1o52g0quSKzW9eifsGu/d5Y8NaIF1LVoVYRMGPVyivE\njMIeNcZkkA1wrZVsIG1WIp1A6jqY7ZGhkDGM9e+74YCRv/p6Oct5bLHgeJhg+SFx3mELKp07sH5X\niKvQLY7TLYisWKqHJSO5pk6RsbdMaCWL8+UszH8DD9ecfl2B2bXvCHhO59rNIo7K1quF0zDAyg1T\nu0GWaRRms/KgU4fWK7lI+//tmzuE7SjQgKeHZzw81LrL58+fRfPqZ19/iT/7s5/j7k3tJsY4y4N1\nOS2SEpWitCHvm+aWAggrMrzVULJKLjkD65ykk4YcDGlq2cCNMQURj1PwpHYjuRqALSa4Tcn2qWQh\nJaecpbZHWSWSB2OVfuOqUnlfJ9J6bJSUUsGRCqtpG/g4arnkcNjjcsMd21iqWKFpx46SXm9xRWQF\nkJg8co7IzeatxI6elaX2VqDuUEbgBq4DbU9yLUopUgfzg4OLDiY36IVXXa0AmWcyRtLzVBJSil0X\ntRMAJHNlk9bDjXrMU13wNcVvc/yCEtbL5WUaBsflAicPBbrkkpBTEdG9lEr3xSC57u6wr1EWWLCP\nik6UdbL6hpikdUuwgA2Ydvwgw8jFt50g/9UE8M83xx0S19/cMCIsXD+yrjtGLbAq89/IDlpMwHis\n0dHjU0DZeOpGYJpGsapfwob9TaVezJcNkUX69tMRBz+o4UUqyFsrFBe4A+/2mbAXRYDmaVj/47zD\nCIaVdKKJxhqk7gHMOaNtwzkXabdfTmc8PjzyIQsMWYE5fPftd7j/VAvt9w8P4p13ezzi7e0exA9P\n2C6Y57qYbVsUTXNrnHymUvRI6SBZuXZrCBIRkbHVJJXvlxo5sWJpIYkA47qBukgnbEGwdlV8sF0z\nZT3Un9WsImS1n8sdXzVmCw+9dwqKRDRbDDCB78UQRaGkoICcyvU4N8B5runaCcSyPfvdLd68rZHS\np09P+PjdvczJzd0R065tQFYWHeerqkHD9hnfqWcURdvnrPIxik1UXqCzXpVsc0HzdRnTWC9VY4GM\ng2yKIW4orebknJrSboFVZVsBXaPSqj7am2l0+Y72WmpQI9QelT16yXitYb2O1/E6fjLjZREWirRR\n7RZgXKv+a7euunyV1hhATtpxKR0XaX88oDDozlpCjMomN1ZRvjHq7kulYNh0la9ib43c2UMcOlgD\nH9X7Ccup1hJyXLDnGlaMJDWxTAuKsdjvaoT0fP8osIb3X7zD8U3drT7+7oJf/qIai+7v3sLbEQeu\nvwVEDOwwE8kgugYp2MMTpN3rB49VTtNrN40iWscqxAj8i3+hYH1r5YI55+C5NgUilFQkIrNGraBK\nydjYuPPzp08wHKU8PR7hnMPzU621fPfNJ5xONdo6nxdhJFSgcMG81L9t6yKKio4MxpZyZNXBavr2\nuas/hpQFAnG+zAh874RU0ePtviKyiqIuRbqi8/mCzOKPxjD8Q3TS+uj6Wlgu5dKZfyrQMgTlLHrK\nyNRF50V132KMCKnVs7LcWWTMle76MOwwcclhcHtYJs3vD2/w5i1HqzcPGMdBtLIOxwn7Y3OeIljX\nyiU1Wm7mstYZuMaQAElNrSBqeti6iNZJ2WDcDQIxSSmxvloNhI1TNso4TcJFjMkj8pxbo3pnMdbr\nJDAHUFeC6d3ZjU4iGMHPc2ZROiCxXqf8by8lLFiZXV7sVtXrUVv5npp9VC2U9oqhoiyYkhSGh3EQ\n0wKiUsPhlkoUCPm5Rr16A5dOWdRCFQBK7gir9ve/1te//rVACggZ+6bm6Cr2CgDOZUUwFiPVxexP\nc8LIRFDaD1gyy8b8EsipLhZbGVASoSR9WJ+4aLnFJMqa+XSpCPR2Dilja+lmLMCmCzQrjiBSvErD\nc06qqgq5l7SY3OJlU+tnAFASYWOt8cfHR7E8H0ePAmCZ63lfTjNyavOsIX5OGct5kUVoMB53NwyN\nWNV663Q54YmxR21xQ1G6SggRJ/a0PD2fMPM12m0BuRQsnKoRVO/bgIT9cDmfBYpibKvZtBSxYzkY\nEqkjMNxDcHlX6WJPhSGBgajKQFfDSop+b8Vk7z2GcZTFYRonMR+ZJsIw1k3reBNwd8ek+/0RZIrM\nwzBZHG9Yn30kgGEzsLmSuE0jQwMjMyQM1KW6IIkdWYptI7SYuH623++xZzpZyAlDat6PBrPTTd25\nPlUzoJbmdh6IKWakkNUTsUAmszZB+g1D0fhXjRcqteyC79Wz/pDqyg+M15TwdbyO1/GTGS8uujdt\nH/IRroHmrIFpuwMVpJgQtlbcVGLjEhOmxk3quFyEzD1PI+9J3J1DIgy8i5VcV/rGCUyFYJrjjcsC\nTCSrlb62U9ovv0LiyGcJERdOQVxMiGdu58eEJS1IoUYIJhQkbi2fw4q5tZYLwbTWLzeLpty6mglL\n0Z2sdeUmP2G5rNiY4D16j4XhAZmyyAKnLakkyORRirr+xrgJIHfISZDR1lrkUrB1KVlzrundcELY\nkDitmi/MSOB0e1sSjGmy0Wqy+fT4jMtpwbRTV+j4p/V4T08X4fA9PT7i80Mt2j+dWVa6qK1bCBvO\nbKL7fD4pXzJFGLICeu3twAiqZ5VT7ECz7GZjW4pTJCJPMSGZFp1XErg6JUO6YR5etcrGEQMTtgtD\nWXoncyFCVwh8PQVbhfJEf95ZkXVxxYLYlNa6DMdd45wzLpdn6VZbDxxYO20cDViJCNZXIGYTzxsn\nh/1+lDlp3WrntOveNN32+0m6uIfjDhPfVzZGkSpPJSIVK9+xyT3XebHiRp1Twbm5/ywrtrCJQIEB\nSXTpOtFLay3IGBB1YNEGKKZOcDNDoDf0AnmZl6k1FGj9KCcV8Ov0f8gQUkoIza15S4LvCduGWeoT\nSS26+OAtQh+GQc0HMrWUvaZHW0M7M0JZ9N2LCvrlLtznBet/+1//ChsvMilA6lYmLgJjSAQkCmJV\nn9YiWuun9YKHR3Z0hsXtjknDt0eMO4uRb0JDCXsmaw/TTqgRZArubg+Yhlr7gu75uOwAACAASURB\nVAH88LP6o3OitZVKEXyMHwb87/9LkUUqpiw1mLwuEsY76+uCtbaFLekC4ElQ5XWNaFio+vamdxQp\no9lmoQTZcO7vP+Hbb77Bhw/1vA+HAz68a1iuk9yAT5+fcHk68bwx3QSqjV5ylPslxIA11tR027Zq\nNsEL2M3NTdWNR10bGkF3XVecGduUkeEHh4lf57wKyxGkxAPk/Hu6/73lGUlXzmpfS0jQ7SFSjBdB\nsUutA0ldiqPtaSMnYYw6UQ+Dw+EwAYavjS3YHxq7wwsFbRoNUiJMbGcX1lHs5giE/a6+Z5oG7VJy\nWWPYDRh3XMscSHTRHBnkBqMJdUFsEI2MqORvGKk7ppgRGvthC9jWJGySwfcwJN1kQHWRb513KlnU\nXI0ibJCsBhMv6Ra+poSv43W8jp/MeFGElYtqVg37QZQcDSArNHKpqooNIxSBlS2jSkqqH2R6NxgA\npQj5OSLACgfNSIRAICAnlCbk06UMJRf0ipsKKqyv/O2//ju8vam8rXVTCRdjCvyeQ+zDDm92hy61\n9Li5rZGFnTw2ThUtACN8LQsyDne3XMSfXAtlYMyAsHHKlSNShqQNIa+SBppU8S9A5Tmu3D5cU6x4\nH079pv2+68aqxvl5vSBsoabLqPIwjfBsjRW0syHVe8o5wzm1ZxtHRa3nuMjnfP78CX/91/8P7u5q\nof329lasz1JI+PS5qox+/PRJoupJXG9MdSJG210VIJW6iAFQfXrvnfDQTIc43+13nVxygPMGflCg\nphDBbQcaLi0t1KZFEXZFx0MtRVPPkrhRVP9EpA0IQ51EcFfAr8eDbv8FXeRFgqcadwNubg9olEiy\nGRPzWA/7ETuOsIbBIqeMPUdKOU7SaQcIe5bxmXaDsEPaPA2DqtIaY67SLZHJyRlb52ZF1oh1GIhA\n3LVIW8I813v+clmwriqV4x2kk+ytVzchnlchj5MVWS9hr+B7c/dvKyUsRZ5lfhBaSmLgW8sUBN9Z\nVyej1AjTheSGLK4CQcqyYNUSVFsMC1xHybDOaH2qdNQZFO0elvpvAHLn/Uf/8X+Im2NdsPxgZcLI\nDYhN3ngETIyie7WAxIYppSCdvBxT14Y3iDlge+CHCRBIhneDPDyXy3M9FenGRLU2nwMmFqoLCbgs\nNbUqhoGATSq3p1DE1KHZqxmBatwTXHPVNkYIp8aQiLalZhHV6CmmoLDiRDFGUPQoCQ8PD1gu3Gm8\nf8Ao2vEQhYKCDOK5JqmtQbpC1pIsKsPg5XOttRi8k2OOg5eHEAVCK/LuTn4fwwbjoL6W3sriZQBJ\nn1KMSGGrJHagSjTLBhdVxSFD9JwaKFMsrMiJoKIxTtQjUBrSvGNXdKRvcfy2APFjtt9NyG9usQvN\nfzNgmDgVPu6w27V6lkVKRutWRQnsBMLUXjf5mg5DFywiVWUIMYhuW9iS1E/nuZKfpWtuDFzrGhpT\nAdqo6XirWYeYkHNS7ffOU7EukJoaW+rQoqRmM71pTEG/eP14Ab/XlPB1vI7X8ZMZL+YSqouG6cJA\ntRsimGp5lWvYupHB0AwcjQQY8N7JGkuldmdaZGi6Ipwlwn7a888Wu8MBSVAeBrZRUIq+L5eilIX2\nGTcjToxZWZ62KsECgMgh5LZrJmznZwRW1AxxxWFXVTgP04hlrR0T44ykGSlHDIPF0Ph/KSvoNWQ1\n0/AZxhWZAENe0t4UAjZR9zfYcaQT1lA5kl1kpU2P3M2XqeFMi5YAmLbzGoJrGKOO+wZTO2uxpYjU\n4bqMNiXajn1m7ND59ATf5Fq6zxy81+5eAwrnKMw7S8CenYG2/dQ50RgM3mM3tuhCFWZLSjCcnphx\nL+TzECsAuUVcZElTppKE5BvDhhBWpNg0p5qECoCUNAg3kOvZsF5tL28uMPV7qTw3BBv5Q+lM9ztD\n0kBy3mEcB9jW5TEqme28E56ptQaELKmx906aNwBJWcFaq7ZlfDPM6yJp2xoCGrM4JdXOuiwzLpdF\neLLGWljfeKtGOn4paOqOUoGk7dxHN2Bo3b8CadLE0SFFTQkLUuc0pBI31Fqi3bn/mEF/zNevH7vd\nrty+qShwgvK85nkRcJ33HtM0qU8c9CGLIYpnHYrWc4bR1w5gE6Db1I5+nEaRSwYKtk1bqyllmYyq\nldVsvwf0GtgxbHh6PKE9rFddCWpn+QcmqOs+aWyr9YBm+X2lBd7WhA6BbYg4pW51ti4M7lJgdITW\npg7w5u6GT6b8gVOt3o3UIY/1ePn6dV2Lv7et+oPfv//e35uTqpLw+x2eUgoua8S2xWugZtHvqLCT\n9txrOnv9WT98Vr0F/fWHo0s76v+rU7iWECpivJ9nPVjKAXtmKxCgBa2ungjQ9aXo9oLfO6Grf+kL\nKyBcj6fnYAAomfrm9iCpMUoRIHZOWet6MeHx+ZHriH9oYnSxrXUm/mfHDqjZZP8du2uG69StP3P5\nlNLuD31uvj8D8h8VeMf9/edSyh/XbXhRhHVze4N/9s//GYCaU3/7bZVY+at/9dd4eKw78C++/hP