WGAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import os\n",
    "import time\n",
    "\n",
    "os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' \n",
    "os.environ['CUDA_VISIBLE_DEVICES']='2'\n",
    "\n",
    "# from tqdm import tnrange, tqdm_notebook\n",
    "from tqdm import tqdm\n",
    "\n",
    "from skimage.transform import resize\n",
    "from skimage import data, color\n",
    "\n",
    "import keras.backend as K\n",
    "from keras.activations import relu\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Input,Dense,Reshape, Flatten, Conv2DTranspose, Dropout,BatchNormalization,Activation,PReLU,LeakyReLU,MaxoutDense\n",
    "from keras.layers.convolutional import UpSampling2D, Conv2D, MaxPooling2D\n",
    "\n",
    "from keras.optimizers import Adam,RMSprop\n",
    "from keras import initializers\n",
    "from keras.models import load_model\n",
    "\n",
    "from keras.utils import multi_gpu_model\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating images with pre-trained actor ###\n",
    "Images from level 3 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py:292: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    }
   ],
   "source": [
    "# saved_actor = load_model('models/actor_final.h5')\n",
    "saved_actor = load_model('results/wgan_gp/models/G_1747.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa324a51668>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8xePcuXNQc3v1zxMRaWhuh1rYXa7GM+kMzEmkcNu+w45bxz3gN4uIFGz6RJwHLFriZ6amoTY5Owu1+dlFrK3orfQVtXUwZ2MTT/MZGsGWpapKvf2+0aPbNkRE/u1nT+PPu3AWal07sd3myHHdFtZbwJNLZmbxuR86fQVqV2/+DGrB0go17i4OwpyyEjzlZS2Cp6FcvYJtZh3tjWo8tYHtL51dugVKRKQsgKe8jE6NQ80d1C015VX6PSsiUh/EVjKnCz8XF+eWoPbM959V483bm2FOqATf68FibNGZHcfTZppaddtdsBFbDb/y2S9BraYUnyuXA5+rN97UbWbbd26DOVW12KaF4BsqIYQQYgAWVEIIIcQALKiEEEKIAVhQCSGEEAOwoBJCCCEGYEElhBBCDGArFApb9mX9P7wKv2x8Ck+OiW3oE1tswLYhInLXPfo0ABGRvp5bUKsKlUGtkM6q8blVbOmwubEzqRJYMEREHG5sf7h48aoavzHQC3N278STRt7zgcehVohjK04E2JneePU1mNO5axfUnBb/7+YsJgT903eeVOPlNdg2IwUb1nL6dRYRaQJTPA4f2A1zilz4u6pq8Brw+f1Qc4B1lUngY2+wmIq0OIGn70RW8cSkv/vaP6vxq4P4HguFw1Czsom0NDdD7ciePWq858ZFmNPUUAu1/Yf3Q62uBeelwXSbmqZ6mJNZwVNeblzFk3lmx7DNbDOvP2q79u6EOTs6sYVkcnIIf1cKWxT9gZAav3rhBswppPAa9tjwc/HCGXytQ9X6c91fgu1ANg9+dj/xj/9Fvan5hkoIIYQYgAWVEEIIMQALKiGEEGIAFlRCCCHEACyohBBCiAFYUAkhhBADbOm0mdEFPMVDLNw7pRV6y3PvzeswJ7aOW/3tFt9ly2MrTjQSUeMVfjxZo7pFn4IhIpK3sFMsR7BNpKFDt4OUNWHLz4OPPgQ1u0ufkCEiMj0/ArU33zylxnfu3QtzmpqwdWN2ehxqVdX6VBMRkW1d+lSI+UU8uWRxEU/quP/uO6H20P26Vl6GJ6g4LRb37V5sL/F6cEt/Btjdug9i+87cJJ42E3DjNVzaVgO13/vsb6vxbzz5bZhz+Qr+zfE0tpA88sj9UOsFNp2aGnz/NVrYcG7dHIDargN4fSez+lQnT5EH5jz79FNQc7rwBJjlRd22JiKyFteP48WXX4c5T/7wu1BbXMJTe1Jr+DjS6Sk1bs/jZ1+Pha0x6MZWsqNHjkNtck5f+wOD2K45tYCtZE+AON9QCSGEEAOwoBJCCCEGYEElhBBCDMCCSgghhBiABZUQQggxwJZujv/K/3oOfpnPgTtNa6rL1fj4OO5AteqsrK/EXYvpqN4dJyLisuv/P7J5vJmzsxh399k9+DfnsSRl1fqG6m4v3li8ADbLFhE5eRJ3/i2M4o3/O3bqG90nY0mYs7qwALW77sHdtTd78WbaO/fona1V9U0w582TeAP/XbvxIIHSsN51OdGP1+LE8DjUgj7ctbgei0NtdmJGjefwcpPkOu6gfeydeECC04c/tOGEvim9Vdt+JoU78F949hWo+bz4XO2o71Tjzz/1bzAnArr2RURKw7hjfsdu/btERMaG+9X4Zhpfy2wOD6C4cK4HarF1vCl9Zbn+jLs9irtan3vuafxdSdwVX+zFG9ZHlvVrffK5V2HO2hLuzrdl8Dugy2Lj/KxNX4+9k/h8JOybUHvy0g+5OT4hhBDyy4IFlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA2zp5vidnduhNnLzNtTyGb093OXEGyzv3d0NtdmpWagtbKxArapEb6X3BvEG1ps53HptZXNx+bDm9ujt4YkEtvxcvHwBaol0CmolgWqobUT03zY7ga0xyQTeSPv8mXNQu/9t90BNgGvpe1//OkxpbcdrMRPD9pL+CX2z77mJOZgzY7EpfVNLK9SWVvCG5OEq/brEY/haJjLYnvHPf/cdqG3fjs/V7W/8vRo/cN9OmPOeT30Cau/89fdC7bWXT0ItYdNtKfOr2PYlKWztmYjiQR5ri/gZUR4OqfFIFFt07n7sPqi9/vpFqHUD25qIyBhYp8kUfh713cYWnV2HdkDttVdegFo8olvoqhv1AR8iImurMag5LXxh6xGct5bUNYcdP2c3MtjqhOAbKiGEEGIAFlRCCCHEACyohBBCiAFYUAkhhBADsKASQgghBmBBJYQQQgywpbaZcHkx1MYGBqB25KjeHl5Zhe0qr518Fh9IAf+PeOTxd0NtdlRvpR8bH4c5IdBGLyJSXB6GWiqJJ0kUgdb3jTi2eyQ38ASY5UU8SaKlFk/WSK/pfpVwSJ8OJCJSX4Mn/did2NZx/o1fQK0kVKLGTxy+A+ZEI9i+Mz2ELRORmD4JIxbB597pxOv+xs1eqMVS+Bg3U/r3FVt81wcf/xDUnA4oSXwVT//o7mxT4xtleKLMyOA1qDW24Ek/9z14P9RkU7fA7Du8H6aM945Dbcc2vO4bwCQXEZG//au/UeNNLTinyI3tf2kbvieuDeAJTPF1/Rnx6d/9FMzZfx+e9tR7+QzU5maxLazYHVTjHV3YipXHjz555gfYolNTWg81r1+fVLRiYZGz2//j5ZFvqIQQQogBWFAJIYQQA7CgEkIIIQZgQSWEEEIMwIJKCCGEGIAFlRBCCDGArVDAExdMM3H6Jvyy8b5hmFdVXqrGX/pX3ELd1ICneDQ2N0Ntekaf0iAisntPhxr3FuFzaC/kobYes5hmUOSFUgi07V8+je0IiSU8ZWJHG25hd5XjiSfjI2NqPFiM7UxlVXjKRDSKbSKJBO6lT23o1oLKMJ6UMzw2ArWiEh/U0oWcGo8mLCxLCbwGrl+4BbVCEv/f9Qd1e0xNO271P3D8ENSsbAy3erA9o6JSP8fXLl6GOdEYttRMLc5A7Yv/+Y+gVt3UoMYzcWz5WV3Cx5HBg0vkO//8faidu3hFja+v40lQx+/cDbU//9O/hdo3v6pbdERE3F59few6hKcAVVdja8+1y5egtr2pCWp1Vc1q/MIbr8GcHV3boFbRjq0xX/+mPvlIROTqtX417vPrE8RERNY2wRgrEfnp1e+rXie+oRJCCCEGYEElhBBCDMCCSgghhBiABZUQQggxAAsqIYQQYgAWVEIIIcQAW2qb+cfP/Rn8sl/7jY/AvMX5eTU+3K/bNkREZkbnoHbiBJ5asRxZhJrDodsfKqr1aSciIl43tjGMjmKrkMuNrRubwIXRd/k2zKmpboTa8ji2FkgA24jsOf23HTl8DOY47W6oTVlYlmw5PA7FUShS4+m0bnEREfH6PFCbXtXXm4iIp0yfWjE+hSduJJK4/X5hHtuS4svYilMarlDjJSE8Vegjn/oY1Lw+vE5n5mahlgen2F5wwZz0BrZAXb14EWoDw9i+88gjD6rxtnZ9Go6ISF2rboMTEVlfwr6Zh9/2Hqjt3KtPxnLb9XUjIlLsx+dqsh9PPvrkx38LapfAeTx77izMqarFFpJkMgW1urpaqIWAhS7g0u9ZEZGa6kqoPfyeR6FWXKvfEyIiV85fUOP/+vTzMGd9E0/6+cYr36RthhBCCPllwYJKCCGEGIAFlRBCCDEACyohhBBiABZUQgghxABb2uWbuDwNv2xlBXfXpjJ6t+P0JO7GDBbjjq9vffN7UCsO4I3d62v0jcDf8Xa9w1BEZHxiCGqNjXgz6lBFGGqJDb2T0+/DXXqbGfzfKTaLu0nLG3CHqsetn6v