deconvolution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D-deconvolution with prior learned GAN\n",
    "\n",
    "## create deconvolution model\n",
    "tf.fft_shift needs even number of dims, so eventually cut an array (then create new otf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from glob import glob\n",
    "from skimage import io\n",
    "import json \n",
    "import pprint\n",
    "from keras import optimizers, losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "sys.path.insert(0, './scripts')\n",
    "\n",
    "import helper as hp\n",
    "import deconvolution\n",
    "from NanoImagingPack.microscopy import PSF3D\n",
    "from NanoImagingPack.view import view\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### load and image, GAN model, reconstruct the OTF and create deconvolution model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file: ./models/2018_12_7/09_05_39_gene.h5\n",
      "Loaded model from disk\n",
      "file: ./models/2018_12_7/09_05_37_disc.h5\n",
      "Loaded model from disk\n"
     ]
    }
   ],
   "source": [
    "day = '2018_12_7'\n",
    "filename_D = '09_05_37_disc'\n",
    "filename_G = '09_05_39_gene'\n",
    "# filename_C = '13_53_02_combined'\n",
    "\n",
    "generator = hp.model_loader(day, filename_G)\n",
    "discriminator = hp.model_loader(day, filename_D)\n",
    "# combined = hp.model_loader(day, filename_C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 64, 32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with open('./models/{0}/hyperparameter_13_52_55.json'.format(day)) as json_data:\n",
    "#     json_file = json.load(json_data)\n",
    "#     otf_info = json_file['OTF']\n",
    "#     hparam = json_file['hyper_parameter']\n",
    "#     pprint.pprint(otf_info)\n",
    "#     pprint.pprint(hyperparameter)\n",
    "    \n",
    "# vol_original = (hparam['RESIZE']['width'],\n",
    "#                 hparam['RESIZE']['height'],\n",
    "#                 hparam['RESIZE']['depth'])   \n",
    "\n",
    "vol_original = (64,64,32)\n",
    "\n",
    "# CAUTION: OTF is already shifted\n",
    "otf = PSF3D(im=(64,64,32))\n",
    "# otf = PSF3D(im=vol_original,\n",
    "#             NA=otf_info['NA'],\n",
    "#             wavelength=otf_info['wavelength'],\n",
    "#             ret_val=otf_info['ret_val'])\n",
    "\n",
    "otf = otf/np.max(np.abs(otf))\n",
    "otf = np.fft.fftshift(otf)\n",
    "\n",
    "otf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# adam = optimizers.Adam(hparam['ADAM_OPTIMIZER_LEARNRATE'], 0.5)\n",
    "\n",
    "# discriminator.compile(loss=hparam['D_LOSS'], optimizer=adam, metrics=['accuracy'])\n",
    "# combined.compile(loss=hparam['COMBINED_LOSS'], loss_weights=hparam['LOSS_WEIGHTS'], optimizer=adam)\n",
    "# generator.compile(loss='mae', optimizer=adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/3D/dub\\dub80.tif\n",
      "(768, 512, 33)\n",
      "(64, 64, 32) \t -1.0 \t 0.4980392156862745\n"
     ]
    }
   ],
   "source": [
    "# load an image\n",
    "\n",
    "# v_path = np.random.choice(glob('./data/3D/dub/*'), size=1)[0]\n",
    "v_path = './data/3D/dub\\dub80.tif'\n",
    "print(v_path)\n",
    "vol = np.swapaxes(io.imread(v_path), 0, 2)/127.5 - 1.\n",
    "print(vol.shape)\n",
    "vol = hp.cut_volume(vol, vol_original)\n",
    "print(vol.shape,'\\t', np.min(vol),'\\t', np.max(vol))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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+Vq1cuTLGaIeb9Cyg60wlQkOez4Q+M417GjtLly6NsRUrVjSP0y48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\nJEkN/z/y5l0ZlhOc3QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23c47670ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(12,8))\n",
    "plt.imshow(np.max(vol, axis=2), cmap='gray', interpolation='none'); \n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.title('Measurement', fontsize=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 56s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# execute prediction with generator\n",
    "t = np.expand_dims(np.expand_dims(vol, axis=0), axis=-1)\n",
    "x = generator.predict(t).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r1w+nn376AbZBgwbRY7du3VqpEGPHjk1s5YbNOfXUUxPb9u3pS5aHDRuW2ABg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VdffXUVo/GQOWyRexS5YVEfy8pEkJsUzUWDuLjRGMvKRG1K8yPtP3M3ozjlq2zcUp1ozqXckrnA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23c4a67eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(12,8))\n",
    "plt.subplot(121); \n",
    "temp = plt.imshow(np.max(t.squeeze(), axis=2), cmap='gray', interpolation='none'); \n",
    "hp.colorbar(temp); plt.xticks([]); plt.yticks([]); plt.title('acquired stack', fontsize=25, loc='right');\n",