8\n5V/++3jzrj5khQrWc119v/vdJ/zm7/8BQJ3sn3/1FQDgV7/6BWJO+M0//D0A4Df/8I91dwDwF//k\nz/Hnv/oTnoyAv/m7v8Hf/k09xsPTCSnW73hzuMGv/vxXfLw/xcg7dsoR//P/9D/it795FnyTdV6n\n2ACSf5tWSW11HRJ8ijVWioxIuvM4a2GIsKz1+69xbQQAHA477FkTfnATYixYLtrOV0xKlugBlCRa\n8N7h4/1H/Bf//D/nvxUlFpestzdZrOsixW5nB0Q0ZdNVC81wUpOwzsIYUo9R0A8qtVZ7cQu9u4GR\nd8f9bi8qFaWz08ql4L/5H/4l3tz+Umgx27qKIF0putGkXBB7rFT34Bqiq2i7DWstK3xYeV0bKSVp\nHqSckPO1X2AS0Tp7VSiWFj0If/fb/xv/6X9W59ymDLRNNkYMXD8qxJrrXYQvwZfVxTQjKdMDrH1O\nWsQvjFB3GEBgP0tMgB3x4asq6fNP/+l/gn/yF1+3L4iNKU6X5xPOz/W+u7//jP/qv/4v8eX7G+Ru\nAdFtu6i/gbOY9hOGsZ7HMDrxPdw2Qkoc4RaDppRSaEPBps01grBWXDVyqK9LlQjfCOxEVjOiUkCt\nhpgSipGCOP67//a//z/wI8YLTSiAxKGf6fhC3nspZk5j9Q5szPMqK1K/9MPDPS7Maxv9iFt2wH3z\n5g4wWeQ1Hh4f8d3fVT2mx4cHbNuX9fNNvuqOpZjkIjjv8eZNdX25vb0TVn67cX759QeYZnywm3R3\nMEUAmFRyt+rXQrF0r8QPECipIIYGiMxYlw27lbt/aYFrErP7PfZs8jC4ASEknMcGxFvUfy9HYcjn\nEuWc62d3D2zRnauUIqH7fJnx8eMnmc93H95BwUiQ98xLwLff3ss1+/DhbeeYXPBDeJhCdZ8liVr6\n3bRb4IxKAl1h9URPSRfBUqiLT2qH7oryIR1e1a+6ot+018k5dUYRSdOOGGPlKUbFAYpMizXSbS7W\nArkZZFT+4Ynlu/fO4sDFZbJGFvjMZXpVLKCraBI9PqudcrbVeEHe42A5y0CyWuxOGUAW0VjnOwVd\nuo5pFWPWRWdZU04ZZORWst7BDR6eNzgYi8vSDF2jpJQxJfjW2TMJbrDS/a/CC+06R22YGQdDA3Ju\n3MQi0t3rssgC6p2V72cVWvlHxwttvvTxyTFK1yGmgG4+667cLl5OmNdaEzpfzuLosZ/2mLg9m0vC\nNFq8eVs7csebg3RjHh6e8MjR2/4wIoYsu6QxFg1x6b2XqMo5Lzdtu6C//vUvdDEyRXWmjZScGpBC\ndmxrtfVdBcc4tY0Zcas3xem0IoYgO7S1HiO3qm+OB4xTs7myNQ1u8sveQUNsC8Nk2VIySlCOXP1v\ngzKoiSYvIwCq4N7vfveN1BHevLtDQ8NWLfX6/m3Z8Pf/5ncyX3e3d5KWAxk/VB6gDhzZ/n2V2jUi\nbvfWvivUZtVZ26UdygfNMQKUIAlcd5zSHaxfsCx3oaWTSZomVV2uVo8KCNvWbXBRU3lYZNKFEV3L\nfd0C/vY3vwUA/PztHQZOyQeoRlmiglz67pyR7xBjQkzt2UgqXBgMYiER2/OuYOD3T35QC3pTicme\nIyBnVQ47d1WBmma333cig6KbrsMUkowwp4QYIwzfw+uacP9U4Qun04pWtspFI/8YVhwOe9zd1ZLQ\nOFq0So03SuoHGaxrwpldly7Lho3nIsQo2bX3DgMj+V147RK+jtfxOv4dHC+LsAgSpYSwYWFQWdg2\n2T0rv0rDz5Qizswtez49SMHcOVc90ACQSVw05pNyXqyzz+cZz881QhtGx1K8XdGS9xHnrMg3O2fU\nRIAX7w9f3EhEEpOK3RmjHabaGetqQ4akm5VKFheTYBMKdxytjbDWSJQ2eI8jm5Pe3d5gYHzVFhJy\nzOomMg3K3/JGOmNEBonlklPIAP1/0n0z0B01xiyYmG2LuP/0gOOe64YZ4gWJQiBW8FzXhM/39Vrs\n9vuq7tkMG6hcpXvaZa2/vUoXm4Jnyorf0Rq2bIMNJwXUVFKigaRRsgsRxrjOfUZ32wocVY6acPVs\nBcPKORVVBe3viZxShSw3zFfHDancv2a+6qWxY4xBCAG/+cdvAAC3+xGZalqfDUn3y4CArOeXC4mS\nybzMOF9qVrAsC2amd50fImI2Igs0DAP2rKzw9uYOBy70+2mEHxQUaoxiq0pJ4lOJHtfU6lMguXYF\nWhnovf+IqjrshWvLnz7N+HRfzzfGAj81ypPBzK85nxY8PwWczvW+P96MeHPH5347SVSXQsE8b/h8\nX9eGp/OM3DqGlhA4qkupiOqEG3983PTilLDdlSFsWNm1JK5bBxbkm6klWCnhcqkPybJc0BxNjKFq\nrwQATPDdNpZbQZaFIoYsBMzdfsL5PHc2UFHiXupQ0Sj5Ks8HqhNPRsu/jQJHSdUcLagWtjs5EjmP\nnEBohO4VjXdXJXJISLWDN+r+M02SpgIrVtoEAOitweHYZHQnOFmwSNDxFaxJ3bl+L2XrUq+UkqYU\nILmBiZykzduW0Box0zjJYqqHa7NWvvdb6rpZVzmJ5nAElWbh9GSavNZ4rP6cOuBt2AK8C1Jnikkl\nawxrTgGs/+UbiNR9L8WMYmhquoJ3ShFbMAoJIHu1ALbvPwyDXKf296H5CwxWICKErAtABkM++Fqt\nGx4f6kP/+PSEp+fKO12WGYFhFgPd1IWC4TEXLFhG5txeEi47dtO5OeC2AFTUdFRqiLmralIn49J1\nrW0jL+ckaqs1V2zNF4MSEpa5vufh/ozTM3/2fsK7t3Xh3B8HXJ5rnes0HvD0POPTd+wAtKwixXxz\nPDJYG1jmFff3Jzx8rq9b4oqBAbDjbpRGx2WOOLUG1HMHN/kj4zUlfB2v43X8ZMaLu4QkRgJR2qsx\nRhinDPvakGgdhIwtqAa1FAQpYWUBu5gdjIEI7xmnOus5ZpEl2T17bEstcgO1Q0e8x+SctYPT9Zhb\nuXIYBhR2EzHOIzcVU2jR3RTuhCbFVMmOXBTHsrki4mpEG9My2iQZNTSA0cgko8PzVNDjkb0Xb+6O\nsINGhxunEGRrOtY6bWR7dYCM9oVLzjUacZpWZtFItyKrcz4tAqrc7XYMVFReJv4AgK+ga4/33bnS\nJRpFBRSbS5IfnBTKnR+E4tKbtm5bgFtWwefFqEYHxhjRrffea4TlPXwXYaUcERm6YI2at6YYq0Fv\nz+trEa73GIeG2xtFIcLaCucdm7kJtIi/5aQCdMbBwooA4v2nB3HHeX4+YWZzjxiT2rPbWDu7wlsE\nQg22cEpAuDSr+wLrJpSNsxY47baW2KWBiuvTjrP2XxNIHGkK6b2Tc1VKaZ6ZcQsiWfP2/R7vP7QM\nweLAOvU7O2JbEp44KpqXhPuH+vPNMSHwSvJw/4zf/vajlCHevj/i7Rc1Yht3HoHvy9Ml47tP9eQ+\nfnrGjx0vJD8XqVudnp7xzGaRKQXc3LDcym6Ac+iE8rOaUXonufSaFukezNsAX9T5xDgnXZviCI/8\nOYfdgK+/+hrr0hyjPwtSe1037VrGKAjz1vEbhqkKEKGaOzROHVGR7q8tnF7lDt3Li2hNVeqvQyqY\nHdew+EFqsATrnDxkznt414wigphMAtXJ+HCsta5pt2unBrLAuGO4yODqzSkLFp8UAFBCgRoigFTr\nqdpjNU0uj8Bz9Pz8jJXleY1xKNnowntV5dDuXvv/9hh8f0nTVERBmXK5B5WNGccB3rX0WN1w1nWr\nfEde2LYtqFuQMcqs8F4WFe89fGeWm1JCjI3grfzIlAJCHKRGWY/X6kdeOri7cZQ0vqHHx9bNz0m+\nuzEk18LYuhidmBVxf/+AT2ziEUISO7dhsIJYP3oLYy1SMyqNwOXEGLFLBpvYoGDEtNswM7Si8m5b\n+ttdpdJBSFK3iLVgAar4mklRDlQMBucxsSv52zcBXC3Bu/cDpqney94msIcKRg9Yk2Xh3ALhmXXk\nnp5XlD3zPBPB2kGcvd/c7vDursFCsixY1hEuc33N4+OPxzW8jPycM1bOv5d1EWIlWSNFS++r3117\n3RYjUsNkZJ3wVHqt7RqNiCF3AyuiLnxt5/bO4+Z4wO1tLS7fPzwhsVBdLEnccaH4acE7+vGgeCsD\noRjUgnJ9jWV8kAL9jOwUJisdx2zKPPejw3SY0KoJfrSS2zvfWS45h2Ec4bjguj8exLJ82k0CQ4g5\naFS4m4BShAKRS5R2OVEVYgPqzj+HrdtNtbYRS8GFa40fP32sdT8A3g5IoTovA7WYWwSL0i1EqNij\nrDwgKc6HECRi9oNXEClHfs7ZribkpWZkjFeGgjXIKXZUp1Qlo/k7Sh2fNBK2tpLgjTRfCiCic1br\nhM4xjITPx2odbJwGTG3BmkZMrYZlbI0cW02MVLvcGYW25JLxfL7gM2uknS4XgbrkUrDjY79//xa3\nb2qUcjMOsIYQeX6WJeCBi92nxxXn5zoH5/MZ/vkoiw1M7nw+tWxYCqQm3NyTC6nWfa1ZtetYmwT1\n8loY63E48vVCEHnmaSD4VncspAIHPsN4SKidMrAyHOH5PGNs9nOjw/4wCbXLeyvwk1IiHGdOu8Fj\n15oP449fsF5rWK/jdbyOn8x4sR5WC4E/f/6Mx2dOCQswcbSQM/Dtd/cCZfDDKEKWiEVC16xKH8i5\nIGeSbhaRkV2tqpmynLG3OBz3Uvtxg0Np3aYUxCihkEqltE1qHHeaspBqalEXKtf6QoEVcwMjAFZK\nWbwWp5QRGrjTWPZh4zTOXpNTW5ex0nzUM86P6hlHxnSpljLEnXGcinYoZ5JXSffpcj5j3VasoaZ7\nGRFkG9I44/7+AUCVud3t2F04BUkb20QJ7eJqMBSg47xp3NNzEQlNXkW7fB3Y06kETJUl0b1yXVcY\nroeWkqUeVb9ri7qLpHYEgqGKSpfPE/qRpviGCJaMOOIY5ogDtQs4dnparZ7VIsJWxnB+QBTdN5Lr\nt6aIz08nfOS61RoByymvRcIt66N9/cu38EOrRdV7Y+Pr5HcWw8Qdub3HN8RQgJNBAjAeGtS9IFMz\nqDCIPL8hAomjptSuT9Got15T06YPaAa1VMsWDUpj/QQq+jw0bjaZztTWb6DBwHKti4ISxkNcJNLb\nTR7u/UFS2P1+B8fRVy6l1oJRO6BNPrydx48ZL5RILpqzP3zGyjCEuzfv8fbD+/oSIjw+PAq7//aG\nRD45lyIt5pxIF69Uw9s2zb0paikRqbm5mFItj8TS3koaFFJAaBbv1B2Mh/eD6F6X6rcDAFcPqSVz\nhR4upRMZJIJvcIViQKx/PsUN4zTKseO6SHg8DaOy3U1FIIfWtEgdx4yM2q47L5SbknK1V+IbyDsN\n60Faw1+XgBKLyCL3tIltTvj2m3v+rh4fvrjj65SRKUgjAh0SGkUlpqWdLiTwCuOov9SmQikdNaWh\n27uU0Hun9SjnZBGuGDZC21lSTrJgoWjDAaV0tSjWmeoI9pL6XKWONX3UzyLRbfPOSp1l9F5+buer\n9AfTLbpGqGmX84Ln80kUPEIs4j1w3B/w5m2d53H