4SgTmjAzhTenXorjb2FuMO6ljEX3j8VQKH/vO/XiT8KwL59Vv0zfn7u0fgDk3Lvfiz6tvhtrtG/gzY1F9Ddx9F95YPFRbDrXd+/AG7dEIvp5DA3qnulWXbEcLPsbNGO5SjizibuPBfn0wRH1bC8y5+xHcMRpZwIMrfu+zfwi1iYVlNb5zm979KyKyZ/8eqF0+izelTyfwubr/vnvVeP9gH8xZWl2AWnkFXjten74Rvwg+xuoyfeCJiEhFaQhqTq/aXCsiIsfvvwvnufVhI+kkHhqSy+Hu684P7GOXLyGEEPLLggWVEEIIMQALKiGEEGIAFlRCCCHEACyohBBCiAFYUAkhhBAD4B2xfwn85Cc/glpzUzPU2ne1q/E73/52mHPjYg/UFlajUPPEsWVicV63D+zchVviF6ZXoLa9E2/ObdXOHVnV7SVjo9hW8MzPX4bazBjeoD2d1m0AIiJvf1Q//zs7sFUhuoEtOsEybPvJ2fHm+BlgI/KFcPu9pwJr8+v4PIYadKvTTh8+vtFBbJ2qrcX2qHDJXqitr+lWIZsTr+2QH29IfuumbjsREcnbsLWupEy3TNRW6RYzEZGChZ3JH8RDIULeZqi11usbtL/84r/BnCv201A7/OAjUPvCpz4Ntb/8m79X40012HZyu3cQah4HvmZ1LbqFS0RkbV23/Swu4fu5uroKannBgyZKgkGorYLpCZE4tlU1NTVArdiPS9bslMVwDad+HqNRbI+KxPCzu/MD+9Q431AJIYQQA7CgEkIIIQZgQSWEEEIMwIJKCCGEGIAFlRBCCDEACyohhBBigC21zYwNTEBtZ1c31BYjeqt3bQe2KhSX41bupTXcDl3sS0Et4PWr8bfO/ALmHL/jENSuXL4KtaIi/F9n1y59MsjE6C2YMzg8BrWKUBPU8k483eHnL76mxmeXsY2oqhpPjQls6lYQEZG8DR9HSblutwlWYBtOcS2eKrR3F75mdp9bja/NYDvQvW97AGp+N57Ucf7MWajlwdK/46H7YE7Bwnq0tGRxT/j1dS8iksnoliWXHds9YgvYprWyiI9j9/4DULv2ykk1/tjb3gVzFufwdJW3fvpzqNU2tkHt87/zSTX+/R//GOYMzy9BrdViGtHc0hzUEll9Pba0Y0tbPI6vS6uFrbHCwiLl9QEbVB6myOQs/l2d7fg4cin8oQ3NuiVofhrbiGxZD9QQfEMlhBBCDMCCSgghhBiABZUQQggxAAsqIYQQYgAWVEIIIcQALKiEEEKIAbbUNuMOlkLtRi+eyJEvTqvxffdji0D/RD/UDj18EGqX3rwAtboKfWJE1oatNhcvYkuN14X/z2xrb4Xaxro+USRUiq1CmSyeXuMswnaKQBBPoCis6stnZBG3vT/6q2+DWmNbHdSKQ7iFfbOgt8unU/q6ERFJF7BFxyP4fHhsXjVeU4+tA1PD2C62OottIk1d2OLQ0qqvD28ptgMtjE9CrbYaf9fPnnoKau94tz6VxWpNpRP43Fd48HlcmBqHWkmVvk7dxXh6Td5ifTRU4+NwFeHHZjCo26A+9RndTiMi8uWv/A3U1tb0CVciIkVF2Jq0tKhbgqan8blvasT3H1pvIiIHDx6D2quv6Xam106+AXMaLY6jwcJGlExh69rzz7ykxhcszm/Ir0+WsoJvqIQQQogBWFAJIYQQA7CgEkIIIQZgQSWEEEIMwIJKCCGEGIAFlRBCCDHAltpmFibwzv7jc9NQe9sH7tUF3Jkvj38QT5k4duIw1MY+NAq1v/zj/6HGt1XgNu/dnTuwtqMLaivzeBJGkUu/bF0d22HOnkP4OPoHsJ1iehxPoKip0W1EdbXYgnHgnqNQ28zGoBaPYXtJJArWVQFPqAmXhKGWTOq2JBGR9ah+HOVhbC9qbsHnw9Wp23BERFYXZ6EWBe3+8Ri+Xh47th599ctfgdrxB8D9JyLzs/q5D5Zgu8r0zDjU2pracd40Xqettc1q/PTJ12HOoe79UBsfxDa+gUt4ClBptT65KZmDKfK7n/oE1L75zW9BzYcmuYjIyqp+Xepr62FOTU0t1GZmsBVueuYZqPUNDqjx5aiFHWgO24Gef0m3v4iIdHd1Qi25oU9FKlhcl7xVgQHwDZUQQggxAAsqIYQQYgAWVEIIIcQALKiEEEKIAVhQCSGEEAPYCoXCln3ZvVX3wC9r3407VFt36R2Uf/SnX8Zfhhs85akf/Ahq733Pe6Fmt7t0weIUZlfjUEst4k63ZHQdaumkvsF1Iqt3somItB/B3bVf/2u8Offff/dJqH34gx9U4//pv30J5rh8uLE8tbEGtWQCa4VcRo07Lbr0ohHcNZzLZqG2PL+oxtObeME57bh4A3kTAAAgAElEQVSTt60Fbzpe5Mb/dyeGR9R4e7veZSoi8sZreFCD310JtbZ23HmbEn19L6zMwJxd3bjjPBbB94RrE5+PfEpv19zcwIMrskk8MOLsG29Craoad/UXefUBFUsr+H6esOjoLyvFm/S/8qq+8byISLi0TI2XlOPu9vlZ3MlbVYu72IeHsTPC5XKr8UwO32NuJ35G1NfpzgIRkYoQHg4SLC5R41ML+NxX1ODu/N/91mfUG55vqIQQQogBWFAJIYQQA7CgEkIIIQZgQSWEEEIMwIJKCCGEGIAFlRBCCDHAlm6Ov+/EIahZbUb92T/4uBpPr2IrxabgXY/f974PQO2lp5+F2ulXT6nx559/Bea0gU27RUSKbdhO8WsffD/Udu3QrRY2CwvUyed+ArXf/uRHoPa7/+ULUFuY1Ddvd1jYiNIr2EZkL+j2FxGR/rOXoeYCe2nb8vg48hn8X9LtxpvIN1XrrfRDI+MwJ76BN9tfdcxDzR8qhlpViW5zWVvDn/fgww9CbWUaH6PHj89HGBxjY2cDzFmL4CEZeTtePBs5bAtzOnSL1OwqtkXUVeFjFA++N5fW8HPHFdetOIGgbmMRESkPYmuPP+CHmtU6jUT162kDgzVERKLr+N60OXDewQP4uf7q6/pwgpY2bBfLJvF1tlnYIQ8cPgi1sSHd2tNQj4cFLFmcDwTfUAkhhBADsKASQgghBmBBJYQQQgzAgkoIIYQYgAWVEEIIMQALKiGEEGKALbXNLIzhaQadrXhn/4awPmEgu4Lb1/tv34ba2jLO29ONW68b36VPfpi6iX+X24WtD+tr2Krw5PeegtrRw11q/L7H7oQ5d96FtZm5aai1hvB0B59d96vcPncN5jzzk59D7eChfVCrrsVTMuqqdAtJZBlPLgmXVkBtehpPSrl06oYa34jhVv/lJXwcby2+BbXGejzho7tTn9iS8OhWJhGRi0v6sYuItLd1Q63UwqvQO9qvxhu3NcKcYAm+ltOTeHJJRwueUpOI6daThu3Y/pLdwBNPHnu/PklJROTM67p9TkRkHNgzMtgRJomYPj1KRMTtDUDt0MH9UBscHVPjV3tuwpwDB/ZCLbGRgNq5s+eg9rGPfFSNj47pxycicvsWPsbqyg6otW3fBrWVFX261NAYfvZdvNUHNQTfUAkhhBADsKASQgghBmBBJYQQQgzAgkoIIYQYgAWVEEIIMQALKiGEEGKALbXNJNK4PTzkw5Mfes7rk0Z8Pnz43d26tUREZHRzBGprFu3coVCNGrdn9AkTIiLhMt1qIyIyNTsFtbJQCdbAhIS8A/8/iifwRAuxmA4zfl23RYiIXL1wS42PDeFzePEXV6HWUoYnPxzfewBqMxPjanzUYgLMxAi2qzjd2Oq0Gl1X4zdv4PMUs7ActLbg37zmw+vq5edPq/GiKmwFWVvFmmRDUDpWjS0wHdt1u43Lr09/ERFxOd1Q6+rC1qmp0QmoBXz68dtdLphz9sJZqNVbTKJpbm2DWjKuX+t0Ig1zPC5sS1pfW4TaahTb/3Ki3+9VlaX485Z1a4mISDiErU67urHl6pXnX1TjHi+elFNdia16leX4+EvK8DPTVaSvg8bmJphz6iptM4QQQsj/FlhQCSGEEAOwoBJCCCEGYEElhBBCDMCCSgghhBiABZUQQggxwJbaZo4dwtMMmipxW3Z7o97CvpnCNpy5gWGoFeVyWLNos8+ldcvE4+96COb84Ol/g5q4sV8l48IWh5RLP/60DX+e3+eD2tAwtrkMXBiH2quv6dYNRx7/Tzu87yjUVhZw2/53v/kdqD32rkfV+MLUMswpK6uDWv/wONTm5vTPdDiw1aa1CdtObFlsjRkdwLaq7nZ98orNh9d2fHUVateuYttPMoXX4l2PHFLjRW58HyWS+L512PAjKbmBz1U6of+2DgtLx4PvehvUnv7ez6BWU4anADVv020YC7N4CtDMEF6nm4481N7/K49DbWFFf1b9n3/+NzCn2GKyzfp6HGpWk2gcwMrX3YUnB1XVlEHt7oeOQe1qj26vFBHp6ta/79wlPIEpZ/E8RfANlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA9gKhf94J9P/W6Zfvwm/LFzshXnZlYganxwZhDkui43ivcEg1or9UMuDTb1jWdwF+bff/ieoFRx4k/C77r0basGg3rH78ONvhzlvvvAS1Eb7h6CWj+HzuLCgX5fVZdxN2ntN31BfRORD78Ndi14P3kAc/S08ftd9MOXZZ16G2sAw3oR9Hvy2XA7fR1XleEDCZkrvxhQRCXpxp2xLPegmTeMN0+NxvEF7dTU+Rk8xXgPH79mvxjt2tMCcyXHcvXz1whWolYTx5ucduzrVuC+E7+fKetzpbSvgc786gzt2Mwm9g/nsaTyM4cQRfK9fvHQOanc/hNf3FbCxezqJ76Mnv/cjqK2u4HV1x1HcuX/q1Otq/NOf+G2Y096Bu+LFlYFSqUV38MiIfk+/dvIMzLk9jq/zTy/+i3oi+YZKCCGEGIAFlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA2zp5vjh0hDU1uYXoDYzPKDGgx684XtlLbYBxBN4M+c10PYuIhKs1jdAr6mugTl/+71vQU2c+P/MyvQ01Eor9N/28+//EOaES3BLeaAI24gigjfFrqjUP3Oo/zbMOXZCt1mIiNQ2W2xYf/Ma1ErL9cEKq2uLMGcDDDoQEfEFPVDz53UbRmw9CXOiG/i7Mha2md3d+6DW36fbj6qa22GOzYVtM6tRPJjAsYE33B/pG1Hj189fhTmbSbzJfU0Vvpdsafy4Sq+l1PjyKv5diU18zaqqyqHm8mPrSZFf32D+4D13wJx8Fp/f6hZ8TywvL0Gtslq/Ny+e64E5GbEYrJDBz8xrt/C9efSOg2r83gfvhzk/+9fvQa1tZzPW9rVBrcOn26CuXrkOczrt26CG4BsqIYQQYgAWVEIIIcQALKiEEEKIAVhQCSGEEAOwoBJCCCEGYEElhBBCDLCl02Ze+/q/wC/zufG0mZBH11IJvVX+/4kidxHU/CXYQuL06tNhliN4usrIoG75ERFJZ/DkhGP34kkSMxO6paayoh7mXL+Mp7w4bXiyRtt2faqJiEg8rluMlmbmYc7i5AzU/F68Bpz4kokvpNun8jhFnEW6BUpEJJd1QO30hctqvOcWtgoV2bDd48DuHVD7jV9/H9QmZobV+LVrozBnahFbsQLA7iEisn1bK9TKgGVpYmAc5mzGsH0nEcO2NbsNX9GSWt2St+euvTBn+25si9gUfG/Ozk1CLbWh/7bDJ+6FOf2nLkKtsgbbiHquYWvSjp171PjoBLYn/sX/+BrUtrV1QS1uYd956IE71fiBA3jdj07i6Ve1LVVQc/jx9K6Ssko17nbi58Ali8lH9//mOzlthhBCCPllwYJKCCGEGIAFlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBttQ2E7s2Cr8sk8Kt9EnQSh+PxGBOMKS384uIpNP4uwJBbB/IpPUpGdevXII5xRat3OEgtuhEU9gSNAtsKcVluMV+ZBhbJjp37oRaa4vebi4i4nTo9hKPE9tOXv3pC1C7525sFSrY8SQMp0+3pZx683WY89IrWPv8Z78EteEp3Xbwwmv483LpLNQ+9uvvh9q2bfjci0M/H4kktujELCwp84t4Mk8qhSeNJOL6xJbYMr43XVl8T7TWN0BtbES3ComILKXm1HiwEdsiSupLoHbkbt3uISISiWKbnMuuW79qK/G9efNNbM9oasG2NYcNvw/FwfPDbzF1yuUuhVr/zUGoHdmLJ0jNjul2skweT7EKBPEUMcGDoMThw2I6o1uuRvuwzay6Ft9/Ox67i7YZQggh5JcFCyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA2xpl29mcgl+WWRmFuatzOodiE1tLTBncWkFaoFi3N0XmcN51y/q3XiNdbgzsbwMd84VCrhzNZ/D16UUbEg+0Is3aO/u3ge19fV1qEUn8HWJJfSu0fZdeNPx8SW8sXh0A3c2F7lxt2YWNNFmLLpro4u4U7OQxdfFF/KrcXsR7q512HHXc015OdTyeXw+0g69U90Wwp3jS8t4bbscuPN2bSkCtfGxcTVeV1WHj2MGdxSXl1RALWDRFW8D7wbTS7i7fXEdD3H40n/5LNRSm3pns4hISZXeldtz+jT+vFm8Tiur8LNqZUnvbBYRKfbpAy9cDjwII2dRC9p2d0DtGhgYISIyNqTf74P9eGjIrp34WeXyWtxLbXjNtXfrm/ufO3MW5ogTn49Hfu8j7PIlhBBCflmwoBJCCCEGYEElhBBCDMCCSgghhBiABZUQQggxAAsqIYQQYgDc6/9LYHJ0CGo5i83gq+v1jaXzonYui4jI0gq2RYyNYStIJoaPo7SqSo03bGuFOSsLuLV9fhG37edsuGXbWaZvHl3RgW1EY0v4Nw8O4E3HAwlsIamtq1XjyxaWlLw+X+DfyeHrub6GrT2BUn3D75WVNZjjK8MWjPJwCGqJDLCr2PF/U6shCLXVeNP0oUFsg0IWkqIA/i6PF1tjihz4UZDPZ6C2t3qvGl9fwZvjt4fboeaw4eO4fAlvIr+tA9g6vPi6fPGJ/wS1+QVstxkfHoHa1NDzarzMws4UWcY2nFDZDqgtruHnR7VPf1YFyrEtqbQSW7hu3sI2l/lIFGoZcPq79x2COYtzy1DbSOEBDzafPphARCSWuqnGW5rwMzO9iTfwR/ANlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA7CgEkIIIQbYUttMuKIaapkN3Gafyet2ikxCtzCIiKwuYcvE8gK2dbS1YAtM2K9PGpmdnYI5xcW6xUVEZFt3J9Q8Xg/Upmd1K051lW5jERF563U8VcHrx5NcppcWoOar0Cfp2DPYeiQu/B/OW4Tb3jczCai5Xfoybmtvhjn+AL4uU5MTUHN6i9R4bbVuUxARia/j9vubt/V2fhGR8rA+VUhExOMBx5+HKVJZWgm1XA77mfYe3AW1cmCdKhSwBWqspw9qLz33CtTe/vjDUPvpz55W413dO2HO8jy2knktpu8sjmMrnMcOprmksP3sjnv2Q+3WtatQO3HPcagVgP1ocxPb8Z5/Fp97jxs/j6JRvL4zOX1Bem14fUzO4euSz+PJPMXj2Opkn9a/L59ugzki2C4Gv+c/nEEIIYSQ/xssqIQQQogBWFAJIYQQA7CgEkIIIQZgQSWEEEIMwIJKCCGEGGBLbTNuD7ZFJCLYyrKZ0KcxJGLYNrOZxC3PLtTaLiI+N26Xt4G/H03teGLB2jq27zjc+DgmZ7AVx1Okt7APW0zB8Hjxud9Yx5aUgkW7/PTCkp6TwxaB4hC26Lg8+HwUB3XLkohIYlM/fpvucBERkWBFHdSaPdhaYHfqi+D6FWxvCJfiCR8+P7bvrCfxFBJkR5iZxzancAWeolNdi20/Jb4SqC0Di0NZLbbI1W+rh9rHP/9JqDkK+HEVfO1lNX7iriMwp6FjO9R+9I1vQW1lbhFqjbX6b9vdvRvmLCbwdJWHHn8EahfOX4Ra77VeNb69aw/MaWvB5yOZxbaqXXv2QS0S1X9bNo39XVkLt0omjY9jbHIQak6XA3wevsdKy/H0HQTfUAkhhBADsKASQgghBmBBJYQQQgzAgkoIIYQYgAWVEEIIMQALKiGEEGIAW6GALQKmGT3TA79sdRZPCvA4dMvH6uo6zCnk8e8qDeEpHl4L20xpWLcdLC5iqwKyWYiIbKQ3oBYIBqCWBFN2FmZXYE4hg49jfRVP+omsYjuTw6nbXNaiOKeiogxqwXAQaqWV+mQbERG3T79mlVXYuhEMYRvRzRvXodbU0qzGS8LYWnLp/GWoBbw4b25qHmrxqG4VCpXhVv/WZmxXKa/BeWfOnMLHkdLvwepGbEsqr8EWnfpanOezmEaUS+tWrVQcTz76h7/7OtT2dh+A2r69eDpM3019elB1Gf7NoTa87peBNU1ExOnCFrTUmv67n332RZgTDOJ7s6MTW2pKK/EabgJrru+GbusRERnqHYLa9q4dUAsG8flYi+n2xZvXr8Gc+tpGqH34q0+o42v4hkoIIYQYgAWVEEIIMQALKiGEEGIAFlRCCCHEACyohBBCiAG2dHN8nwdvBL6Sx7Xd5tY3Nt6xsxPmTE/hrmGrfxE2G+4OXpjXuy6TFhssO4vwhu8Ji83PG5pwh9nIkL4Re3VZDcxZi+COYpcT7yLvLcGdc5OT+jkuLcNdiw+/+1GouS02x4+s4A7m+WW9y/rUm6/CnKPH74Ba+/YOqKU29WuWsxgIUG7Rebsex2vAF8LnMZfT74kLl67AnJdefAVqjzzyINQeuO8hqC0szqjxoYlhmONx4MeOx6LLPhqJQm2gZ0CN59J4gEZXK+4Y3d66DWrTY+NQE/D8WNvAnfSBNF73uQLeRN4j+hoQERkdGlfjTouc5AZ+RgQD2HVQW4O76R1utRlWug90w5y9h3CH9foqfg5Mzc9BraxGdwkUDeL1Njyhrykr+IZKCCGEGIAFlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA2ypbUayuAW8sgJvHj09N6vGrWwn+UwWaoEw3gTaYcNt5QG3vjn+2hRuiY8n8ebcXTvwhtNDQ9h2UFevbyC+OIc3pd9M4vOR2tyEWrAEb0p/pLFZjbdvw5afZArbGC5dxpvIu1z4v9+uPTvVeGcXPr+Lc4tQS0SxfWBhRbdOJdbjMKeyshJqTfV4w/dbfSNQi0X0dZWwOL8eP7bhnL+I7TbFIWydKi/Rj7+yFK8byeK1KBbDOkaH8PmYGB4Dx1EBc6oqsc1sZGQKanNWgzy8ugVt9+4GmDM1NQ61XXsPQ83rwc+x/IZu41qew5vtLy7j58fF8+eh5vAdgdreWt3aeK0Hb0pfEsBrp7QCX8/Dbfug5gZrP2dhk0xEl6GG4BsqIYQQYgAWVEIIIcQALKiEEEKIAVhQCSGEEAOwoBJCCCEGYEElhBBCDLClthm7HdfvXBZP6zh8TJ8MMjrUD3OWV3B7eLgiDLWJoUmoxYGdIpnA9p3GhlqoDQ9ia4y32AM1j1efkFBbh+0ZC4sRqBUF/FBbm8Z5Pf2X1HhZKZ4qlM5iW4fVecw4sJ1psF8/j7VV+HwELSa53LjaA7XaWn2yRmIdW6cGZvRpOP9+HNj6UFFSArXUhm49KbP4vLTF5BW3Gz8KxgfwOg3t1W0RpeX4HnNZrG3BzjpprMfWk4HLfWrc58NrMb6BLW09125CbT2Br3Vjk36Mt/tGYc7uQ3ga0eykPs1HRKT/2imopcH6yGYyMCe6tga1bZ14+k5XZxfUzl44o8ZLw/j+iybwcUxemYCapxhf6/qGJjWesrBenjt3Fmr3P/ERNc43VEIIIcQALKiEEEKIAVhQCSGEEAOwoBJCCCEGYEElhBBCDMCCSgghhBhgS20zhQze2X95fgVqdqdNjVeU46kEuSxuD89uYotOfhP37Sfj+lSWoMUUD8FfJW6XPplCRMTnx1NIyiv1Nvt4HE9JGRvHEzLcxXiaSFUlnu7QtbtNjfvLsA1nNYptOKlNbOtw2fBS3QD2h8lZC7tKFFsf7rzvfqhFI7odq5DFdhU7XvYyMjIOtZnxQajZbPraCXotbCJ5fCDBYrzevEUuqKGrElvB19mziddbMpaA2syUPnVKRGTfQX3SSHU5tq298MxLUFuxsJCUlWM71npcP/793bptQ0SkkMWTioq9+FyNjWIrztoasPil8GSpylo88WvV4nxkCviZec+DD6jxWzeuwxxHHk8jqq7TJ22JiIQC+jQwERGvX9eOHNSfYSIip954BWoIvqESQgghBmBBJYQQQgzAgkoIIYQYgAWVEEIIMQALKiGEEGIAFlRCCCHEAFtqm5kYGoJaVSVu2Xa79ekqUzNTMKe5