    "plt.subplot(122); \n",
    "temp = plt.imshow(np.max(x, axis=2), cmap='gray', interpolation='none'); \n",
    "hp.colorbar(temp); plt.xticks([]); plt.yticks([]); plt.title('volume generated with GAN', fontsize=25, loc='right')\n",
    "plt.subplots_adjust(left=0.02, wspace=0.15, top=0.92)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+XNq9u07V6grqnMy1v3379snYHXfcIWOq5MGdS7UaTYS/X9x1qrjr3pUauP67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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a82b1b1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(12,8))\n",
    "plt.imshow(np.max(x, axis=2), cmap='gray', interpolation='none'); \n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.title('Generator-Output', fontsize=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vol:\t (64, 64, 32)\n",
      "otf:\t (64, 64, 32)\n",
      "x\t (64, 64, 32)\n",
      "-1.0 -0.28627450980392155\n"
     ]
    }
   ],
   "source": [
    "# check shapes for deconvolution model\n",
    "# CAUTION: check if the axes of vol must be swaped\n",
    "#     if yes: swap it, before commit it to the deconvolution-model\n",
    "\n",
    "print('vol:\\t', vol.shape)\n",
    "print('otf:\\t', otf.shape)\n",
    "print('x\\t', x.shape)\n",
    "\n",
    "print(np.min(vol), np.max(vol))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47519"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# release GPU\n",
    "import gc\n",
    "\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15f7d1e7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(generator.layers)\n",
    "t = generator.layers[-2].get_weights()[0].squeeze()\n",
    "plt.hist(t.flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## declare iterative Deconvolution model and build graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Successfully build tensorflow-graph for iterative Deconvolution\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1661d924b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0 ,\tloss: 61.69987106323242 , D: 0.16899999976158142 ,variance: 0.14000000059604645\n"
     ]
    },
    {
     "data": {
      "image/png": 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HY7hm9DGhd8eQeKZ06q/H8EXdD46xz47zqY/xS6tvpH9nvAFzTJrZBv/7xxPjeJy6qN41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\nZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1666d585e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1661d962dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 10 ,\tloss: 61.334049224853516 , D: 0.16899999976158142 ,variance: 0.13899999856948853\n",
      "epoch: 20 ,\tloss: 60.97003173828125 , D: 0.16899999976158142 ,variance: 0.13899999856948853\n",
      "epoch: 30 ,\tloss: 60.60782241821289 , D: 0.16899999976158142 ,variance: 0.1379999965429306\n",
      "epoch: 40 ,\tloss: 60.24748229980469 , D: 0.16899999976158142 ,variance: 0.1379999965429306\n",
      "epoch: 50 ,\tloss: 59.88892364501953 , D: 0.16899999976158142 ,variance: 0.13699999451637268\n",
      "epoch: 60 ,\tloss: 59.532264709472656 , D: 0.16899999976158142 ,variance: 0.13699999451637268\n",
      "epoch: 70 ,\tloss: 59.17744064331055 , D: 0.16899999976158142 ,variance: 0.13600000739097595\n",
      "epoch: 80 ,\tloss: 58.8245735168457 , D: 0.16899999976158142 ,variance: 0.13600000739097595\n",
      "epoch: 90 ,\tloss: 58.47356033325195 , D: 0.16899999976158142 ,variance: 0.13500000536441803\n",
      "epoch: 100 ,\tloss: 58.12446975708008 , D: 0.16899999976158142 ,variance: 0.13500000536441803\n",
      "epoch: 110 ,\tloss: 57.777320861816406 , D: 0.16899999976158142 ,variance: 0.1340000033378601\n",
      "epoch: 120 ,\tloss: 57.43211364746094 , D: 0.16899999976158142 ,variance: 0.1340000033378601\n",
      "epoch: 130 ,\tloss: 57.08885192871094 , D: 0.16899999976158142 ,variance: 0.13300000131130219\n",
      "epoch: 140 ,\tloss: 56.74757766723633 , D: 0.16899999976158142 ,variance: 0.13300000131130219\n",
      "epoch: 150 ,\tloss: 56.40830612182617 , D: 0.16899999976158142 ,variance: 0.13199999928474426\n",
      "epoch: 160 ,\tloss: 56.07099533081055 , D: 0.16899999976158142 ,variance: 0.13199999928474426\n",
      "epoch: 170 ,\tloss: 55.73572540283203 , D: 0.16899999976158142 ,variance: 0.13099999725818634\n",
      "epoch: 180 ,\tloss: 55.4024772644043 , D: 0.16899999976158142 ,variance: 0.13099999725818634\n",
      "epoch: 190 ,\tloss: 55.07129669189453 , D: 0.16899999976158142 ,variance: 0.12999999523162842\n",
      "epoch: 200 ,\tloss: 54.74213790893555 , D: 0.16899999976158142 ,variance: 0.12999999523162842\n",
      "epoch: 210 ,\tloss: 54.415035247802734 , D: 0.16899999976158142 ,variance: 0.12999999523162842\n",
      "epoch: 220 ,\tloss: 54.09003448486328 , D: 0.16899999976158142 ,variance: 0.1289999932050705\n",