0MLwCpMCcw7YOkRHoQtoDh2M9zyU4kHPSvKgY\nt3ZxFEN4BT4RbfcAUZooei1KUepPRq3Ttr8N3koKl2K8UgEpPOe55ErTao2xVNDMdclkUZ3YDQ40\neunl+NEp15ObawBA2cCZa/rcjxmvKeHreB2v4yczXqw4OrOF+bJuGJk8+dVXP8OOeYHzecbnz58F\n0T76qaZ74JZr15ItHdO9ryb2PmeAmnEWJidLQdgZKJctdooHitkWtU1LKBISl+sArLmHgPtjLYwm\nkijIoMAI099J8XxNHoYIZzZpDcuFEdW1SdB2Rj84uGiE75dSFoiBIaeQBKNt6Vwqv69QQ1MniXSA\nvhtWADKqMWWKpNQhJDwwoNENE969/8DHmhFLQGm3AHVyKaRpHQQBzxFW0fSwes51M9nkbfhI4+TF\nq2+cFE1ORAJrSCkihojI8xLWTeAypRSkyFFUTho5Gar8OknlCYZTR0NFYSqchheoKmeLoK6AqB1k\nQhQgfDuIdrxjMnIvn84zTs9nbE1riwjEx7h9e8TdmzueEifdXNiaurXzMYbgORXdHQbs9uzK9JwB\nQ8pw6EjM1MFK+uaSuPyUgNaAqETodh21GUKWAIpQTKk2uUxOEgEao6CXmApihDShrI1oNNTdZLFj\n38Td6EAJIM6KBue1REBqpBxKEYPkP6px1I2X1bByxhM7owAGX3yo8hcf+L8AcP/pHvf39xjZqHQc\ndzgxUj2nrDlxx+XoNaiBmjI0vIux6uJRcoI1RhQG+i6SH0ZFPpciahGFUzBrLVTBgHBFNZFSgSoy\n1OOrWkAuGUZa2hGUW23LIsUEx5TziuZui6TByHWNhIJ1SyDO71KslBoA2LbuIUORTl7YEisRtA5i\n7GpYVvTDYswwUH17QpVtAYBPn+7xzTf/CAB4//4LTGys+fT8jJhjd+8bgFodo1/Oa7qmDUQ9H2T9\nzPq+9hOj6adRaj7jMMqiEGMWyZdtXbEuKxbeCNd1FYJ8yRmppWglXxHHc3HyOWQMFAlnRCnADxY+\nWEmXeqya7+g4fvDiPmNNpZWpIwwpmTpnMZu9zBvmNUhnlSww7Oo53Nyq1VZIEa1u4a1HKhm53WeF\n4G2DWRiMY3sOVhhykmLWmi7fhygdvD3LglRy/9C3xatjbZB2zEEJuSRkxlFkUgFO6zr3KUuyQF+W\nhMslCj2t5A3jvr5uPxo40zr5pW7YjRqWingNGKuSNJUlpaT+HzteU8LX8Tpex09mvCjCijFhY6zJ\nm7u3+OKL6qu32+3Fpvz56RHLZcbk2cARSiC2UOvrXjmx734BLPkivoQd4C3jOnwsQItmWuLWjl2+\nZ3/ddtbvjz6acMyb6+VRtOhIXQHeo/mhkzVIY1ZdI3Kyo1rrZZfMsFjWjMvC0VLMmGd2ILKLWMsb\nVzRyWiPHNw3FrSlYSupyU2WpJ939kpJlL5cTMov5HW8nGO6ebjFUgbZWTO+kYICuaVEymHbNn5Wl\nI1TjrbbdZ428+LWHw16Bo8MghPYYla2wbWsV9+MSwrauCNzMQSkgRi4W7+S4hoiNT1oRX6WCc7aS\nQhXUjqMV1DpJlOadFXKx9/qzNTXVFiK+VSJ9SUU6xesakZISy40pcEOL7KgyEcCaba0sYAgpFfX9\nyxmmkanJwTUz4lJgjBHcmrNq2FI6RgGVDHQuSABAZLXUAtIeTSlgyUF204GmgXYQrbiUIR3JGDJO\n5yajtGG5RGQuY0wj4YbNdQ87i7HJOxNAnSxyTzctqN6f9fcGMbVONn70eGFKWOBcXYjev/0Cdzdv\n+A9FahApBjhDmIT9TvC2KTuSpGilZJl4a1uezqkUGThZNaCMkYqU0/C3FAmJ62u0eyLUIGq2RE5y\nll6srvDNAf50c7VgQVdLZEEoJ8owqS2oFhsFOLHVUiiENR7eM/ETBuOYMA6MzN9WbEv9+TE9Y57b\n+4uSwGOlMjVFBDKEkhqxtYhlGkAYx0muQc5FHiTvDL78WbUq++Wf/kycWfK3hUNyrUfJuPq5VPxJ\nZyCqfy4CRWn1tjZXAHDY74TwbN2A3NyyUxTi/Hy5YFkWbIzAD+sm36OHLhCyghGdwegdJnZrHpzT\nRaUU+NBQ7wU5biIYCmgLfRgcBlFrUG2mei/ovVhyRiMQWLLYliYLvlamQEtZKWKwqiaRpUBkhFVR\nbC1RtPdQ0XqpM1as2QzqM9PArM5abcwWZRTkTqVCICpFN5eWztcfdaMhMgzFadSSDBY+xRoDFp7/\nZS04VVAATs8BOUHAnrdvHD68q9/jdjfAt+ckhatSChltuKIDZa8JmFeGRayveliv43W8jn8Hx4u7\nhKNjMuR0kMhnXRacmO9nQRgHj8Qh/+V0Ug0oIpG7qCut4pAqgbjIMaRAmLvdPxsgq134FRClCz37\n7kkb1trf48G1c5Kfu3e3X+jfCaaB9EhdQUzJVdJDsDqd1rhxkgZ5T5jGEdPUbNyLuJacw4rLuYIG\nDUWZO8sqm7ZpyFJA7qSPG5eTAIzDDucTk2dnYGLQ4VdffYnbN5XK9P79W5yZvGqNrTLPEr1qEwTd\nLl4nAUJGr+fOUTJUXqVGve01rYOgf0/ripVleB8fHvDABqyPD884ny5Y2d8wxyQFdGdth93ywsmz\nPN+uEaudarLX79aiI4+wDfJv6tKs/X4nINph8B3Gi7ijVo+VU4SQ/KDUtLgG7nprN6w1Xoyxgl9z\nxkpBu5R6P7WMo3RWRZStdFSdtZyltOMZSedKyWghW9XGF/R1PQ4ZzWK6lD13/qCAQSmdVb2JCPz+\np2XF0yNzdZ8TtoWL7ucASwa3hxr1ffnO4t2b+rfdZKRJVlJE6ZoEhUgi41gyNj63y5KFOB7mH58T\nvjglbDfd/adHPDxWBHWIAfPMAmAh1G7iqda0UAjvGFTqnEFZ2oIVkHMT8MRlvQAAGkBJREFUuK9d\nFlkwTNbOTindRNdko+XoraoCtPC6HQDdpZFfKYCN9Ea50r/qXgu0bLAD6QnoTc+v3jCqv22tdjGt\nq2kJUFPS3X6HrbV4rQH4IV0um2rQlyA8NrJVyDBrPtCVlgo+fvwoP3u/E8hEiSSp8s3xiNvbvXzm\nsra0irAtC0q+0VmUNFk7N3Xj+J5DjdQkSGo6LkFSjpYdrGETYb7lvOH0VPOLTx/v8eljXbDOpwvW\nJUn9DSgK6ByV7zeNQ02N0B7groTQGfcq3h5IzmP0TjZMN4yYWDDveDxiz0KLRNcKHQSCaVpUhaDO\nHwaxU5VANoI0p3HAyHVb7wcBS1s7CIyhxAgqRRawDAAs1pgRFcRcMhx1ZZXuPqTSMRL6n+Vp6J6W\nTk2jQoK0nJBSgpcbOlWpagCXy4xPrGV1eorIoS6aKWYMg3IuD3uHPXdFHTbRtivFIMYEgwYf0Tpa\nKhkr12rnDbisTZ3ix+thvVDAj3A614fsPC8gwwRRQ1I8rDceCRv/6XzCdKxUmrfv3+Ky8UO6XfB8\nabSGGdbvZPIT58Ft3LLF/PHmFiAjTHjKwMARjLNObqxpHNDqKG0XMURX0VJfrJdfcxu4LQsZ+UrH\nv68MtHMlAsMBGsq8SPQFsBchv24yHsXUB8Y5I4VOS4SN8/iSCJaY6W8HWGuQcitCR/WMS1HOiGxt\nj8tDB3UkBpIWZLsH21qDyNby8t36hbv73lWItd2Q3csoa4Eia7u9NSpSUsOQZZ1xudQHYZ5nwVqF\nrSprtM3GOSfF9N00yQIzTgo9cK4qIEiEZYwsZl1lpkYqzgmeaRxHcRaaOnHFPlKpCzXBmfbAeakz\n5RSrAzjqJkFwQtR2GDG5ugB6M8hC7ZClnlOyshuAek8medCTtP9LzJXiJbUunfSSu6iqqwMXoQ/p\ngkXQzd4QBOpDlADKcg/bBDg+5ME7vL1j9yWTsM71PfM5IsQNJ/73d49GnHZuRmDwbfOwMNnCG/Yy\nKJaxYajBAd+Xz8uGJ84q5O8/YrzWsF7H63gdP5nxogir5vecj+bYNdBKJyQGGGtRTNvtIURca0j0\nrK1XAbZtDcipdHLEvVZ1rQsBikhudQjvB5Ry4WMsykEjDuUBWdEd6drcO49cR1vpiqVVoNZV3x9q\nsFq49tZel1CE7Z2lLmcsqs56q3NYA8u70uAdtpXrVCnCUYsah8o/jM0azYvOl3WE9+////aubUeO\nHLmeIJmXqupSV3dLszNYAzZswMC++v+/wU8G/GbAgMfenVG3+lZVmUyS4QeSESxZg1EbMNYNZACS\nSn2pymRmBuNy4pxDWXMLPyd8eSwpul9A2Jb1i2gT4wrvMNbAL402IjHom+f6VbrMTYGH0dAi44Kg\nEMgRnbTOyQqIdBw3uLoquztN8NOi4EIiibCGXnmqht5hKBHW2Gfyvfr/vutgS1udE4MF7nE5D3ep\n3EwyJEwwcH2lxqkpvMIc6jmEEIQSKcUACyMdxM7q+RFZuU6cGrruGGGYEetABpHQ9kRE5WFDzFTj\nlfK66YwzGHW0NhkSzb8gw8smR1DIV71GrhYaaRrLcB0EcjOQaijwRjOYDwfgfMxv8OXXCfcPRzyV\nqOiUGL7wWf10M+JwqGDhDGHhOb9f9BGpK8feG8wl83o5z/DlOg2bDid8n72x6M6Yw1QWhuGcQhd8\nrC31BENaKTdktbVt7QXZf01V/NkjuiTpSmgksmMMyhhZtN/qc2GtlQu5+EV4wjmpQu+FGvJvjABQ\nU7v56owvUtMLtedGfiqrP2uKyd9IrWxHMNaJPH0/dhhDvmHCzsMvm/qR6KoIZczDQrUOxkjKnd8R\nPv14U47L4f7XF8SSlp79jJta9kosqWcru2WMwTIHSUls02BIST+nrks9v3b4OaeRFaqQVP27Nhqc\nlQcmDD1CSe8IFn2Bx5z6M04vZ2G/JEBqgOPYSwo3jJ2IjGzGAZtxEAK+vtOieQwBgZrja1r73MqL\npaRjV+ZrzcMknO7MCbE8JhE6tM1xAcWEfTm+znpwKumiIbgCZ0FKqO0ejgzrrEw4sOlEiGHyM+YK\nV3AJ/caJA2RW0Q4mklphAGMp5xDKWBo1kB2T0Og6cpMqGhjjGihOJ+flTBC2VTdYjGXz4NnidFxw\nPpcU/zUCpaKwpRFXpcljdhbEJBz7MTigq/i7gOeXQgD6cpLNbrMb8QXfZ2tKuNpqq70be1OEZZ3F\nx08ZhDgMvTAFBr/g6TkzL55PEwCS4eDdbo+bm/w7xhGej7nQ7hePqfBe+7Bgvxml/ezDhLl0Y4Zh\nwG6nzJzG2tq6ygVgaaApXc2yBNg6h1X3lRTl96j5+zIiMgCUeZOgEUmOtsrLtmtDBhk8WQukGsml\nlHS3K5JVIocGK2lI6jsMIvwKmbeMPsMOjNXUQACSKWHcltTYbXA6zZJiLovK0wOQaCkllgkCZ3rM\nftG5NNM2JSJaYG1rmQmmNiW+BpHWV/nFdrNF7Eqqxw4GNb1bsBlyNNK7Iywc/OTlfSosZBx7gR4M\nQyf322YzYLPdCGVy3ytdsvfamSQyBXpRziolAduGoHNx1ukgdf49CCeabaKvHEkXGIN1AKx0xyhG\nzb8