sRlqc5OLUOu5cgtqO3Z0q3GXC/8vmVvE1o2cA7eHN3Q247y83qa+sICtRyUl2GI0NoLb7+NzeBpDxYo+UaRz93aYk0zjz1uLLEOtslKf8iIiki8Ab5INt/MvWFyXkad/CrVYRLeDlFhMrykrwzaL9hZ8rra14ikeL7/4hhr3+7DNImVx7uvq8fktL8e/zQEmSNktHi3JdWwTWbc4xqbGRqgF/fpknuE+/MzZzGALST6P7+mFBbxO8zbdmvT1b/wjzOncE4Da8jKeiOMS/bkoItJU26rGlyKrMKfnxm2oVYIpSyIiLz/7KtQ++oefVON2O7YMTk2MQ81ZwBaufBbf75m0rtmzuiVTRORjH/g01BB8QyWEEEIMwIJKCCGEGIAFlRBCCDEACyohhBBiABZUQgghxAC2QsFi527DzJ3qh18WWcKdt6FKvcvQF/DAnNgG3mR7oG8Eapl13ClWV6F3us0s4k27s1m84fvRe49AraRC3wBfRORH335SjcfX8XfNTs1Drc6iw7q+pgFqyZTe8VrVgDsCK+vxd7V17oTa0vwc1LJ5vcu3pAR3T44ODUNtfg53AB8+clCN37rRB3MuvnUOaok4vmarUdwN27VN7zj3FuF7wuXGnbclYbypfiCAu0mryvWhAPkMHk5hNSAhFNY7x0VE8jl8by6u6p9p1eV7xwk8BOFP/vSrUAsE9I5iERFPUD//22rwJuyLyTGoVYbwZvCOggNqqbS+rhYW9eEOIiIzc/gZnEzj5+nKGu56/tlz31XjLgt/yY0bvVArDeH1sWmx8X9/74Aar6rAHfjLs1GoffCvP6G2B/MNlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA7CgEkIIIQbY0s3xe2/1QK3Yh9v2XcAJ0Hf7BszZdeAA1O556EGoXTlzHmo2l+762SzgDaw7dm2D2tjkBNTOfu9foOZz6237hQz+f7StqRNquRQ+/skp3NKfSuhWhbraCphTEcR2oPHefqg5PNgO4vPrtiqXA2/AXVOHN1ovKcG2iJeefV6Nl5Xi33z02HGoRZbWoJbYwDaAIrAJfjaG7Q02h4VFDg0YEJFirxdqaWDPwFuOi7ic+Fo6bXjz82QGb5yPrvWR4/jc253YDuTz4iEDIbDeRETKq3Rb2MQ4vter27CVLJfDZzKVxtakYq9fjdvt2ApSW4vviXwOD/LIbmLrVy6hW3sWlmdgzt5dh6CWSuA14Lawhc0BK9yVy9jS1lzTAjUE31AJIYQQA7CgEkIIIQZgQSWEEEIMwIJKCCGEGIAFlRBCCDEACyohhBBigC21zcRWsEXgxJ3HoDY9MarGbQU8fWJpAbdlLy3gySvdu7dDbTOrf9/2fR0wx+HFrfmZBLY4hAJ4Usr5t66o8fkZPPXGWYqPI2kx1aS+DdtBYit6S/zyEp4+0WnxH+7P/ux/Qs1mMUVlNbquxvfs0yeyiIgcOoC1E3fjtbjnwD41XhbGdqCohTVm17Y9UEtYTM84c15v9w8HdbuEiEi4PAS1rGALhtuHbTOS0604GxbTnian8Dot5PB9axd8v7dt1+1ppWWlMGd0HH+XrxhbrhYX8eSjxiZ9OlNLezPMydnwdfYXY4vO6PI41sb0Z6bdgSfU2OxYW17G93RZUJ84JCISnddtOmOj0zDnC5/9r1A7cATfL0fAJCgRkUff+041fvXSGZjjK7ZY9wC+oRJCCCEGYEElhBBCDMCCSgghhBiABZUQQggxAAsqIYQQYgAWVEIIIcQAtkLBYgKFYXp/8Dr8sqWVFZhXWqq3L2cEt5sn7Xg6wtE774PaUB+eeFIOrBFOF56Qcbt/AGpd29uhlknj4//u/3pSjbuceEJGOoGniZQHsLVgs6BPlBER8bp0K04hjy0Y9e3YlvT6KTzp58pNi+tSV6fGk3HdTiMi0tCAW/3f/tgDULvn7iNqfKAHT1IavIWPPbeJp4nMTGOrwic//3k1PnJOt1SJiIzMj0MtVIZtWts7sS3s+lX0u7EFQ2xY84MpKSIi68v4GWFz6JaaplY8QSWdxfdYz43bUCsuDkNtZlq35BUsxu9UhvHnTUxje0kuh+/poiL93pxbxpbBiIWt0W3Htrt3vvPtUGtqrlfjf/LlP4M5tfXNUJtfwpaloB9b6/7n1/5Cjfsq8Hqb6r8FtdZ3nFCvKN9QCSGEEAOwoBJCCCEGYEElhBBCDMCCSgghhBiABZUQQggxAAsqIYQQYoAtnTYzODwOtT27d0FtfkHPK7Fo9Xe4sB0oYzEJY3EWt+Y/+9MX1PhmCrffV5Zje8biOG5h371Ln2oiInLs6Ak1fvLkGxbHobevi4jkHLinP5/GS6SstlaNpxNJmOPz44kn8SS2AZSV61M8REQcLp/+eYlVmLO8GoNaa1sT1OxF+n/QUBW2PtxRdw/UkivY+pV5/RrUPvu5z6nxT/7KB2HO7gO7oWZ34v/W0XVsPyoAC0w4jKcUJTfSUHPbsPWhJlwFtes9Z9X4XQ8chTnrm3idNnViu8345AL+zIS+rpwWU6fCDvyby/bh52JiAx9/Oquvq2Cpfq+IiEzMjEOttrQGaju69Uk/IiKn3zqtxhM5bK0bm8BTgNpb8HdFV3He1/76a2r8C3/8BzBHPBbWLwDfUAkhhBADsKASQgghBmBBJYQQQgzAgkoIIYQYgAWVEEIIMcCWdvm++spJqO3bsx9qDpveITc5jTdKPvHQvVCz24ug1tszCrUf//glNV5ZjjsaH77nLqh1HtsBNcng/zoz4/qm6dta8cbz/YND+KtE31hcRGTvDrwxerBC72xNx3HX4swE7mw+dvQOqD332utQ6x/QN2h3WXTpPbj/MNRqG3E36cSEvj5cbtwpvZnF3ZiRdbwhucejD4UQETlx9Lgav3XjJszZ6MHdtcEQ7phvam2BWm2hJyAAABb3SURBVFtrsxpPb+CObdnEa3sziTvml+YWoYbOx+I83lw+bt+A2loCOwF27TsGtaKg3kUbKiuBObF+3J2asRgkcMNiA3+7S3+033f/gzDn+o3LUAsHcHf+4jw+/tn5KTXu8eKBIk7B6z6fxu6N9lbcAVwa0muIqxg/q6oa8HMAwTdUQgghxAAsqIQQQogBWFAJIYQQA7CgEkIIIQZgQSWEEEIMwIJKCCGEGGBLbTPN29qgtrSEN5x2unRLgiOHW6iLbHgT6Od+gu07P/q2vgG+iEhjSLe5VPrxxugrk1GoLc5hC8nxd94DtZhNt1pcPX8L5vjdxVBz2nCbet+FQahNj+gt8Z/+4sdhzsIsbrEf6BuG2kcfuxNq4fDjary2uQ7nlPihNnNlBGoXzp5R48XF2HZS19EMtcpWbLmq6sLH/8pLp9R4egXbLIIhbAMoDeDjDznx+lid0W0pHV3tMCe6ijdGz4rFhuRu/Ljy1ZWq8f5efC2jK9g2E7CwEb15+xWotbTpQyicPrw5fmwCW3s8FXh9nD97AWpjs7Nq/MWXXoM5fh+2E3oF28KqyoNQe/djj6rxltJymDM2OQ41+0Ycagf3dENteGpMjT/9z0/CnIfehS1GCL6hEkIIIQZgQSWEEEIMwIJKCCGEGIAFlRBCCDEACyohhBBiABZUQgghxABbaptZWMYWkpOv6TYAEZEHHtAtE0U+bH1YXVyC2qm38Hdt5jeh1tCh237mF/SWbBGRiSFsZTnuOgC19fgq1LqP6XkeH7bGPDP9LNSqyrHtx5HB1qSSsG6nePWFV2FO5248EefYvXjaTHIDT2xx2PSWfp+FVWG0D9uB5qaxhat9x041Xl1dC3PiG+tYs7BuONN4Ld55tz7x5M3JN2BOIoUtBysD2M60Oo/vs7bWRjXec+UGzunAU0Hsdvwfv6mjDGrRmP7bBifxvRmP4zV1tPUo1G5ewvf0Yx9+hxp/+UV8TzSWYovOz1/BNr6RyUmolYQr1bjDhS1Q6+t4faTy+DnwsY9+BGrnr15V48fuxPe6rxzfS9VBPPWmsbkZaoMD/WrcIXgq0n9+4k+g9nenvqfG+YZKCCGEGIAFlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA2ypbSabzUMtn8dTJlZW9XbuYAjbIvxurK0tY0vNvmO7oHbmmj6podiHj/31C7jtvX+wB2qBSmxlGR7oU+MtFtN8ysqx9cEf8kDNXoOnXUwvjqrx0voWmLO+rk/KERFJpRJQc9rwOQ6ASS8nn38e5lSU4t8V8OPpGelEVo1fu4ivZbAMt/oXHPrniYhUN1ZDze9y6YI7DXMCxXgNDPbfhNqe3Q9DrbRCt7JUe2tgzvzCItSCJSVQu3buItScPt0OcuxO3V4kIuJxW0ykeh7bzJqbdKuQiEhsVV/DpSV4ukpFHb7XH/DgiSc5Jz7+t85eVuPhELYexaP43iyyqBTf+s4PoVZao08B2ngLX8sn/tvnoPbdP/8G1KZm56D22GP6RKqvfO3PYU7Xjj1QQ/ANlRBCCDEACyohhBBiABZUQgghxAAsqIQQQogBWFAJIYQQA7CgEkIIIQawFQp4ioBp/ubTfwW/bG0xAvNcNt1asJmLwZzPfe4zUFtP4QkDr50+A7V/e163wHzpjz8Pc5wefH737sdt2XY7toncuHRdjdeCCRMiIuO3xqEmm9jONHwbW4w8Pv237T3cCXPW07g1PxzCdpXlhWWouYD7q8iGe/2jK3gCzPIy1kbHp9R4wI+tMc3t2EaUzGCrUEkYTw/K5fUJO9WN+DhiaxmoRSymM8kmzquv0609uSzOSVvY56ansfVhx+5uqMWS+uSYiWk8Redajz4JRURk7x58b+7fuxtqm2ndttR35TbMufNt+6EWi+Pnx7m38PE/9bRuGctn9XUjImJVC/bv2Qc1rwfb7l546WU1vhDDz/t8Aa+PHe3NUDvUoU+CEhGpDOn3Ui6PJw65/Pj+++jXv6SeSL6hEkIIIQZgQSWEEEIMwIJKCCGEGIAFlRBCCDEACyohhBBigC3dHH9HdwfUvvdPT0JtCWx63NaIN+CeGNW7MUVE6prqoNZQjzdN//6/fFuNzy2Nw5yWHe1Qiy2sQO2nP3gKavlNvVMvCzZuFxH5xBOfhdr/8UdfgJrfg89xRbne3Tcxjzft3taBO14lj7v7SkotNpgHXdt5i27utEXnapEbdy1OTU2r8cYGfCv1XL8Btbwd/+ZcDncgfuw3f1ON20O4U3NlfgRqGzHc2SwZfK4qKvaq8QsXz8KcA4ePQ+1W7yDUfvHWOXwc9fo63bsHd+S2W6xFm8V1KQnjIQOnX9U7b/cdOABzhofwdWlrxd3Gdxw9DLXIalSNnzqLr0tdXRPUYinsqBgawce/ktDzAj68SX+4Eg9IGJ+chFppkT4kQ0QkU6cPJ/jVX30/zDl3Aa83BN9QCSGEEAOwoBJCCCEGYEElhBBCDMCCSgghhBiABZUQQggxAAsqIYQQYoAttc0cugu3gH/lv38Fak21DWo8FtNbw0VERofHoDYzPwu1ux5+EGqXz+sb57d1tMKctfEFqL11ErewR2fwpulTYANxG95PX15+/hmoffSTvw61nz/1OtS69uublTc24U36iwPYUhNZwRtmV9Vg+44At9DqHP68snL8X3JxCa+rxeVVNR4uw795M4ftTOLA1p6lFbxRfM/1m2p8fGIY5jQ2YZuIw1kEtZJwGGrrcd1us7mJf3M2j609u/foNhwRkavXeqA2Panb5KLreA08+q6HoTYzhW13Lz33ItRmp/RrFl/WN80XESltwZawdBpblmIWv622Trf/Hd6/C+b0DWPLUnwdW7jm57H9r9ijbzDftKMN5kyPYhtOeRjbbeYW8bN2b/cONR4oxmu71MKqh+AbKiGEEGIAFlRCCCHEACyohBBCiAFYUAkhhBADsKASQgghBmBBJYQQQgywpbaZ4pDeQi0ishJdhloVaJV+59vfgT9vfh5q7/u190FtfhpPM3C7dF/Kyiz+rgunL0BteQlP+FhbxdrhXfvVeNP2epgzt4x/V3ndIah94c+/CLXx67qN4XZfL8ypqsL2F78Xr4+33sCTH6LLwOaS1afyiIgMDmFb1XoCWwTCFbodoakFW6emJieg5nRBSfY//BAWbbrd5oGH3gtTfvH6y1ALleAJKnNz2L7T2KJbcexON8xZW8N2jyELu1uhgO02UzO6zeW+nffCnOvXrkNtR9d2qAVC2E4RjG6qcStbUn8fXh/zk2tQCwXx/eIN6fa0D30IT1dxWlyzvA3bd/76r/4eavPRuBrv6bkGc7wuPO2pgG9p2bMH2zKXVpbU+MjwKMw5tPcO/GUAvqESQgghBmBBJYQQQgzAgkoIIYQYgAWVEEIIMQALKiGEEGIAFlRCCCHEAFtqm0lF9BZqEZE/fOJzUHMWdLvK6vw0zNnehSdrjA8OQM1XGoSa16H3bDfW18Gcfn8AauWhKqjlMnmo1TXo39e1uxPmeBbwKBqHG/eipxP4moXL9EkNFeXVMOfWFWypyWfwcRTwUBZJxHWby6qF9ajIg60PmXU8GcTt060FS0vY9pWwOIclQWxXyVtMbKmoK1Hjw+N4Ysjb3o1tZkvzeLrKudNvQg39JW/ehu+/KQsbji+ArSA2B/7/f7hGtzPlNrHdo7t7J9Ru3bgBteIAvmZuT0yNz8/iCVd5D37mXLt6C2qd7fgcV+V1q6HFKZRcbgNqs/P4mv3+7/8O1K723NaPQ7AF6p4T2OpUZOEzG7jVB7W6St2u95d/+9cw5wtfwL+rTPRnLd9QCSGEEAOwoBJCCCEGYEElhBBCDMCCSgghhBiABZUQQggxAAsqIYQQYgCb1QQH03zm7g/CLzu+/36YZ8vqre9lpbh9vaFObxsXESku0ScxiIgkNxNQc/r0KQjJtD5hQkQkGCiFmt2GpzskY/g4FlZ0i0bWhu0eR+4/DLV8EbarFLmxvWS0R29TX5nDFpLFyQWoTU9gzefD9qONuN7u77HIud6L7TuVFXgiTjKlf9f0xDjMaW1ugFrQi9dAaRgf/+EjB9S4zY8tGL1XLuHjKMPf1dbUCLXiEn19FFscx6VLV6GWiOJJP3YHdvn1XL+ixg8exZOU7E5sTduzdxfUohFsx+q7oduWlqbwRKrlNLaCtFSXQ81hx8/uZFKfUnPkxEGYY7cwUQZK8PpYWlqBWnmZbqErOLCNb20VTI8SkWtXb0Ktr68ff+a6bmcaGRqCOd/5x29DreqRFvWhyTdUQgghxAAsqIQQQogBWFAJIYQQA7CgEkIIIQZgQSWEEEIMsKVdvv/9Q/8VflnQhTt27aJ34zU34m7MinK8ybarCEoSia1CrbxS71jzh/SNykVEcjncQZu12AB/aQkfR1Wtvqm+z6J72RPCP3o5hrtyG+qaoLY4vajG7bjpWX7x+nmoxdZTUAuV4K5tj8+rxicm8Ibvy1HcqXn+/EWo7dmtd392tbfDHKfV39Y83gA/ENR/l4hIC+gcjllsOl5hsfF8e3cb1M6dPg21YLF+38YtNqXf1t4FtfUlvTtVROSrf4E3Mv/0Zz6lxs9duABzXEW4rfWhRx6EmrdY7/YXEYks6x2vmQ3ctf/mVYthHRZTIWwF/PzYzOgDGarqcNdwwMI14fPjtVNegYd8OBz6cyebweu+txdvcn/5Cu4Qz+fxjTYHnlX1DbgDf2kBP4P//q1vscuXEEII+WXBgkoIIYQYgAWVEEIIMQALKiGEEGIAFlRCCCHEACyohBBCiAEstkM2z8zkDNQyFbolRUQkHNQ3Zl5d11vDRURW1pag1thUCzW7HVtP+m7qmy8XeXFLeaAEb44ftzj+0nJsE0kl9A3E83m8Of7yCt50vK6lHmqLU5NQqwAbX0cWsfUhEMTn1+HE1p5cHtswWtq3qfFYAltjzlw+B7U9+7qhVocsAnZsYRiaHIWay4k3Cd8ebIVafdd2NT4wom/OLiKymsDX5fz5M1DbBr5LRKTIqW/svriA77/yenz/DfViC0lrOz4f167cUuM917AFw8oyeOYUttt89Dd/DWqdHfq5+unzL8Ccut17oWZLYitZXR1+Zvq8wK6Sx562nOB7LBrD99KQxQbzdqdu/aqqroQ5x07ggQb7Du6G2moUb6pfUabbY/7wi38Ec2w5bI9C8A2VEEIIMQALKiGEEGIAFlRCCCHEACyohBBCiAFYUAkhhBADsKASQgghBtjSaTM//PI/wS8bG8b2jHAwrMbb2ltgTjyCJwWglnIREZ/bDbV1MKEkHtuAOds6seVgZm4OauUV2DbjdOlWC38At3nnC7j9Pp7G9p2mRjyNIRXXbTqrEdy+vryItb7bw1Cz2fF/v9/4+Mf041jFNpHMJra59PXq9igREXtBnx40MzEOc2qa6qB29/13Qe1nP30Kah/66EfU+Kk3T8Icr9fComOxTv0+bHVaXoqo8fISPNXktRfxMfq9eHLTwC1szzj1C90GVVfdCHM24thK5nDgKVFFbrwWf+d39bW4EtPPk4jIagofR2WZ/uwTEfEV4edYJKqv/ZIgtvjlC3gCjNuFn4v5PD5XsZRu03FYvMplcvh51NyCn0cl5XjN2UV/Nk6NYivn8qA+OUhE5M4vvo3TZgghhJBfFiyohBBCiAFYUAkhhBADsKASQgghBmBBJYQQQgzAgkoIIYQYYEunzTz8ngehdumti1BLJfQpCBW12FriDehTMERENjdwm/rqKrZ1uN1667Uzg61HSxafZ3fi0+/16VMaRERyeb29fXZhHuZUV+KpNyFfCGrRpRjUZqb1lvPWFn36i4jIehTbd5KpBNS2d3ZBbS2qWxKcbtzO7/VgK8hGDFuuGhv0tv29ux+BOb84dxpqV3ouQe3gsX1QGxrRp6j4S/G6qa7AtoK8DVsmegf0SS4iIrv+r/bu5jfKKorj+Jl22pkyM60V+hYo1ALWULDQVhMKLiRoIyoJKxM0LFm56MYaI1uXLv0HNHErEklcNEaDBloZBQQJtrb0BUrpDC20tDNtZ+o/cH5PXNx09f0s78mdeWael5MnOefeA/5OKZP/6ja4QsQOKsf6umWsXNJtPw9zOXf89l9695qahL4GWna2yFhbxG5VE1P+7+493ivnZLN/yNjUhN6pqL5et9RUJ/znXzKpW+sWF3Vrz8K83m3m0axuL1kr+e9syaR+9vW/e1zGxsbuyFhtOi1jN+/598uxE6fknC8unJGxNz55xx3nDRUAgABIqAAABEBCBQAgABIqAAABkFABAAiAhAoAQABb2jZTuU1/XWdPh4w9zfmtJy9kdNtMKqVL7BcXdTtFslaXlW8U/daCclx/XtPORhmrrdVl3lURO0nMPvTbVRI1eieJirj+XffHJ2Rs6ZluMdoo+jtJVFU/lnOiLrnGhmYZa23dJWMjw9fc8Z7XdAtGcd3fKcfMLJnS7RSHujrd8QfinJiZ7e/U1/bLnftkbDmijSge99siUiv6uonpy9QqRJuFmdnBI7p9JybO5+Ocbj3q6DwgY58NDspYd8/rMrZW9s9nPKmfAy91tMnYzNSkjA0MnpexZMq/z7bV6Xu9u/uQjOXzuiVlR8TuKvnHfhtRapt+RjyP2DVrraDb52Kmd6K5e/euO55O6+dR7a/6GA8f0bsiTY9Py1ivaLv79suv5JyBjwdkTOENFQCAAEioAAAEQEIFACAAEioAAAGQUAEACGBLq3zH7vgLFJuZtTQ0yVjFZtkdL2/oSs2WXboCzuJ6XkVMVzs+yfvVxnvadeXZ8pKujtuM+Yv+m5mVyiUZS9T4py0RUam5uuJX5JqZTU/pRfUfRSyKvbt5pzu+WdLHsb6uj+P6sF4kfHtjg4x1dPj/f3VCV7xeuepXBpuZXbr8o4xlb95wx8+cfl/OeV7UldLZ61kZ646oUp6c8CuzdzTr/+l5RNVwc+tuGcvNzsnYxW++dsczSb3hQtdbeiOBC59/KmPff/eDjPWfPOmOP3lVV8neuq2vtw8/OC1j8Qr9/Mg98u+l0dFFOad5u372lQr6u2bGdSXy0tKyO15TrSty97S1y1hRH4bNzunq2tqM3+UQr9Al57kZvaHIQoM+n9WVepOSW78Nu+P9fUflnKFrV2Ss106447yhAgAQAAkVAIAASKgAAARAQgUAIAASKgAAAZBQAQAIYEvbZibHZ2VsdkYvqN531F8UuxBRUp69/buMVaf1z963Wy9knnvmLzi9XuW39ZiZrZR1q0Iioq1jddUvezcza9rll6Ln5/SC5OslfYyHj+nS8Z+HrspYPFPvjo9k/dYSM7N1saC+mdnBLr0Ie7GgW4xiZb8Ef2ZyRs5pbPFbfszMzp47J2PXhv3r6smKbo2py+jFvh/MzctY0XTrVEOrf/zpiEXYG5J6g4HLFy/J2N69+2WsXbQsNWd0+87Sgr5Oi8v6fnlln27ruDHit0FF/ffnPzorY1U1+t7cjLinn85PueOVSd2uUi7oNpEX6/QGIBP3xmSsMu5/359Z/Vx8u/+UjK2u6Pa/oV9+krGNDf84jnT1yjnzeb1I/+jf/8jYm30HZWz6vn9eRhb173rvuG7vUnhDBQAgABIqAAABkFABAAiAhAoAQAAkVAAAAiChAgAQQGxzU6/QDwAA/h/eUAEACICECgBAACRUAAACIKECABAACRUAgABIqAAABEBCBQAgABIqAAABkFABAAiAhAoAQAAkVAAAAiChAgAQAAkVAIAASKgAAARAQgUAIAASKgAAAZBQAQAIgIQKAEAAJFQAAAIgoQIAEAAJFQCAAEioAAAEQEIFACCA/wCvtQQ0JivEZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "noise = np.random.normal(0,1,size=(1,100))\n",