      "epoch: 230 ,\tloss: 53.767147064208984 , D: 0.16899999976158142 ,variance: 0.1289999932050705\n",
      "epoch: 240 ,\tloss: 53.44631576538086 , D: 0.16899999976158142 ,variance: 0.12800000607967377\n",
      "epoch: 250 ,\tloss: 53.127647399902344 , D: 0.16899999976158142 ,variance: 0.12800000607967377\n"
     ]
    },
    {
     "data": {
      "image/png": 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gDNeMPib07hgSz5RO/XoMX9T9+Bj7+jif+hi/tPpm+nfGGzDHpJlt8H90PDGOxsmL6t0T\n2yyNiP9i3O7wOAGeHwfz52PiW+BzTcZp2/3VcR9TpRMeiPFLalNtSd5zVkT81zF8z2JqQvybY4v5\nJZErpr3v274bAtuXjn+Ofc2rb8Ztb4jhQvRUDH9TPxqDAfbpGGyX7PsdF0TEfxnDBfNoDBetZ8aT\n965k+xUR8d+NJ/jL43u+HsM3+LOSNndHPYlcEMN3Lz4VQ5I7No73tyLif42Ia2BfPxXDBX/qwvtv\nLjbzGedp+/nAOFYvjp97X0TcFXOXPVkVw5dUH42TZXmeiNHyq8zLadttjohfiuGb+sfHf0/HkEh+\nOiLWJe24NAadd8+478fGMV4cb/IkMm0e3xtDMpgq2/LfxPAl1LQt4/tWx3C3siOGc+6ZGL70eF6c\nrLYx48ur43vfP863HTEksf3jOP5GDH/CLpUr+k7868aGi4jIaWQst/9UDAn4wr7vS6bgm5Xv1mci\nIiJvOsZnRDcn8fUx3JUtiojf/F5JIBHhnYiIyEIxfknxtRh068dieA64PiLeEYMM82gM9ctav6T6\npsMkIiKyQIz22j+I4cvAl8VgXL0SwzOpfxERv9TP//s23xWYREREpBmfiYiISDOlb9XOozjYvDjr\nrDx3LVq0KI2fe+65afycc/IvOL/lLW9J43TXdexYfneZteetb31rsiXztrfllSuojQS1ff5fTJ6d\n6n5O9/ZVFuqOurIfOqbqsdJnUvyNN7DAdMqJE/kzXIpX+7La/tdffz2Nv/baa2n88OH88QG1n/ZP\nVNpfPdaFguYUXTPXrUuLEsf555+fxvfsmVk8+NixY/Hyyy/POZnPSO0suvhfeOGMKhYREXHttdeW\n4uvXr0/jNLnuv//+NJ4lgMsvz8tjUWJ817velcY3bJhRFWFW6MKxeHE+hDS5aLLTfui4qpOa4rQf\n+tzqhYmgC9Crr746I1Y9Vprf1YveK6/ktTyPHj2axqnP6JekF198MY1nfTAbNDfpuHbv3l2Kf+5z\nefm348ePp/FDh7KKRNwemlMvvzyz4AH1zUIll+r5uXRpXoLsZ3/2Z9P4rbfemsY/9rGPzYh99rPz\nqzjvn7NERKQZk4iIiDRjEhERkWZKiu9CPVinZxbUltWr88KVt9xySxqnh9/0t+qVK/PFwrK/N15w\nwQXpttVnB9nfW2fjoouyCtJ8TPQAjf4uT3/Xpr8B037o4Wj179f0N2BqDz0noL/709/Hs3GhZx+0\n7+pzKhJHzjtvRvHlWT+X+obGhJ6VPP7442n8yJEjaZyg41q+fHkap7lM5xaN+VNPPZXGDx48mMZp\nLmf7rz5XWSiq+6fr1F133ZXGX3pp5hppX/3qV+PQoUNzPlj3TkRERJoxiYiISDMmERERacYkIiIi\nzZhERESkmfI31jPbiKwTMgToK/kEmUlUDoHsmBUrVpT2k1kb+/btS7cl+4hMMbI8yETJ7IkI/sYq\nmTdUboXMHoqTIUTtpG9fk/FD8aqZRAZPxUajz6RjojEhU4/sKYrT3Dn77LPTOM17Om+XLVuWxjdt\n2pTGqY/JvKN20rlC/UDnStWOq1Q7WKiSNlXoc8ngo+ofNGc3btw4Izbf0kzeiYiISDMmERERacYk\nIiIizZhERESkGZOIiIg0U7azMsPh+uuvT7ddu3ZtGqfaOWRPrFmzJo1v3rw5jVPNHqrZRRbCCy+8\nMCNGxgkZPHRMZBORcUJ9RnYGHRO1h4wWgo6XrCdqDxl81XZW18ygeNb/ZDeRxURxso/ImKE2kk1I\ncbKnyJK65JJL0viqVavSOJl09Ll0vDTmZGfR3KeF4KrrvFx11VUzYk888US6LdmZ27ZtS+N0PtAc\nofWHPvzhD6dxGpN3vOMdafzAgQMzYvfee2+67STeiYiISDMmERERacYkIiIizZhERESkGZOIiIg0\nU1Jy3vKWt6R1r9773vem25NVVV0d7Oqrr07jZJGQ8UPmAxlF2eqAVBuK7CBaTa264h4dE5lodKxk\nf5EBQ0YLxSt1iCLYjKmunEjjQtYM2XFZzSFa+ZIsLLKMqP4RtYXmJUGfS6YO9XHVaKtaVWSR0TlE\nNlflvI3g462sbHrTTTel29I8e+CBB9L4zp070/iSJUvS+G233ZbGqU4b2Zw01zJTb751wrwTERGR\nZkwiIiLSjElERESaMYmIiEgzJhEREWmmZGede+65ce21186Ikz1FJhDVn6KaN2QUUB0bqq9UXQmN\nDKQMspKqJhrFySwhaPvqSmvUN2SF0f6pVhAZIGTeVFYkjGBri8Yl28/27dvTbR999NE0TiYdzb9K\nWyLYktqzZ08ar1pPZFVSnGpwUW0rmps0R+h6Ue236hzM7DKyLat2JkFt/MY3vpHGr7zyyjT+7LPP\npvE777wzjT/99NMzYvO95ngnIiIizZhERESkGZOIiIg0YxIREZFmTCIiItJMyc4666yzUisne7I/\ntX0GWR5Vq4qsEDJ4aIU6Mo0yy4bqBBEvv/xyGifzgUwRsjaqn0v1jKpWFY0JxWlM6HOr+6d+ozlF\nBk9m5FGNLDommiPVFQzJ+CFrsGo3PvPMM2mcViSk+lBkYVENK9oPjSH1A9lZtH3VmsvMPmo7xasr\nbtJ8pVUlb7nlljRO5iDZYtmxameJiMhpxyQiIiLNmERERKQZk4iIiDRjEhERkWZKdtaJEydSu2fv\n3r3p9rRiGEG2Fa3SR9uTEVFdLS+L0z6oLWQfkTFDdgbthwwh6gOqJbVQdhOZLgQZNtSfZNLRGFZr\nbWXbV1dTrI55te5atQYXjSGtonfgwIE0Xu1jqnlFKy2SUUjQ8e7evTuNk51FKy1mZiKtSEht2b9/\nfxqv1OuaDVrZcPXq1WmcxkQ7S0REzggmERERacYkIiIizZhERESkGZOIiIg0U7Kzzj777LTu1UUX\nXZRuTzV1qB4T1dqhWkFkOFRNqUoNHmojfSYdK9XaIdOFrCTaP21P+yeqNhq1p1pzjNpZbQ/FyXDK\n7Bhqe7V2G9lZ1TjZVjQ36VjJeqxaZDTXqJ30ufv27SvFyeaidlbrt2XGEo151YAjU2zLli2l7Wl1\nV1rlktqZWWfzNcW8ExERkWZMIiIi0oxJREREmjGJiIhIMyYRERFppmxnZfWwyHqgFdJoBbbqimdk\nAtEKctV6NZkJRCYaGR5kolDbaT9kVZAtQlAfkPVUrd9UXZ2S4hVjZrb9EJXVMqnPyGIia6jaZxSn\n9tBY0VyjuXPttdeWtt+xY0cap9X7yDQiC476oToXFsIcpGtF1Qgj6HpB+6EVD+kaSMZsZtjN95zy\nTkRERJoxiYiISDMmERERacYkIiIizZhERESkmZKd9frrr6crda1YsSLdnmwOqoVFhgOZOtWVEKv1\np7LPJWOhaj3R9mRVkLlGx0pmDJlDZJ1Q31Oc+of6gcybijEXwdYJtYeMnCxerctF84nmNx0TrU5J\nc4RWuav2AdmTZFXR+U81r5588sk0Xq0VRv1G+6HtqR/oepFx8ODBNF5dqfDCCy9M4zQmV111VRq/\n9NJL0zitWvnQQw/NiNE1ahLvREREpBmTiIiINGMSERGRZkwiIiLSTOnB+rFjx+LBBx+cEb/99tvT\n7Tdu3JjG6WETPQCkB3T0oIweQtODUNpPBrWdSrzQA2g6JnrwRQ/Q6WEn9QE9rKV4VWqgh5TVB+IE\nfe66devSOD3kJrJ2Uh9UJQWC9k/7qZ4/1QXBqotS7dq1K41TORTanuYgkUk+ERGHDh1K4yT00IP7\nbD+7d+9Ot61KEDSG3/zmN9M4PUCvygI0ZzMxYL4lW7wTERGRZkwiIiLSjElERESaMYmIiEgzJhER\nEWmmXPZk7969M+KPPfZYuj1ZHps3b07jZDicf/75aZxKe1RLfpB1kkHWE1kSZGFs3bo1jZOdRZZH\ntkhYRL1cCfV9dWGkalkY2j+ZNGTBZfMygu2vysJI1MbqfKounEVjQtYMzTXqA5rLdN5Snz3//PNp\nnOwp+tyq4UjnCvUDjeOyZcvSeFb2hPq+Ola0oBYdE13TaGzpWKmdLkolIiJnBJOIiIg0YxIREZFm\nTCIiItKMSURERJop21mZPUCLzFx00UVpfM+ePWmcDB6yXcjgOZ2Q+UFWBZkoZGdt3749jR85ciSN\nk4VBNgeZLmSRLFmyJI1Xa15