oD6qXD5X00g2ZDqkiu1Mj6BtTVOWgsMAYVm0ANrg4QF1oZV0V3haWckgibiJjSHSfUualqxJ4\ngTXazLCL2kl2cFwoc0bGZuzRlTWbJ0JRAMPLkXEq0da4JESagKLx4EyPWM7jeJzwVPjbp2nGtqjp\nHG63+E98n73JYY3jgD/96R/L640s4svzM/jn/DPMhBgTxu2uHMwdfvrpJwB56v1LgUI8fFHZ+hij\nhP5AHlvxFYvktL4AApbohYcrS29lM8bIwxVCAErtQ+BcETKuQ2QFy2XA0nb9XiNo+mS4TLrLkDQu\n+LUlBStOSBpsRFoDbLjLI1g4ryoXuxMeo9i8Q9RxGcNwvUMoafl5PgnpYYaLaHJUMV3OjniZz1+X\nqMRaR8V8yX7BknI0D0Smbiiv87/bcSdpojUDOldwNz7CT3XcqEOKgHVTOa3GYW0uHVZlNhg3A4ZN\n47Bc5dkoKP9G7iwllhEmJgJKZ9u5BdYWdoSkCt8t00M9F8VUGQwlTdv0PcLMWKqs+7Lo8DkBIgPH\nCXXoPjOsqxCLMVY23JhCI8SxqL5nPg29TgylOm5G1VpIj2zJze8ZWJnAAHK3rlZsDTWjSJ1F1ewi\nw0CpRfcdo+9Ih/ytFXaUORKmIk13nhf0XRJIBjGEm/7h8wueK3MIATeH/F63d5Xr//dtTQlXW221\nd2NvirCcc7gpTIpEVjivmCGv/bKAQZLGdEOng6VM+LDPQMXn52ccX5Vu2blOCqLPTy8S1t/cHIS9\nEcTwywxh6WHIrsFgHGsH4zRhKA2BoS+UwGgidroMLBrtBbSvUkZQAsg7rgoz6N8wtnSf6uC1ggk5\nNcV5EDhpuhSRLj5YklIyWFB3qwlg1uisoXLJeUdpTAQP6yADt36ZZPfrjG1SvYa10xksTZeQ8FWY\neZHe/UYH8XeoIp11GkWyg0GhITYBxDlK9sOCYRga6mwV0XV9D1epi7sOrkShdZpAJgqsRteZ16t2\nsjkP7Jc0xiRWeqiSLgKZDbUW+qX43gAy6ylYVkGQ3nZgWjDXYni0WtBnjaJgScVR0lKm+SsvmAJ0\ng2f4MlsXYqZLqk2G9n4zF5Esi7S5oNvba8KQDim3ZYySitYOeOAobDBkktw7lKwMODiT4IxpZhe1\nq5+IhZ58CYS+N0JRPvszHl9zJvX45Rlc1uv2sMPHm5IS7r8/bnojRXJCqFqEMeFc6g7H16PUA4zJ\n+Xblrf7y5TPuHzJv9fXhGje3uZ71/HrEw31GO//lL3/B/f2D5ON+8sKd9OMff8RdqZtZSziejjif\n8wIYq7p1gBFOpS8PT4gl5dgV0GqATpcQf/Wg1ZpVRQZ/o5vYSqjHlC6m0Q0cqk5SAulEelPlYU5I\nSVkUErR8RFXMEXmgtXJF5ZSZhOiPDEmL1JARlRoCsBsH7Pe5C/fy8kWgFQZW+LWIFOA7DjZLfBUn\n59woKWVZhHrmGbrRUPTWJ5ignd4Yo6ovh+rw9eGypgMXEG0KhKUwE5Bx+Y/VNK5iBchYHRQ26mCS\n/Kn1H237h8gIDQ31vCxSDwUZuFC7z0k2xSwLr93rFurSLAgYLADVzjksCHJPWWPkQQ9hETk365Q9\ngWEK1KNsQOSQqibBBPhzqc2G3EWtBJkwVKnK8mlwc78KhrSuxbfl2hiQjnDkhI4VxEPEcs/SRUdS\n3TU5AK7hagsRKCSHUwyYfeXE32Az9tIJvX864Zf7nAaepgkfbzMU5+OnHbbXhe1h/D9yWMyMc6k7\ncVIKmM2wwd1tHr9JyHUnufmbB73rBlwP+SJ8+nSWHP3x8RHH15OwZG7GER/L+x0OB2EzneYTzvMk\nNayUFJUeY8LjYx4H+vk/fsZyl51cnZon6/QWJMrUHMAFawSAi/Y2Nw8qN7OPpnl4LBP88iqjNdb1\ngvIPMV1AOlKIOL7mKPA8e3FS1o3oClvA6/EVz2WEJc8X6lyfAgrqaWidxVnCZsw3zfHlrEIOm16L\nrUjCzGltxuPUWlfLYEENZq3O/rfFdd3Em9DwG1HY+TRDHbYV1gK/BIl6lhCwpIgg0UkSTx5jFJ56\nD5WFA1GJqmp9zEj0OU0TpllZcb1fcnMB5fGt7xcjfMF+dV24gNsA1LAwaMGbCRLljdsRx5cz6gJE\nDiKe+no84sOHvEn3rpOak+1GMOl4CkeD+ZQX5fV5wXSqGC+HfhhhS5aQC2vFibcabqnZfGuE2syB\nMrTmc6FLKE2r0qRKyuFvm+9bKAYtE/5xjhKBLMpXSCznJSKVOus45AbIqTCR/PnXJzy95OuxP3zA\n3V2ZWtlZVMan3yijftPWGtZqq632buzNbA0ym9T1IgR6/QH44cdM5methe0M5jIzuCwL9ru824zj\nKN707u4Ou11O1+4+fsTry1EGRnebDfble1dXW0HHvxwDTk1KGKN2CXP0lz/z/vOjsFrXHZFICfjq\nwHL9vVY5Jw9eV3Q2KTygqTcwkXA7wVgQOVib8/Gu2yjq2vRgIa0DzkePp8ccPT0+v8r7GeNkGsBP\nXhgBUggAQZDLuIh1WFNZMqBE2JQU4vX5hFTn35g02mqiEj8v8NMibf6Ukg53U8upZMpaKQODzAyS\n7sytaGdFbz88PDTHa8FVUHeJmOd8TMfTGeezUiQzK4CYkQTJndV18tfnZcYSZuGzss15TdMktdHz\n+QzvvXScY2KJlrwPmMvwbdf5RuTVlIywcuwbuTaUDPoCJdntd3h6fkUovF7eT+AC7aGBYCtR4G6Q\nTISoz2K4odBBn894fsrH+vx0xDzV89nBdZ0wllDDtx8b1RywBlsK79Gud2peX0QmbBGDxanK2yMK\nPfPoHDphlyDEWKMoxjRHEUUdOysh22a0GMcK6wlYFsLjc17zXx5moZW+227ztAcAHwjTq37+99ob\nR3MMhk0lFeux2+VUzTqncB5LsJ3DUlHcy4JhyD9nyEmdYzOMIod1d3uLeVLxgb7rNCePQdrSKWZi\nunHM7/fxdoDfVxktKxey75wMekqhEfpAZSqbmqoBFUPFiDCGoNz4rOyNfImSrg9frdOonFUnDJcw\nRrjVGQvOi5d05XQ6adMiaYrZW71hbK/tepSz+KZRfg774th9iJhKfXG/3wnSHawTBNN5wsvLURke\nWlYMIoh+GpWuepMGSn8hqTRYItXOqynow/2jHHNmwsy/GKPCAc7HCafzJC195oSKSogpaF3MGFWZ\nni1m3xU4Q65t1qLiPM84lQ3tfD5jXhqHFRvSHAqwtjqsIFMSNSUUeSxSzchkGENxWPubK+xejzhW\nIsJlFpgOPwCuFNaxEDpTudk9Inucy9D5dI44vRQs4ryUWmhWpd6MDq6TwlKTBZLcU20LRq+Djvvk\nIkbSH+DKpmGwLMCp1JlDjNhf5aBi2KujTDEhFGa/8xQwzRFUarWWEpjymm13o2AlUzJ4+HzCr79k\nR+wXJ6N1p2nBUgIZ0/VYynu/hXF0TQlXW221d2NvirCICFf77InJGAXEcdBCXiKE2Wv3yVhM56qC\nMl3wWdddMYUAJEgHJsag6OTo8flzARVyws2HA66vSkE9qfpsZGT5IhSwae3SVOFHUi5tbruE1AiI\nInOcC9CSWpkv0lTRGImwYmBYk7tu+XN6SQm7wckKk7FwQyc79M4PcE4L0pUjfug6DFV+yjmAoQO3\nTJIGMSegQbOnlNCViNV7j8en3IC4vTtIYGaNkXMnWPhpkULxNSVIpBmbrqBRFe5yEST9IqMAXOYo\ncI6urNOXx6fm16yEAcwaJUzzjPN0lqYFmkZHDFEiH2p4/60ldGerhH4Ned+yLJjrBIX38D5gCbWT\nmZoeUMxdUuSOl2sjLNbuGAMyQM8gLHX4fHC4vt7jXEHML4/yvel0xuf7ewDA6fmEoauZyIDAU1bS\nAeBnRiiF9sUzespRymbTYxwtGFXJ2ymhYFKa4bZRovljTeHLtaqvmOXcORnMweO+gDjnGMGFO38/\n7iWFXWaPEqzi+SXi6WmWRoXtCdvC6X5zvZEh9dNxwX/9+Yxf7kv0hgFcyiePTyeEsl623yDEUi6J\nzfP4O/ZGPqx2CdLlYoSaWtQUoOZVhNPk5ee4Wez6Bq5zF3xVxlhJCzabrVwsApXRl/z/0DishCS1\nBuMMbKfhr3xePXJuRk8SGv6qLO7QDjYbwULYr9Df5ViJMfYG+8LEGLYqgrDddejGWgsBRuqxR3b4\nzpG03ympAjCgjjuPohCk4RqDII1htJWd/YQyVgYfMRWBixijpBbZyZUaRD/i8OGAQSYCLge6kwgd\n5PSIRIWYm/yw7UhFbd8Xh/ZSup35i6rAzE1NxvuA2c8CMWAtMyGGqAISpNeJSNlYgcvObi4hVHhB\nKPCFsk6xYRVhRsUKhJBgK8yCDC66n2yEzA9wigjvDa4OO9zGgifjGS+n7AD8EnB8KSMo5DH0tT6X\nWRkEIBJIuLasGbHd5Rrk7e0OHw6jpoTgZnPii7ptrfuiOEtnpGGIROYC4lHvq8ABx3PE8VhqaUtA\n32cHsxk8tmN2nPNk8fyUf+b+YcZ5TsJdtdv3+PFTvpc/ftgJ3Ob+yeOXp4gXX4KHoVP8YAhAcVIJ\nDrFskLGyrHyHrSnhaqut9m7szRGWzFgRKXULjDIOK9ip/KNyTAyNXtC8ttZmnu4KErQapbi28Mw5\n5TQ1HSMIR3xsirXGWUkPxSezphQtMjpTfCnWKv/RXQmkO7wet1EAJ4Cht0i7IudFvXQ9+tGhGWuD\n64zsoq6zgk6mqMdD1sjQq7V5LvPXXzL5xv5qj3HQ86rNCGNMKUrXdTZKqwwDW1DHiw9wpQB8e3OL\nP/7NLKybLVdUaiikKZkLABiDNP2oF0UW5vJrtRMI5ABBabSUFTSmlIG4DXZII+ioFFvEaFOdFtrZ\n0v0wc6OCzJk3vSFWkeNhbsC/7Rh3gnMWY8G0ERuYEhVQhBSak0noB4Pb29zNdn3C/ZeCK3x+xbzU\nYwCOlQd+iXDWwZaCvIlROL2ub29wfZ1Blbc/XOH6MGIsKb5jFs51SipqSwywRJM6PC5rERNiAXCm\nlMBFhoysBRmg62rHLuHpKafvforoCvVPCoxjwVC9vk4wSPhwyN/7w487/FDO/WrscS7NpDkwTiEg\n1nskBKAAajnMsFwirOhRqaNTamSNfsfeXMPqSwqR79s6cmOlMyE1jcrJDotx2zq5mvaZ/+G82pRE\n77HmRk0GzEbQxdw6LCSdbndWUpr64EUmgS8YA031GuMGTCrHJd9rboSUlNLXOmw3o3JWGUIoaarr\nnKDRA2epddHCs70C6kOSsDkZiBI1EWGePf71X/4NAHC42ePv/+HvAAC3H68zLTSAUB4+V27w/fUB\nfeGKMp2KbxAl8TXb3Yg//PQDbLmJlxSVlzslTTU4wViSehEjisw8jJMVisziyIZNcd5R17EpWeYB\ncXFemc5ZfGBTNkjN6zb9RK3fXfCO1RSdlLu/uDWqTBpEmlZ+RUTYqg93XYfD4QOAzM1VJbRSWMCV\nCttlvcIK7bGuQ1e64ZvdEaeSkp8nL3WuZYkY7Cjc72PX47