    "imgs = saved_actor.predict(noise)\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.axis('off')\n",
    "plt.imshow((imgs[0]*1* 127.5+127.5).astype(np.int32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Images from PCam dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper methods ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_folders(name):\n",
    "    if not os.path.exists('results'):\n",
    "        os.mkdir('results')\n",
    "    if not os.path.exists('results/'+name):\n",
    "        os.mkdir('results/'+name)\n",
    "        \n",
    "def save_loss(name, epoch, Dloss, Gloss):\n",
    "    path = 'results/'+name+'/loss'\n",
    "    if not os.path.exists(path):\n",
    "        os.mkdir(path)\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.plot(Dloss,label='Dsicriminiative loss')\n",
    "    plt.plot(Gloss,label='Generative loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.savefig(path+'/loss_%d.png' % epoch)\n",
    "    \n",
    "def save_samples(folder, w, h, c, epoch, G,example=16, dim=(10,10),figsize=(10,10), randomDim=100):\n",
    "    noise = np.random.normal(0,1,size=(example,randomDim))\n",
    "    generatedImage = G.predict(noise)\n",
    "    \n",
    "    plt.figure(figsize=figsize)\n",
    "    \n",
    "    for i in range(example):\n",
    "        plt.subplot(dim[0],dim[1],i+1)\n",
    "        plt.imshow((generatedImage[i]* 127.5+127.5).astype(np.int32),interpolation='nearest',cmap='gray')\n",
    "        '''drop the x and y axis'''\n",
    "        plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    path = 'results/'+folder+'/samples'\n",
    "    if not os.path.exists(path):\n",
    "        os.mkdir(path)\n",
    "    plt.savefig(path+'/epoch_%d.png' % epoch)\n",
    "    plt.close()\n",
    "    \n",
    "def save_models(name, epoch, d=None, g=None):\n",
    "    path = 'results/'+name+'/models'\n",
    "    if not os.path.exists(path):\n",
    "        os.mkdir(path)\n",
    "    g.save(path+'/G_%d.h5' % epoch)\n",
    "    d.save(path+'/D_%d.h5' % epoch)\n",
    "    \n",
    "def get_model_memory_usage(batch_size, model):\n",
    "    shapes_mem_count = 0\n",
    "    for l in model.layers:\n",
    "        single_layer_mem = 1\n",
    "        for s in l.output_shape:\n",
    "            if s is None:\n",
    "                continue\n",
    "            single_layer_mem *= s\n",
    "        shapes_mem_count += single_layer_mem\n",
    "\n",
    "    trainable_count = np.sum([K.count_params(p) for p in set(model.trainable_weights)])\n",
    "    non_trainable_count = np.sum([K.count_params(p) for p in set(model.non_trainable_weights)])\n",
    "\n",
    "    number_size = 4.0\n",
    "    if K.floatx() == 'float16':\n",
    "         number_size = 2.0\n",
    "    if K.floatx() == 'float64':\n",
    "         number_size = 8.0\n",
    "\n",
    "    total_memory = number_size*(batch_size*shapes_mem_count + trainable_count + non_trainable_count)\n",
    "    gbytes = np.round(total_memory / (1024.0 ** 3), 3)\n",
    "    return gbytes\n",
    "\n",
    "\n",
    "# class CustomDataProvider:\n",
    "#     def __init__(self, batch_size):\n",
    "#         self.batch_size = batch_size\n",
    "#         self.file = h5py.File('camelyonpatch_level_2_split_train_x.h5', 'r')\n",
    "#         self.dataset = self.file['x']\n",
    "#         self.input_shape = self.dataset[0].shape\n",
    "#         self.image_number = self.dataset.shape[0]\n",
    "        \n",
    "#     def sample(self):\n",
    "#         random_index = np.random.randint(0, self.image_number - self.batch_size)\n",
    "#         return (self.dataset[random_index : random_index +  self.batch_size] - 127.5) / 127.5\n",
    "        \n",
    "#     def close(self):\n",
    "#         if self.file:\n",
    "#             self.file.close()\n",
    "#         self.file = None\n",
    "        \n",
    "#     def __del__(self):\n",
    "#         self.close()\n",
    "\n",
    "class PcamDataProvider:\n",
    "    def __init__(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.file = h5py.File('X.hdf5', 'r')\n",
    "        self.dataset = self.file['X']\n",
    "        self.input_shape = self.dataset[0].shape\n",
    "        self.image_number = self.dataset.shape[0]\n",
    "        \n",
    "    def sample(self):\n",
    "        random_index = np.random.randint(0, self.image_number - self.batch_size)\n",
    "        return self.dataset[random_index : random_index +  self.batch_size]\n",
    "        \n",
    "    def close(self):\n",
    "        if self.file:\n",
    "            self.file.close()\n",
    "        self.file = None\n",
    "        \n",
    "    def __del__(self):\n",