RLZ8NGzakcTou6ufnnnsujZMFQzW4skWBaLEn6jOal7SwEPUx7Z/a\nTpA5SPshS4qOq1J7LoINIaonV7XaCLqOUI2vbFzIUKM+oPOtuhAZtbFqQ9I1+dprr50Rm++iXN6J\niIhIMyYRERFpxiQiIiLNmERERKQZk4iIiDRTsrMicqugWheJDASq2UMmENWTotXsyIgggydrD7Wd\nrCEyXXbu3JnGaf9kW1Ef07GSzUEGEtkotNIi9SW18/Dhw2mcqO6f7Bias1k9LNoHrdBHfUk14Nau\nXZvGqY3V1RrJCqPzhywmGis6b8lMpJpjZH9VV8us1jSjczfrZzpWMunofKM5RdcL6nuyRanPKnXg\n5mvdeSciIiLNmERERKQZk4iIiDRjEhERkWZMIiIi0kzJzuq6LjUWqPYP1eYhw+GFF15I47R6H5k6\nZLVUV4TL6tXQCnpkhJChQn0z33o1U5D9QWYF2RlkDlGcxpA+l8wYss5orKifq59LxlJ2XNRn1TaS\n6Ub7oTaSGUdGDp2f1Dc0x8nmojGkOULtJzuLzhWa+1WDikyp7LjIjCODlOYCQXOEroGVVVkj+Nr4\n1FNPzYjReE/inYiIiDRjEhERkWZMIiIi0oxJREREmjGJiIhIMwtSO4ssErJFqD4UmQ9kIJCFQat3\nVVdCzNpPJgrFydqo1kWqrpxIfUYmCvUl1fIho432T0YO2ShkRNHnknlD0JzN+p/GkMZq2bJlaTxb\nNTGC+57GkPqGzrdqnEy3+do6U5A9RcYfjQnFae7TeU5zmfo/O16ar3TNOXToUBqnvrn++uvT+ObN\nm9N49TykMcy2p31M4p2IiIg0YxIREZFmTCIiItKMSURERJoxiYiISDNlOyszOsiGIGOG4mTwkMlA\nkG1RrdNUMRaotg2ZJWRtkHFCRhvZFtRnVJuL+n6+q5vNBRlFZDhVDSRqJ21PxlW2PY0hWUZkZ9H2\n1bpfNF9pPzTvqfYczSnanmpbVVcqJMOJrDYyk2guHzlypBTPaojR2BLV1SNvuOGGNE52FvUx2Z/V\nWnvzwTsRERFpxiQiIiLNmERERKQZk4iIiDRjEhERkWZKdtaiRYtSw4Se+JMBQ5YHmToLZQhV6y5l\nVsg111yTblu1HqorD5IVRqvWkbWxevXqNF5dRa9am4vmQsWSiqjXyKqaQ1n/U9/QWNFcoHlfrQFF\nx0R9Q2NF5lA1Xl1pkQzBqsFHx1ut2UX1sLJ2Um2rbdu2pfHt27en8WoNOzrPaeVEmjs0F7IxJMNr\nEu9ERESkGZOIiIg0YxIREZFmTCIiItKMSURERJop2Vld16VP8cl6qNYtIntlz549abxqPpGdRfVw\nMguGaliRyUBtIauiunocfS71MZk0ZMyQCUTbU3uqKxsS1J9k5FD7K3XaqC/pM2lsyYyhPqPzh9pO\nn0vznvqSzKHqCoNUk4pWGKRzi7YnO4uuFzReZCxmVA1PGkNqCxlqdF2gPqPzqtLH87VivRMREZFm\nTCIiItKMSURERJoxiYiISDMmERERaaa8smH2xJ6sEIpXa20dPHgwjVN9mPXr16fxiy++OI1X6tKQ\niUbHun///jROlgf1zYoVK9I4tYeMk+XLl6dxMn6qq1NSnMaK2k+GEFE19ciIygwqqmdEVFcSpGOl\n/VTnSLX9a9euTeO08iDN8S984QtpnEyj6qqeZLuRzUVz8PLLL5/39tQ39Jl0vlGf0TWQjpWuOzSn\nqI8rNRFnbDevrURERBJMIiIi0oxJREREmjGJiIhIMyYRERFppmRn9X2f2i5kPZAhUK0bQzV7li5d\nmsbJTKquAkh1bzKqqy9STSf6zFWrVqVx6gPaPlutcbb2VGt/kZ1F9hetWjffVdWmqNaHqtSNojGp\n1vGi86G6ch8ZPNUxpPpn1fONjuvqq69O49U6UPv27UvjO3bsSOOHDh1K43S8ZK9V5iCZcVSXjz7z\nhhtumPemNZZ4AAAdnUlEQVRnRnDf05iTIfjDP/zDM2Kf/OQn59UG70RERKQZk4iIiDRjEhERkWZM\nIiIi0oxJREREminXzsqgVbSoLhKZCVVbhAwksiqqRlFWu4jMDzJIKE6WBB1rdeU+6gOqx0SQ9US1\ngmhsyc6i9lePi2oLUbxi01FbCLKMyEqiPqN4dUxorh0+fDiN0xzcu3dvGie7bOPGjWmczvPt27en\ncbI2t23blsar5znFs3OOxrBax4/GhOIEzRE6Jpqb995774wYXesm8U5ERESaMYmIiEgzJhEREWnG\nJCIiIs2YREREpJkFsbPIFiGTplpTq1pnioyc6gpvmSlBdkM1TsZJNU7WE/U99VnFUImo17yi+EKt\nSFg1byorJ9K2FKc+pjGk+U3GT7XPqn1DlhedVwTNTdo/mYN0DlXnOEErp2Y1ysg4rbaRVomkaxRZ\nYQSN7c6dO9P4fFcxTN/b/E4REflzj0lERESaMYmIiEgzJhEREWnGJCIiIs0siJ21cuXKNE52CZkJ\ntCIhmQPVFdjIKCJrJrNvyHogI4TqBJGRQ9vTCmm0yh21h0w6itPxUn0iipNdUu3PqkWyEAZP1fCq\n1GKLqNtWNF8JqpFFVhKdt2QmVdtD0PlJNfKqc+rIkSOl7TPoWGkftJIobU/7p+vCQq3omZ0P813Z\n0TsRERFpxiQiIiLNmERERKQZk4iIiDRjEhERkWZKWsVZZ52V1sMhQ4iMArI8yFgiI4cMm/laBVNU\nVrkjI4fMEjLIqnYW1SGizz3dVO0PMmmoHlO1zhTNBYpX5hr1PRk21AdUF4msIeobMvVo+127dqXx\n5557Lo3TSqW0f7Izae5XV/Wjc2LdunVpnFZgpOsI2V833njjjNgll1ySbkum2zXXXJPGt27dmsbX\nrFmTxqnv6VpKx1rp4/makN6JiIhIMyYRERFpxiQiIiLNmERERKQZk4iIiDRTsrO6rkutlmpNKrJd\nqtuTmUBxsmYqxtWSJUvSbclkqJhfs0FtpHpMZENRbSsyhMgootXpaIW3Q4cOpXGyRch8qq4CWJ1r\nmc1F84n6mGpVVVe/pDFfu3ZtGqe5Rvs/fvx4Gt+/f38ar67YSHOHTCOam9W6dMuXL0/jNBfIoLrt\ntttmxKgvaY7QsdI1k/qsev7T9uvXr0/ju3fvTuPzwTsRERFpxiQiIiLNmERERKQZk4iIiDRjEhER\nkWbKS5JVaguRsUS1rcjyoP1UtydTglYHyyDroWL7RHAfUJzaSMdUtTyqq/RRnNpDJhNBFgwZSFSn\nieq6Edk40rGS3US1m6i+EllGNCZk0tFco9pWNFZkZ5HBQ31MY05jVVl1L4JNvWq8YnnSXCA7kPqY\nznPaD13TqgYcHWt2vs3XLPVOREREmjGJiIhIMyYRERFpxiQiIiLNmERERKSZcu2szF45cOBAuj3V\nhyEDgcwHMgpo/2QykC1CJlBmOFDNqGpbiKqFUa2XRLV2yMih7amd1A/Vukhkl1CtLTKfqD1kRGVQ\nvTTqM7KA6Jio7WQ9VS0mMgfJkiLrjMaQxoT6nmxOGpMLL7wwjdP5TFYbXUdofLN+prGiemk0VtVV\nKKleGpl69Lm0Kmb2ufO1Vr0TERGRZkwiIiLSjElERESaMYmIiEgzJhEREWmmZGctWrQoNRmoDgxB\nT/0XYhW62dpTXWUw23+1tg21sbrKYrWPq3XLqu0hA4nGivZD/UPQ/mlO0f4r8aVLl6bbkpVEfUPW\nE0FmHFlS1Af0uTTmZEmRPUmWFMVp/zQ3q7YlHS/th7bPjpfaTvXJqnW/NmzYkMZp3j/99NNpnGxO\n6oNs5VEa7xn7nNdWIiIiCSYRERFpxiQiIiLNmERERKQZk4iIiDRTsrP6vk9rvpCxRLV/LrjggjRO\ndhaZPdVVvcjmoO0zk6G6ol91xUCCzJvqqntUU4tq/1RXNiQq9cki+HhPt9WW7Z/aQp9JbayuxPn8\n88+ncaJak47sMjqvqqtlUr9V68DRGO7bty+N0wqM1P9PPvlkGl++fPmMWNXwpD6meoNkAlKdMxpb\n6nvqg+yaPF9z0jsRERFpxiQiIiLNmERERKQZk4iIiDRjEhERkWbKKxtmBghZVbSKVmY9RPDKYwRZ\nLdWV3yr1bcgmoji1hfqMDBsyTmg/ZEMRZNhUrSqCVoSkz60Yc7OxEHZcdZ5VLSwaQ7KS5rvi3BRU\ng6tqZ9GY0AqG1ZpdtB/anmqIVetGUT9k/UbnJ7WFbCsyRbdt25bGyWjdu3dvGqdjpXmfmbTzPde8\nExERkWZMIiIi0oxJREREmjGJiIhIM6UH64sXL04fitOCLPRgnR6g0wPJaqkBepha3T6L0wOu/fv3\np/EVK1akcdoPPUylB9y0H3q4SOVN6OFrtbQHxak99FCTHjzSHKk+ZKUHpNlDbjqm6kN+orqgGT2w\nrj7QpzGvSg3VB9k0p+h6Qe2pijLV0jhZ+6tteeyxx9L4Qw89lMZpsarLL788jdO8J2hMMgHAsici\nInLaMYmIiEgzJhEREWnGJCIiIs2YREREpJmSnXXixIm0fAXZVlTGoLpQUMWeili40iSrV6+eEaOF\ntsjCIsPh3HPPPeW2RHBZBTpWKnvw3HPPLcj+qSQHGUU0d8g6oTGn7RdiESsqS0JxsqRoYSE6T6hv\njhw5ksarVBeTovOW7CzqH4