rAd7a7O2w32WHtrjZwndaimC952uuG\nwNzASuq3k47cpBTRakVyqsh+4HA1YDNUsHIv4iSLf0X0FTakTCE3Nz363uJDoU++uXW4GktnmwJC\n5bbqDa6vRri+pu8Wrt4HnnR/M41OQDLfTeC3poSrrbbau7E3R1h1F0cTIRkYKOxJeXmAMqxptLtm\nmoFVK90XXOysseHniSlIYbLyUEkR3lhQVXeBpqiGjPy+jOYQyTHFFJpBYhWNaHfifF5NQbedmCYt\n4scQkFIS9tWUooBVmTSdNUxIIBFOgAGodNUCh5yGIXcGazrLKWGeFtx/zsXcq6sriRjzadeFyPJJ\ndZzk5vZGmSznGZXqZPZnoe5FCVCOvhS7X6AD0A1ei0OmexmLAGnwHq+F/IjhIEosxLg+5CKsLVxI\n7ax2Sm3kpBFWXidozt02NtAoS7fRVhWNaKKvGukxGbnf2mQwf6G9hnTxWS2oyVojgqsg7cYae4l9\nipHBNctwPbbbcm+7Htt9oV5ZvIwhkekwmAGDKSylzmJTmEmt28JU7Bbl86uziQx70RlUOvLUvG5T\n6vqzytGVo9MSxS8ThgHYlEbRh12PyefocFmi8nUlI6ykfT+g6y1KUI+hiyjTZHAUMNr83tdXPdgY\nLLFi5Ay6ygkd59wRL2shRQvD+Pnf8V1Gv0Ub/M0fJvoVwHe+9f8r+ycA//zXPoj/pb3XY3+vxw28\n32N/r8cNAH/LzJ9+74fe5LBWW2211f6attawVltttXdjq8NabbXV3o2tDmu11VZ7N7Y6rNVWW+3d\n2OqwVltttXdjq8NabbXV3o2tDmu11VZ7N7Y6rNVWW+3d2OqwVltttXdj/w0ucjD/jGqWZwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26746a730b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = np.random.randint(0, trainset['X'].shape[3], size=36)\n",
    "fig, axes = plt.subplots(6, 6, sharex=True, sharey=True, figsize=(5,5),)\n",
    "for ii, ax in zip(idx, axes.flatten()):\n",
    "    ax.imshow(trainset['X'][:,:,:,ii], aspect='equal')\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax.yaxis.set_visible(False)\n",
    "plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we need to do a bit of preprocessing and getting the images into a form where we can pass batches to the network. First off, we need to rescale the images to a range of -1 to 1, since the output of our generator is also in that range. We also have a set of test and validation images which could be used if we're trying to identify the numbers in the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def scale(x, feature_range=(-1, 1)):\n",
    "    # scale to (0, 1)\n",
    "    x = ((x - x.min())/(255 - x.min()))\n",
    "    \n",
    "    # scale to feature_range\n",
    "    min, max = feature_range\n",
    "    x = x * (max - min) + min\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Dataset:\n",
    "    def __init__(self, train, test, val_frac=0.5, shuffle=False, scale_func=None):\n",
    "        split_idx = int(len(test['y'])*(1 - val_frac))  #分割点\n",
    "        self.test_x, self.valid_x = test['X'][:,:,:,:split_idx], test['X'][:,:,:,split_idx:]\n",
    "        self.test_y, self.valid_y = test['y'][:split_idx], test['y'][split_idx:]\n",
    "        self.train_x, self.train_y = train['X'], train['y']\n",
    "        \n",
    "        self.train_x = np.rollaxis(self.train_x, 3)\n",
    "        self.valid_x = np.rollaxis(self.valid_x, 3)\n",
    "        self.test_x = np.rollaxis(self.test_x, 3)\n",
    "        \n",
    "        if scale_func is None:\n",
    "            self.scaler = scale\n",
    "        else:\n",
    "            self.scaler = scale_func\n",
    "        self.shuffle = shuffle\n",
    "        \n",
    "    def batches(self, batch_size):\n",
    "        if self.shuffle:\n",
    "            idx = np.arange(len(dataset.train_x))\n",
    "            np.random.shuffle(idx)\n",
    "            self.train_x = self.train_x[idx]\n",
    "            self.train_y = self.train_y[idx]\n",
    "        \n",
    "        n_batches = len(self.train_y)//batch_size\n",
    "        for ii in range(0, len(self.train_y), batch_size):\n",
    "            x = self.train_x[ii:ii+batch_size]\n",
    "            y = self.train_y[ii:ii+batch_size]\n",
    "            \n",
    "            yield self.scaler(x), y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Inputs\n",
    "\n",
    "Here, just creating some placeholders like normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_inputs(real_dim, z_dim):\n",
    "    inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='input_real')\n",
    "    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n",
    "    \n",
    "    return inputs_real, inputs_z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generator\n",
    "\n",
    "Here you'll build the generator network. The input will be our noise vector `z` as before. Also as before, the output will be a $tanh$ output, but this time with size 32x32 which is the size of our SVHN images.\n",
    "\n",
    "What's new here is we'll use convolutional layers to create our new images. The first layer is a fully connected layer which is reshaped into a deep and narrow layer, something like 4x4x1024 as in the original DCGAN paper. Then we use batch normalization and a leaky ReLU activation. Next is a transposed convolution where typically you'd halve the depth and double the width and height of the previous layer. Again, we use batch normalization and leaky ReLU. For each of these layers, the general scheme is convolution > batch norm > leaky ReLU.\n",
    "\n",
    "You keep stacking layers up like this until you get the final transposed convolution layer with shape 32x32x3. Below is the archicture used in the original DCGAN paper:\n",
    "\n",
    "![DCGAN Generator](assets/dcgan.png)\n",
    "\n",
    "Note that the final layer here is 64x64x3, while for our SVHN dataset, we only want it to be 32x32x3. \n",
    "\n",
    ">**Exercise:** Build the transposed convolutional network for the generator in the function below. Be sure to use leaky ReLUs on all the layers except for the last tanh layer, as well as batch normalization on all the transposed convolutional layers except the last one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator(z, output_dim, reuse=False, alpha=0.2, training=True):\n",
    "    with tf.variable_scope('generator', reuse=reuse):\n",