    "        self.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wasserstein loss ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wasserstein_loss(y_true,y_pred):\n",
    "    return K.mean(y_true*y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final critic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_critic_final(width, height, channels, kernel_size=3):\n",
    "    \"\"\" Declare discriminator \"\"\"\n",
    "\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Conv2D(32, kernel_initializer=initializers.RandomNormal(mean=0,stddev=0.2), \n",
    "                     kernel_size=kernel_size, strides=2, \n",
    "                     input_shape=(width, height, channels), padding=\"same\"))\n",
    "    model.add(LeakyReLU(alpha=0.1))\n",
    "    model.add(Dropout(0.15))\n",
    "\n",
    "    model.add(Conv2D(64, kernel_size=kernel_size, strides=2, padding=\"same\",\n",
    "             kernel_initializer=initializers.RandomNormal(mean=0,stddev=0.2)))\n",
    "    model.add(LeakyReLU(alpha=0.1))\n",
    "    model.add(Dropout(0.15))\n",
    "\n",
    "    model.add(Conv2D(128, kernel_size=kernel_size, strides=2, padding=\"same\",\n",
    "             kernel_initializer=initializers.RandomNormal(mean=0,stddev=0.2)))\n",
    "    model.add(LeakyReLU(alpha=0.1))\n",
    "    model.add(Dropout(0.15))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(256,\n",
    "             kernel_initializer=initializers.RandomNormal(mean=0,stddev=0.2)))\n",
    "    model.add(LeakyReLU(alpha=0.1))\n",
    "    model.add(Dropout(0.15))\n",
    "\n",
    "    model.add(Dense(1,\n",
    "             kernel_initializer=initializers.RandomNormal(mean=0,stddev=0.2)))\n",
    "\n",
    "    print('Critic')\n",
    "    model.summary()\n",
    "#     model.compile(loss=wasserstein_loss,optimizer=RMSprop(lr=0.00005))\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final actor ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_actor_final(width, height, channels, latent_dim=100):\n",
    "    \n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(Dense(32*int(width/4)*int(height/4), input_dim=latent_dim,\n",
    "                    kernel_initializer=initializers.RandomNormal(mean=0,stddev=0.2)))\n",
    "#     model.add(BatchNormalization(momentum=0.9, epsilon=0.00002))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Reshape((int(width/4),int(height/4), 32)))\n",
    "\n",
    "    model.add(UpSampling2D(interpolation='nearest'))\n",
    "    model.add(Conv2D(64, kernel_size=3, strides=1, padding=\"same\"))\n",
    "#     model.add(BatchNormalization(momentum=0.9, epsilon=0.00002))\n",
    "    model.add(Activation('relu'))\n",
    "\n",
    "    model.add(UpSampling2D(interpolation='nearest'))\n",
    "    model.add(Conv2D(48, kernel_size=4, strides=2, padding=\"same\"))\n",
    "#     model.add(BatchNormalization(momentum=0.9, epsilon=0.00002))\n",
    "    model.add(Activation('relu'))\n",
    "    \n",
    "    model.add(UpSampling2D(interpolation='nearest'))\n",
    "    model.add(Conv2D(32, kernel_size=3, strides=1, padding=\"same\"))\n",
    "#     model.add(BatchNormalization(momentum=0.9, epsilon=0.00002))\n",
    "    model.add(Activation('relu'))\n",
    "\n",
    "    model.add(UpSampling2D(interpolation='nearest'))\n",
    "    model.add(Conv2D(16, kernel_size=4, strides=2, padding=\"same\"))\n",
    "#     model.add(BatchNormalization(momentum=0.9, epsilon=0.00002))\n",
    "    model.add(Activation('relu'))\n",
    " \n",
    "    model.add(Conv2D(8, kernel_size=3, strides=1, padding=\"same\"))\n",
    "#     model.add(BatchNormalization(momentum=0.9, epsilon=0.00002))\n",
    "    model.add(Activation('relu'))\n",
    "\n",
    "    model.add(UpSampling2D(interpolation='nearest'))\n",
    "    model.add(Conv2D(16, kernel_size=4, strides=2, padding=\"same\"))\n",
    "#     model.add(BatchNormalization(momentum=0.9, epsilon=0.00002))\n",
    "    model.add(Activation('relu'))\n",
    "    \n",
    "    model.add(Conv2D(channels, kernel_size=3, strides=1, padding=\"same\"))\n",
    "    model.add(Activation(\"tanh\"))\n",
    "    \n",
    "    print('Actor')\n",
    "    model.summary()    \n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training method ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(name, critic, actor, sampler_provider,\n",
    "          batch_size=64,\n",
    "          epochs=3000, \n",
    "          randomDim=100, \n",
    "          do_save_loss=True, \n",
    "          do_save_models=True, \n",
    "          do_save_samples=True, \n",
    "          batchCount=128, \n",
    "          epoch_offset=0):\n",
    "    \"\"\"\n",
    "    :name: name of the directory, which will store all data\n",
    "    :critic: model for critic\n",
    "    :actor: model for critic\n",
    "    \"\"\"\n",
    "    \n",
    "    init_folders(name)\n",
    "    \n",
    "    sampler = sampler_provider(batch_size)\n",
    "\n",
    "    batchsize = batch_size\n",
    "\n",
    "    width, height, channels = sampler.input_shape\n",
    "\n",
    "    critic.compile(loss=wasserstein_loss,optimizer=RMSprop(lr=0.00005))\n",
    "    generator = actor\n",
    "    discriminator = critic\n",