LGiowlildL3VA7s+MiG5Lie/bsSeN/+qd/msbpevHNb34zjd9+++1p\nnMaK4tpZIiJyRjCJiIhIMyYRERFpxiQiIiLNmERERKSZkp3V931qPtAT/2p9pdNdo4igRbIuuuii\nGTGys6o1bKrmWnVxK6qRRTW+yMipWl6VxZ4i6vWeiGodJbLjsvpNZFUdPHgwjZM1VLWYKE72ES38\nRXOHxrxae476h+YyzREy+KimFo0tnVs33XRTGr/xxhvTeHZdoLGlPn7++efTOPU9zRHqy0OHDqVx\nqj1HfZP1sXaWiIicdkwiIiLSjElERESaMYmIiEgzJhEREWmmZGctWrQoNTHoiT9ZHmQOzNcGaIVM\nIKoPlZlYdKxkW1RNtGrdH9oP9fG+fftK+yFLqlq3iMaWxqRqwdB+qN/Issvmd9U4I8uoWt+rWp9s\noUw6GnNaDZLOCWoPGUXVfiAbjebaunXr0jgdb2bwVa9pZK5Vx4SO9dlnn03j1JdkJZ6KAeudiIiI\nNGMSERGRZkwiIiLSjElERESaMYmIiEgzJTvrjTfeSM2Kao0fqgNDhk0VMhM2bNiQxsk6IUMoo7qS\nGEFmSbW+0pIlS9L4ypUrS59LfUmmEZlDFKf9VE0msmOIF198MY1n/UB9Q3WOqpDBQ6vlUV20aj0m\n6kuaO2Rh0XlC/VY1EKvtp3auX78+jVP7s/2QKbZ9+/Y0fuDAgTRO84/2T9dGqoVXtbayY52vseWd\niIiINGMSERGRZkwiIiLSjElERESaMYmIiEgzC6NDAfR0f6FWKqT9kBVCFhYZTtl+yG4gs4QsI2oj\nHRMZKmRtkK1EBg/V5qHPJbukaltRLa+qkVM1ASvtIQOG+ri6MiBR3X+1fhjth+qKVc+fak09mptV\ny+v9739/Gt+0aVMar1xHyJ5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aJ1uEbBSq2ZVBBkm1jhL1DcWp9hStGLhq1ao0TtYTGTNk\ni1CczCRi+/btaZzsr8yYiYh44YUX0jgdL8UzyA4iU4eo2lxkGS1Ue6iuWLXmHZmAdE7Q/un8rF5f\naC5Xa5plc5COlcawWreM7KnqiqpXXHFFGl+/fn0av+uuu2bEvva1r6XbTuKdiIiINGMSERGRZkwi\nIiLSjElERESaMYmIiEgz5dpZmfnw7LPPptuThXHHHXekcTIWqFYQWRsEmT1kkSxZsmRGrFqXi/ZN\nK5uR/UE2FPVZtWbP4cOHS+2h46UxJ4ukWnOIViR8+umn0zjNHarllbWfPpMsIOoD6rNq/bOqhVWt\nPUX7JzuL5hTVb6JzgqxKOm/JZCLbitpD51a2n2otNjLsqC3Ul48++mgaf/e7353GqyukXnfddTNi\n8zUYvRMREZFmTCIiItKMSURERJoxiYiISDMmERERaaZkZ5133nnxzne+c0acbKsnn3wyjf/Ij/xI\nGidr47LLLkvjZMGQjUIWBq32l9kJZDGRybB169Y0ftVVV6VxOiayPMj4IcOG7BLaDxkzBFknVRul\nWv/olltuSeM0XrSfrJ/J8KI20nyqWklkZ5EtRvFqPTOqSUU2F51XdFzLli1L49WVDauraNIc3LBh\nQxq/9dZbZ8RoLmQmZwTPEepLqhlHBt/VV1+dxul6QbXqsmssHesk3omIiEgzJhEREWnGJCIiIs2Y\nREREpBmTiIiINFOysxYvXpzaA1//+tfT7ckEohUJaXuyBMhAojgZFGR/ZGYFGR5kwFx88cVpnI6J\nDBUyb6g9ZAiRMVOtx0Q2F31u1dqidlL9pjVr1qRxsmBoVb/MiFq6dGm6LdX3IsOGrCc6VupjspKq\nNheZgDSXqS9pP2RhUftp7lfrwFE/0DjSKqCbNm2aEaPzlgw16ntakZRsrptvvjmNV+uZZccUwX0/\nH7wTERGRZkwiIiLSjElERESaMYmIiEgzJhEREWmmZGe98cYb6Yp8VAuLbBRakYzMHjIZVq9eXdoP\n2SVkf2SrgJH5QYYK2RbUN7Q9mTpktFH9IOoD+lxasa1qzJEBRzV+yJghk4lWuSTTiOyVrP27du2a\n97YRPBd27NiRxsngeeyxx9I4zYXdu3encVrl7vjx42m8aostX748jVP/0P5vuummNE7m0ObNm9M4\nrXJJ/UOGU9ZvZFvSGFb7oLpyIsXpmknn7YMPPjgjRvNjEu9ERESkGZOIiIg0YxIREZFmTCIiItKM\nSURERJrpyLjIWLFiRf+BD3xgRvxnfuZn0u3JeiBLgmwrMnjI2iCroLqqX7aaGNV6qvRjRH3lwWot\nLLJFCLKnqtB+yCJZqNX+aAVD6rdt27al8czaIjtr//79afyRRx4pbU/HSsYcjS3Ne7LFqC+pPdU4\nGXAEGXmZJTnb59JcoO2ppt673vWuGbHqCqZvf/vb0/htt92Wxjdu3JjG77vvvjROdQiztkfwtXfL\nli0zYq+++mqcOHFizqVNvRMREZFmTCIiItKMSURERJoxiYiISDMmERERaaZkZ3Vdtzcicq1FRES+\nl9jY932+TOc0SklERERkOv45S0REmjGJiIhIMyYRERFpxiQiIiLNmERERKQZk4iIiDRjEhERkWZM\nIiIi0oxJREREmvn/Af7I5DQLZMcFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16606847048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WtXjxYrou1oCvXbuWllm1apUZX79+vRn3GslZs2aZcVaXAWDnzp1m3DtnrDFm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\nb8bZOVu6dCld17x588x4nvv5dOZrfYNYRERERER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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16613e70dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 260 ,\tloss: 52.81108856201172 , D: 0.16899999976158142 ,variance: 0.12700000405311584\n",
      "epoch: 270 ,\tloss: 52.496681213378906 , D: 0.16899999976158142 ,variance: 0.12700000405311584\n",
      "epoch: 280 ,\tloss: 52.184425354003906 , D: 0.16899999976158142 ,variance: 0.12600000202655792\n",
      "epoch: 290 ,\tloss: 51.87433624267578 , D: 0.1679999977350235 ,variance: 0.12600000202655792\n",
      "epoch: 300 ,\tloss: 51.56644058227539 , D: 0.1679999977350235 ,variance: 0.12600000202655792\n",
      "epoch: 310 ,\tloss: 51.26072311401367 , D: 0.1679999977350235 ,variance: 0.125\n",
      "epoch: 320 ,\tloss: 50.95722198486328 , D: 0.1679999977350235 ,variance: 0.125\n",
      "epoch: 330 ,\tloss: 50.655914306640625 , D: 0.1679999977350235 ,variance: 0.12399999797344208\n",
      "epoch: 340 ,\tloss: 50.35685729980469 , D: 0.1679999977350235 ,variance: 0.12399999797344208\n",
      "epoch: 350 ,\tloss: 50.05998611450195 , D: 0.1679999977350235 ,variance: 0.12399999797344208\n",
      "epoch: 360 ,\tloss: 49.76538848876953 , D: 0.1679999977350235 ,variance: 0.12300000339746475\n",
      "epoch: 370 ,\tloss: 49.473052978515625 , D: 0.1679999977350235 ,variance: 0.12300000339746475\n",
      "epoch: 380 ,\tloss: 49.1829719543457 , D: 0.1679999977350235 ,variance: 0.12200000137090683\n",
      "epoch: 390 ,\tloss: 48.895172119140625 , D: 0.1679999977350235 ,variance: 0.12200000137090683\n",
      "epoch: 400 ,\tloss: 48.609657287597656 , D: 0.1679999977350235 ,variance: 0.12200000137090683\n",
      "epoch: 410 ,\tloss: 48.32640075683594 , D: 0.1679999977350235 ,variance: 0.12099999934434891\n",