    "        # First fully connected layer\n",
    "        x1 = tf.layers.dense(z,4*4*512)\n",
    "        x1 = tf.reshape(x1,(-1,4,4,512))\n",
    "        x1 = tf.layers.batch_normalization(x1,training = training)\n",
    "        x1 = tf.maximum(alpha*x1,x1)\n",
    "        #4*4*512\n",
    "        \n",
    "        x2 = tf.layers.conv2d_transpose(x1,256,5,strides=2,padding='same')\n",
    "        x2 = tf.layers.batch_normalization(x2,training = training)\n",
    "        x2 = tf.maximum(alpha*x2,x2)\n",
    "        #8*8*256\n",
    "        \n",
    "        x3 = tf.layers.conv2d_transpose(x2,128,5,strides=2,padding='same')\n",
    "        x3 = tf.layers.batch_normalization(x3,training = training)\n",
    "        x3 = tf.maximum(alpha*x3,x3)\n",
    "        #16*16*128\n",
    "        \n",
    "        # Output layer, 32x32x3\n",
    "        logits = tf.layers.conv2d_transpose(x3,output_dim,5,strides=2,padding='same')\n",
    "        #32*32*3\n",
    "        \n",
    "        out = tf.tanh(logits)\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discriminator\n",
    "\n",
    "Here you'll build the discriminator. This is basically just a convolutional classifier like you've build before. The input to the discriminator are 32x32x3 tensors/images. You'll want a few convolutional layers, then a fully connected layer for the output. As before, we want a sigmoid output, and you'll need to return the logits as well. For the depths of the convolutional layers I suggest starting with 16, 32, 64 filters in the first layer, then double the depth as you add layers. Note that in the DCGAN paper, they did all the downsampling using only strided convolutional layers with no maxpool layers.\n",
    "\n",
    "You'll also want to use batch normalization with `tf.layers.batch_normalization` on each layer except the first convolutional and output layers. Again, each layer should look something like convolution > batch norm > leaky ReLU.\n",
    "\n",
    "Note: in this project, your batch normalization layers will always use batch statistics. (That is, always set `training` to `True`.) That's because we are only interested in using the discriminator to help train the generator. However, if you wanted to use the discriminator for inference later, then you would need to set the `training` parameter appropriately.\n",
    "\n",
    ">**Exercise:** Build the convolutional network for the discriminator. The input is a 32x32x3 images, the output is a sigmoid plus the logits. Again, use Leaky ReLU activations and batch normalization on all the layers except the first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def discriminator(x, reuse=False, alpha=0.2):\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        # Input layer is 32x32x3\n",
    "        x1 = tf.layers.conv2d(x,64,5,strides=2,padding='same')\n",
    "        x1 = tf.maximum(alpha*x1,x1)\n",
    "        x1 = tf.nn.dropout(x1,keep_prob = 0.8)\n",
    "        #16*16*64\n",
    "        \n",
    "        x2 = tf.layers.conv2d(x1,128,5,strides=2,padding='same')\n",
    "        x2 = tf.layers.batch_normalization(x2,training=True)\n",
    "        x2 = tf.maximum(alpha*x2,x2)\n",
    "        x2 = tf.nn.dropout(x2,keep_prob = 0.8)\n",
    "        #8*8*128\n",
    "        \n",
    "        x3 = tf.layers.conv2d(x2,256,5,strides=2,padding='same')\n",
    "        x3 = tf.layers.batch_normalization(x3,training=True)\n",
    "        x3 = tf.maximum(alpha*x3,x3)\n",
    "        x3 = tf.nn.dropout(x3,keep_prob = 0.8)\n",
    "        #4*4*256\n",
    "        \n",
    "        flat = tf.reshape(x3,(-1,4*4*256))\n",
    "        logits = tf.layers.dense(flat,1)\n",
    "        out = tf.sigmoid(logits)\n",
    "        \n",
    "        return out, logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Loss\n",
    "\n",
    "Calculating the loss like before, nothing new here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_loss(input_real, input_z, output_dim, alpha=0.2,smooth = 0.1):\n",
    "    \"\"\"\n",
    "    Get the loss for the discriminator and generator\n",
    "    :param input_real: Images from the real dataset\n",
    "    :param input_z: Z input\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :return: A tuple of (discriminator loss, generator loss)\n",
    "    :smooth:用于label smooth的参数 防止过拟合\n",
    "    \"\"\"\n",
    "    g_model = generator(input_z, output_dim, alpha=alpha)\n",
    "    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)\n",
    "    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)\n",
    "\n",
    "    d_loss_real = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1-smooth)))\n",
    "    d_loss_fake = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))\n",
    "    g_loss = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)*(1-smooth)))\n",
    "\n",
    "    d_loss = d_loss_real + d_loss_fake\n",
    "\n",
    "    return d_loss, g_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "\n",
    "Not much new here, but notice how the train operations are wrapped in a `with tf.control_dependencies` block so the batch normalization layers can update their population statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
    "    \"\"\"\n",
    "    Get optimization operations\n",
    "    :param d_loss: Discriminator loss Tensor\n",
    "    :param g_loss: Generator loss Tensor\n",
    "    :param learning_rate: Learning Rate Placeholder\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :return: A tuple of (discriminator training operation, generator training operation)\n",
    "    \"\"\"\n",