    "\n",
    "    discriminator.trainable = False\n",
    "    gan_input = Input((randomDim,))\n",
    "    x = generator(gan_input)\n",
    "    gan_output = discriminator(x)\n",
    "\n",
    "    gan = Model(gan_input,gan_output)\n",
    "    gan.compile(loss=wasserstein_loss,optimizer=RMSprop(lr=0.00005))\n",
    "\n",
    "    Dloss = []\n",
    "    Gloss = []\n",
    "\n",
    "    print('Actor', get_model_memory_usage(batchsize, generator), 'GB')\n",
    "    print('Critic', get_model_memory_usage(batchsize*2, discriminator), 'GB')\n",
    "    \n",
    "    print('Epochs',epochs)\n",
    "    print('Bathc size',batchsize)\n",
    "    print('Batches per epoch',batchCount)\n",
    "\n",
    "    for e in range(epoch_offset+1,epochs+1):\n",
    "        for _ in tqdm(range(batchCount), desc=\"epoch \"+str(e)):\n",
    "            imageBatch = sampler.sample()\n",
    "            \n",
    "            #Get a random set of input noise and images\n",
    "            noise = np.random.normal(0,1,size=[batchsize,randomDim])\n",
    "            generatedImages = generator.predict(noise)\n",
    "            \n",
    "            X = np.concatenate([imageBatch,generatedImages])\n",
    "            \n",
    "            yDis = np.ones(2*batchsize)\n",
    "            \n",
    "            yDis[:batchsize] = -1\n",
    "            \n",
    "            #Train critic\n",
    "            discriminator.trainable = True\n",
    "            \n",
    "            ### Clip weights ###\n",
    "            weights = [np.clip(w, -0.01, 0.01) for w in discriminator.get_weights()]\n",
    "            discriminator.set_weights(weights)\n",
    "            \n",
    "            dloss = discriminator.train_on_batch(X,yDis)\n",
    "            \n",
    "            #Train actor\n",
    "            noise = np.random.normal(0,1,size=[batchsize,randomDim])\n",
    "            yGen = np.ones(batchsize) * -1\n",
    "            discriminator.trainable = False\n",
    "            gloss = gan.train_on_batch(noise,yGen)\n",
    "\n",
    "        if do_save_loss:\n",
    "            Dloss.append(dloss)\n",
    "            Gloss.append(gloss)\n",
    "\n",
    "        if e ==1 or e % 10 == 0:            \n",
    "            if do_save_loss:\n",
    "                Dloss.append(dloss)\n",
    "                Gloss.append(gloss)\n",
    "                save_loss(name, e, Dloss, Gloss)\n",
    "            if do_save_samples:\n",
    "                save_samples(name, width, height, channels, e, generator, 16, dim=(4,4))\n",
    "            if do_save_models:\n",
    "                save_models(name, e, d=discriminator, g=generator)\n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start training ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Critic\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 128)         73856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               2097408   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 2,190,913\n",
      "Trainable params: 2,190,913\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Actor\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 8192)              827392    \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 32, 32, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2 (None, 64, 64, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 32, 32, 48)        49200     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 32, 32, 48)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 64, 64, 48)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 64, 64, 32)        13856     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2 (None, 128, 128, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 64, 64, 16)        8208      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 64, 64, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 64, 64, 8)         1160      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 64, 64, 8)         0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_5 (UpSampling2 (None, 128, 128, 8)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 64, 64, 16)        2064      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 64, 64, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 64, 64, 3)         435       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 64, 64, 3)         0         \n",
      "=================================================================\n",
      "Total params: 920,811\n",
      "Trainable params: 920,811\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# critic = load_model('results/WGAN_multi_gpu_critic1/models/D_170.h5', custom_objects={'wasserstein_loss': wasserstein_loss})\n",
    "# actor = load_model('results/WGAN_multi_gpu_critic1/models/G_170.h5', custom_objects={'wasserstein_loss': wasserstein_loss})\n",
    "critic = create_critic_final(64, 64, 3)\n",
    "actor = create_actor_final(64, 64, 3, latent_dim=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train('WGAN_old', critic, actor, PcamDataProvider, batch_size=64, epochs=10000, randomDim=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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