      "epoch: 420 ,\tloss: 48.04545593261719 , D: 0.1679999977350235 ,variance: 0.12099999934434891\n",
      "epoch: 430 ,\tloss: 47.766841888427734 , D: 0.1679999977350235 ,variance: 0.12099999934434891\n",
      "epoch: 440 ,\tloss: 47.490535736083984 , D: 0.1679999977350235 ,variance: 0.11999999731779099\n",
      "epoch: 450 ,\tloss: 47.21654510498047 , D: 0.1679999977350235 ,variance: 0.11999999731779099\n",
      "epoch: 460 ,\tloss: 46.94487380981445 , D: 0.1679999977350235 ,variance: 0.11900000274181366\n",
      "epoch: 470 ,\tloss: 46.675537109375 , D: 0.1679999977350235 ,variance: 0.11900000274181366\n",
      "epoch: 480 ,\tloss: 46.408565521240234 , D: 0.1679999977350235 ,variance: 0.11900000274181366\n",
      "epoch: 490 ,\tloss: 46.14394760131836 , D: 0.1679999977350235 ,variance: 0.11800000071525574\n",
      "epoch: 500 ,\tloss: 45.88166046142578 , D: 0.1679999977350235 ,variance: 0.11800000071525574\n"
     ]
    },
    {
     "data": {
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jIiIiIs0sSO4sskjInqDdu8goIKuC7BKqZ747dU2R2TRVC4vMNbKMqO2HDh1K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\n/z+J+9rF8a4RwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x166146db898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2UDr3c4wz5C5jGzqQ/CZwJZLfmLZYeX8EwG3pf9dM0PbJy4TeQSwT5Q4kPxl9XwjhC0i+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\nsWIFrWvJkiVm3GtnJ0+eNON5zrOcOd61wtqKN4Z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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16614ed8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 510 ,\tloss: 45.62173080444336 , D: 0.1679999977350235 ,variance: 0.11800000071525574\n",
      "epoch: 520 ,\tloss: 45.3641471862793 , D: 0.1679999977350235 ,variance: 0.11699999868869781\n",
      "epoch: 530 ,\tloss: 45.10897445678711 , D: 0.1679999977350235 ,variance: 0.11699999868869781\n",
      "epoch: 540 ,\tloss: 44.85613250732422 , D: 0.1679999977350235 ,variance: 0.11699999868869781\n",
      "epoch: 550 ,\tloss: 44.605674743652344 , D: 0.1679999977350235 ,variance: 0.11599999666213989\n",
      "epoch: 560 ,\tloss: 44.35759735107422 , D: 0.1679999977350235 ,variance: 0.11599999666213989\n",
      "epoch: 570 ,\tloss: 44.11190414428711 , D: 0.1679999977350235 ,variance: 0.11599999666213989\n",
      "epoch: 580 ,\tloss: 43.86858367919922 , D: 0.1679999977350235 ,variance: 0.11500000208616257\n",
      "epoch: 590 ,\tloss: 43.627647399902344 , D: 0.1679999977350235 ,variance: 0.11500000208616257\n",
      "epoch: 600 ,\tloss: 43.38908004760742 , D: 0.1679999977350235 ,variance: 0.11500000208616257\n",
      "epoch: 610 ,\tloss: 43.15289306640625 , D: 0.1679999977350235 ,variance: 0.11400000005960464\n",
      "epoch: 620 ,\tloss: 42.91911697387695 , D: 0.1679999977350235 ,variance: 0.11400000005960464\n",
      "epoch: 630 ,\tloss: 42.687705993652344 , D: 0.1679999977350235 ,variance: 0.11400000005960464\n",
      "epoch: 640 ,\tloss: 42.45866012573242 , D: 0.1679999977350235 ,variance: 0.11299999803304672\n",
      "epoch: 650 ,\tloss: 42.231998443603516 , D: 0.1679999977350235 ,variance: 0.11299999803304672\n",
      "epoch: 660 ,\tloss: 42.00772476196289 , D: 0.1679999977350235 ,variance: 0.11299999803304672\n",
      "epoch: 670 ,\tloss: 41.78582763671875 , D: 0.1679999977350235 ,variance: 0.1120000034570694\n",
      "epoch: 680 ,\tloss: 41.5662841796875 , D: 0.1679999977350235 ,variance: 0.1120000034570694\n",
      "epoch: 690 ,\tloss: 41.34910202026367 , D: 0.1679999977350235 ,variance: 0.1120000034570694\n",
      "epoch: 700 ,\tloss: 41.13430404663086 , D: 0.1679999977350235 ,variance: 0.1120000034570694\n",
      "epoch: 710 ,\tloss: 40.9218635559082 , D: 0.1679999977350235 ,variance: 0.11100000143051147\n",
      "epoch: 720 ,\tloss: 40.7117805480957 , D: 0.1679999977350235 ,variance: 0.11100000143051147\n",
      "epoch: 730 ,\tloss: 40.50399398803711 , D: 0.1679999977350235 ,variance: 0.11100000143051147\n",
      "epoch: 740 ,\tloss: 40.298583984375 , D: 0.1679999977350235 ,variance: 0.10999999940395355\n",
      "epoch: 750 ,\tloss: 40.095516204833984 , D: 0.1679999977350235 ,variance: 0.10999999940395355\n"
     ]
    },
    {
     "data": {
      "image/png": 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XPYqTyZTZZWQskGW0dOnSNE6WEZkoVDuHavmQlURjRiYd1TOjOSEzqVqba6HM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\niEgz/z9t18pdLu/qKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1660b0cc7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2za5rXsOyceNGM+5dm4ULF2bKa+nSpTQv1hitW7eOplm9erUZ37Bhgxn3Hmpm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\nVs7eWIbVQbYfrw1i27wxBhuzsHbDa8/YWMobY7F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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16614a99da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 760 ,\tloss: 39.89475631713867 , D: 0.1679999977350235 ,variance: 0.10999999940395355\n",