    "    # Get weights and bias to update\n",
    "    t_vars = tf.trainable_variables()\n",
    "    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
    "    g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "\n",
    "    # Optimize\n",
    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
    "        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
    "\n",
    "    return d_train_opt, g_train_opt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the model\n",
    "\n",
    "Here we can use the functions we defined about to build the model as a class. This will make it easier to move the network around in our code since the nodes and operations in the graph are packaged in one object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class GAN:\n",
    "    def __init__(self, real_size, z_size, learning_rate, alpha=0.2, beta1=0.5):\n",
    "        tf.reset_default_graph()\n",
    "        \n",
    "        self.input_real, self.input_z = model_inputs(real_size, z_size)\n",
    "        \n",
    "        self.d_loss, self.g_loss = model_loss(self.input_real, self.input_z,\n",
    "                                              real_size[2], alpha=0.2)\n",
    "        \n",
    "        self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, learning_rate, beta1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a function for displaying generated images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def view_samples(epoch, samples, nrows, ncols, figsize=(5,5)):\n",
    "    fig, axes = plt.subplots(figsize=figsize, nrows=nrows, ncols=ncols, \n",
    "                             sharey=True, sharex=True)\n",
    "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
    "        ax.axis('off')\n",
    "        img = ((img - img.min())*255 / (img.max() - img.min())).astype(np.uint8)\n",
    "        ax.set_adjustable('box-forced')\n",
    "        im = ax.imshow(img, aspect='equal')\n",
    "   \n",
    "    plt.subplots_adjust(wspace=0, hspace=0)\n",
    "    return fig, axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And another function we can use to train our network. Notice when we call `generator` to create the samples to display, we set `training` to `False`. That's so the batch normalization layers will use the population statistics rather than the batch statistics. Also notice that we set the `net.input_real` placeholder when we run the generator's optimizer. The generator doesn't actually use it, but we'd get an errror without it because of the `tf.control_dependencies` block we created in `model_opt`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(net, dataset, epochs, batch_size, print_every=10, show_every=100, figsize=(5,5)):\n",
    "    saver = tf.train.Saver()\n",
    "    sample_z = np.random.uniform(-1, 1, size=(72, z_size))\n",
    "\n",
    "    samples, losses = [], []\n",
    "    steps = 0\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        for e in range(epochs):\n",
    "            for x, y in dataset.batches(batch_size):\n",
    "                steps += 1\n",
    "\n",
    "                # Sample random noise for G\n",
    "                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "\n",
    "                # Run optimizers\n",
    "                _ = sess.run(net.d_opt, feed_dict={net.input_real: x, net.input_z: batch_z})\n",
    "                _ = sess.run(net.g_opt, feed_dict={net.input_z: batch_z, net.input_real: x})\n",
    "\n",
    "                if steps % print_every == 0:\n",
    "                    # At the end of each epoch, get the losses and print them out\n",
    "                    train_loss_d = net.d_loss.eval({net.input_z: batch_z, net.input_real: x})\n",
    "                    train_loss_g = net.g_loss.eval({net.input_z: batch_z})\n",
    "\n",
    "                    print(\"Epoch {}/{}...\".format(e+1, epochs),\n",
    "                          \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
    "                          \"Generator Loss: {:.4f}\".format(train_loss_g))\n",
    "                    # Save losses to view after training\n",
    "                    losses.append((train_loss_d, train_loss_g))\n",
    "\n",
    "                if steps % show_every == 0:\n",
    "                    gen_samples = sess.run(\n",
    "                                   generator(net.input_z, 3, reuse=True, training=False),\n",
    "                                   feed_dict={net.input_z: sample_z})\n",
    "                    samples.append(gen_samples)\n",
    "                    _ = view_samples(-1, samples, 6, 12, figsize=figsize)\n",
    "                    plt.show()\n",
    "\n",
    "        saver.save(sess, './checkpoints/generator.ckpt')\n",
    "\n",
    "    with open('samples.pkl', 'wb') as f:\n",
    "        pkl.dump(samples, f)\n",
    "    \n",
    "    return losses, samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameters\n",
    "\n",
    "GANs are very senstive to hyperparameters. A lot of experimentation goes into finding the best hyperparameters such that the generator and discriminator don't overpower each other. Try out your own hyperparameters or read [the DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf) to see what worked for them.\n",
    "\n",
    ">**Exercise:** Find hyperparameters to train this GAN. The values found in the DCGAN paper work well, or you can experiment on your own. In general, you want the discriminator loss to be around 0.3, this means it is correctly classifying images as fake or real about 50% of the time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "real_size = (32,32,3)\n",
    "z_size = 100\n",
    "learning_rate = 0.0002\n",
    "batch_size = 128\n",
    "epochs = 10\n",
    "alpha = 0.2\n",
    "beta1 = 0.5\n",
    "\n",
    "# Create the network\n",
    "net = GAN(real_size, z_size, learning_rate, alpha=alpha, beta1=beta1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10... Discriminator Loss: 1.4855... Generator Loss: 0.8782\n",
      "Epoch 1/10... Discriminator Loss: 0.5580... Generator Loss: 2.3329\n",
      "Epoch 1/10... Discriminator Loss: 0.4659... Generator Loss: 2.5310\n",
      "Epoch 1/10... Discriminator Loss: 0.4914... Generator Loss: 2.6463\n",
      "Epoch 1/10... Discriminator Loss: 0.4285... Generator Loss: 3.1031\n",
      "Epoch 1/10... Discriminator Loss: 0.4219... Generator Loss: 3.1091\n",
      "Epoch 1/10... Discriminator Loss: 0.4477... Generator Loss: 3.1482\n",
      "Epoch 1/10... Discriminator Loss: 0.5618... Generator Loss: 2.5834\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-44f7f3b1a182>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Load the data and train the network here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdataset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtestset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mlosses\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-17-e173767cd948>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(net, dataset, epochs, batch_size, print_every, show_every, figsize)\u001b[0m\n\u001b[0;32m     21\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0msteps\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mprint_every\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m                     \u001b[1;31m# At the end of each epoch, get the losses and print them out\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m                     \u001b[0mtrain_loss_d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0md_loss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_z\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbatch_z\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_real\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m                     \u001b[0mtrain_loss_g\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg_loss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mnet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_z\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbatch_z\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36meval\u001b[1;34m(self, feed_dict, session)\u001b[0m\n\u001b[0;32m    568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m     \"\"\"\n\u001b[1;32m--> 570\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    572\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_dup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[1;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[0;32m   4453\u001b[0m                        \u001b[1;34m\"the tensor's graph is different from the session's \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4454\u001b[0m                        \"graph.\")\n\u001b[1;32m-> 4455\u001b[1;33m   \u001b[1;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4457\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m    887\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 889\u001b[1;33m                          run_metadata_ptr)\n\u001b[0m\u001b[0;32m    890\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1118\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1120\u001b[1;33m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m   1121\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1122\u001b[0m       \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m   1315\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[1;32m-> 1317\u001b[1;33m                            options, run_metadata)\n\u001b[0m\u001b[0;32m   1318\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1319\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m   1321\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1322\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1323\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1324\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1325\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m   1300\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[0;32m   1301\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1302\u001b[1;33m                                    status, run_metadata)\n\u001b[0m\u001b[0;32m   1303\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1304\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Load the data and train the network here\n",
    "dataset = Dataset(trainset, testset)\n",
    "losses, samples = train(net, dataset, epochs, batch_size, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'losses' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-f591e7f5ca20>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mlosses\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Discriminator'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Generator'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Training Losses\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'losses' is not defined"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x267613e2f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "losses = np.array(losses)\n",
    "plt.plot(losses.T[0], label='Discriminator', alpha=0.5)\n",
    "plt.plot(losses.T[1], label='Generator', alpha=0.5)\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "_ = view_samples(-1, samples, 6, 12, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}