      "epoch: 770 ,\tloss: 39.696327209472656 , D: 0.1679999977350235 ,variance: 0.10999999940395355\n",
      "epoch: 780 ,\tloss: 39.50019073486328 , D: 0.1679999977350235 ,variance: 0.10899999737739563\n",
      "epoch: 790 ,\tloss: 39.30636978149414 , D: 0.1679999977350235 ,variance: 0.10899999737739563\n",
      "epoch: 800 ,\tloss: 39.11482238769531 , D: 0.1679999977350235 ,variance: 0.10899999737739563\n",
      "epoch: 810 ,\tloss: 38.92557144165039 , D: 0.1679999977350235 ,variance: 0.1080000028014183\n",
      "epoch: 820 ,\tloss: 38.73859405517578 , D: 0.1679999977350235 ,variance: 0.1080000028014183\n",
      "epoch: 830 ,\tloss: 38.55387496948242 , D: 0.1679999977350235 ,variance: 0.1080000028014183\n",
      "epoch: 840 ,\tloss: 38.37139129638672 , D: 0.1679999977350235 ,variance: 0.1080000028014183\n",
      "epoch: 850 ,\tloss: 38.191158294677734 , D: 0.1679999977350235 ,variance: 0.10700000077486038\n",
      "epoch: 860 ,\tloss: 38.013145446777344 , D: 0.1679999977350235 ,variance: 0.10700000077486038\n",
      "epoch: 870 ,\tloss: 37.83733367919922 , D: 0.1679999977350235 ,variance: 0.10700000077486038\n",
      "epoch: 880 ,\tloss: 37.66374206542969 , D: 0.1679999977350235 ,variance: 0.10700000077486038\n",
      "epoch: 890 ,\tloss: 37.492332458496094 , D: 0.1679999977350235 ,variance: 0.10599999874830246\n",
      "epoch: 900 ,\tloss: 37.323089599609375 , D: 0.1679999977350235 ,variance: 0.10599999874830246\n",
      "epoch: 910 ,\tloss: 37.156005859375 , D: 0.1679999977350235 ,variance: 0.10599999874830246\n",
      "epoch: 920 ,\tloss: 36.991065979003906 , D: 0.1679999977350235 ,variance: 0.10599999874830246\n",
      "epoch: 930 ,\tloss: 36.828243255615234 , D: 0.1679999977350235 ,variance: 0.10499999672174454\n",
      "epoch: 940 ,\tloss: 36.66754913330078 , D: 0.1679999977350235 ,variance: 0.10499999672174454\n",
      "epoch: 950 ,\tloss: 36.50895309448242 , D: 0.1679999977350235 ,variance: 0.10499999672174454\n",
      "epoch: 960 ,\tloss: 36.352413177490234 , D: 0.1679999977350235 ,variance: 0.10499999672174454\n",
      "epoch: 970 ,\tloss: 36.19797134399414 , D: 0.1679999977350235 ,variance: 0.10499999672174454\n",
      "epoch: 980 ,\tloss: 36.04554748535156 , D: 0.1679999977350235 ,variance: 0.10400000214576721\n",
      "epoch: 990 ,\tloss: 35.89516830444336 , D: 0.1679999977350235 ,variance: 0.10400000214576721\n"
     ]
    },
    {
     "data": {
      "image/png": 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IPUW+IUULqcWoPanC6LikRqG9U1UOEaRYypRDpFCjtaK+V6xYkdpp7IcPHy4d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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1660685f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1h 21min 10s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "model = deconvolution.Deconvolution_3D(vol=vol, \n",
    "                                        otf=otf,\n",
    "                                        generator=generator,\n",
    "                                        discriminator=discriminator,\n",
    "                                        lam_TV=0 ,\n",
    "                                        lam_GAN=1.,\n",
    "                                        plot_init=True)\n",
    "\n",
    "model.optimize(epochs=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"add:0\", dtype=float32)\n",
      "0 [-0.21999997] [-0.456]\n",
      "50 [-0.7127021] [0.15530905]\n",
      "100 [-0.84270465] [0.5375326]\n",
      "150 [-0.913881] [0.74679965]\n",
      "200 [-0.9528499] [0.86137295]\n"
     ]
    },
    {
     "data": {
      "image/png": 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QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2213e03fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# Model parameters\n",
    "W = tf.Variable([.3], tf.float32)\n",
    "b = tf.Variable([-.3], tf.float32)\n",
    "# Model input and output\n",
    "x = tf.placeholder(tf.float32)\n",
    "linear_model = W * x + b\n",
    "y = tf.placeholder(tf.float32)\n",
    "# loss\n",
    "loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares\n",
    "# optimizer\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.01)\n",
    "train = optimizer.minimize(loss)\n",
    "# training data\n",
    "x_train = [1,2,3,4]\n",
    "y_train = [0,-1,-2,-3]\n",
    "# training loop\n",
    "init = tf.global_variables_initializer()\n",
    "sess = tf.Session()\n",
    "sess.run(init) # reset values to wrong\n",
    "print(linear_model)\n",
    "for i in range(250):\n",
    "    r = sess.run([train, W, b], {x:x_train, y:y_train})\n",
    "    if i % 50 == 0:\n",
    "        print(i, r[1], r[2])\n",
    "    \n",
    "    \n",
    "plt.scatter(x_train, y_train, color='red')\n",
    "plt.scatter([0.], r[2], color='green')\n",
    "plt.scatter([-2.], r[1]*-2.+r[2] , color='blue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train/iterate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 1153252.875\n",
      "loss: -1329872764928.0\n",
      "loss: -2659746578432.0\n"
     ]
    }
   ],
   "source": [
    "model.optimize(epochs=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the FT-convolution with tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Lets just try to convolve a point like object to get an idea of the PSF\n",
    "\n",
    "# generate an object with exactly one zero element\n",
    "myobject = np.zeros(otf.shape)\n",
    "m_w, m_h, m_d = int(myobject.shape[0]/2), int(myobject.shape[1]/2), int(myobject.shape[2]/2) \n",
    "\n",
    "myobject[m_w, m_h, m_d] = 1\n",
    "# myobject[myobject.shape[0]/2] = 1\n",
    "\n",
    "# now convolve the point with the PSF\n",
    "myobject_conv = deconvolution.conv3d_fft_tf(myobject, otf)\n",
    "\n",
    "# display result \n",
    "import matplotlib.pyplot as plt \n",
    "import tensorflow as tf\n",
    "\n",
    "myobject_conv = tf.Session().run(myobject_conv)\n",
    "\n",
    "# plt.imshow(myobject_conv[:,:,myobject.shape()/2]) # assuming last axis is Z?\n",
    "# plt.imshow(myobject_conv[:,:,m_d], cmap='gray') # assuming last axis is Z?\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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efmbFMvykPO2KYZZl6+T5pbKV2TSK5VhfqsNfONNdk4ZZPeQ6Va2bDZJ2ZO9/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa4baa0080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(576, 864, 3)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.subplot(131); \n",
    "temp = plt.imshow(myobject[:,:,m_d], cmap='gray'); \n",
    "plt.xticks([]); plt.yticks([]); plt.title('test image', fontsize=25, loc='right');\n",
    "plt.subplot(132); \n",
    "temp = plt.imshow(myobject_conv[:,:,m_d], cmap='gray'); \n",
    "plt.xticks([]); plt.yticks([]); plt.title('convolved image-along z-axes', fontsize=25)\n",
    "plt.subplot(133); \n",
    "temp = plt.imshow(np.max(myobject_conv, axis=0), cmap='gray'); \n",
    "plt.xticks([]); plt.yticks([]); plt.title('convolved image-along y-axes', fontsize=25, loc='right')\n",
    "\n",
    "plt.subplots_adjust(left=0.02, wspace=0.15, top=0.92)\n",
    "plt.show()\n",
    "\n",
    "# If we haven't already shown or saved the plot, then we need to\n",
    "# draw the figure first...\n",
    "f.canvas.draw()\n",
    "\n",
    "# Now we can save it to a numpy array.\n",
    "data = np.fromstring(f.canvas.tostring_rgb(), dtype=np.uint8, sep='')\n",
    "data = data.reshape(f.canvas.get_width_height()[::-1] + (3,))\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3] [4 5 6]\n"
     ]
    }
   ],
   "source": [
    "a,b = tf.Session().run([tf.constant([1,2,3]), tf.constant([4,5,6])])\n",
    "print(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[name: \"/device:CPU:0\"\n",
       " device_type: \"CPU\"\n",
       " memory_limit: 268435456\n",
       " locality {\n",
       " }\n",
       " incarnation: 2801656681870403299, name: \"/device:GPU:0\"\n",
       " device_type: \"GPU\"\n",
       " memory_limit: 6681632440\n",
       " locality {\n",
       "   bus_id: 1\n",
       "   links {\n",
       "   }\n",
       " }\n",
       " incarnation: 929224753502256188\n",
       " physical_device_desc: \"device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1\"]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "\n",
    "device_lib.list_local_devices()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(64, 64, 32, 1)\n"
     ]
    }
   ],
   "source": [
    "print((otf.shape+(1,)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(408, 387)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2 \n",
    "\n",
    "imgG = cv2.imread('GeneratorO.png', 0)\n",
    "imgI = cv2.imread('iterationModel.png', 0)\n",
    "# imgI.shape\n",
    "\n",
    "f = plt.figure(figsize=(12,8))\n",
    "\n",
    "plt.subplot(121); \n",
    "temp = plt.imshow(myobject[:,:,m_d], cmap='gray'); \n",
    "plt.xticks([]); plt.yticks([]); plt.title('test image', fontsize=25, loc='right');\n",
    "plt.subplot(122); \n",
    "temp = plt.imshow(myobject_conv[:,:,m_d], cmap='gray'); \n",
    "plt.xticks([]); plt.yticks([]); plt.title('convolved image-along z-axes', fontsize=25)\n",
    "\n",
    "plt.subplots_adjust(left=0.02, wspace=0.15, top=0.92)\n",
    "plt.show()"
   ]
  }
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