CNN training & evaluation - plasmodium (motic).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['f', 'det', 'clf']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import nolearn\n",
    "from nolearn.lasagne import NeuralNet\n",
    "import readdata\n",
    "import lasagne\n",
    "from lasagne import layers\n",
    "from sklearn import metrics\n",
    "import detectobjects as det\n",
    "import os.path\n",
    "from scipy import misc\n",
    "import cv2\n",
    "from progress_bar import ProgressBar\n",
    "import shapefeatures\n",
    "from sklearn import ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "opts = {'img_dir': '../data/plasmodium/',\n",
    "        'annotation_dir': '../data/plasmodium/',\n",
    "        'detection_probability_threshold': 0.5,\n",
    "        'detection_overlap_threshold': 0.3, \n",
    "        'gauss': 1,\n",
    "        'patch_size': (50,50),\n",
    "        'image_downsample' : 2,\n",
    "        'detection_step': 5,\n",
    "        'patch_creation_step': 40,\n",
    "        'object_class': None,\n",
    "        'negative_training_discard_rate': .9\n",
    "       }\n",
    "opts['patch_stride_training'] = int(opts['patch_size'][0]*.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[****************100%******************]  1352 of 1352 complete \n",
      "\n",
      "184616 positive training examples, 507549 negative training examples\n",
      "187896 positive testing examples, 507140 negative testing examples\n",
      "1387201 patches (26.9% positive)\n"
     ]
    }
   ],
   "source": [
    "trainfiles, valfiles, testfiles = readdata.create_sets(opts['img_dir'], train_set_proportion=.5, \n",
    "                                                  test_set_proportion=.5,\n",
    "                                                  val_set_proportion=0)\n",
    "\n",
    "train_y, train_X = readdata.create_patches(trainfiles, opts['annotation_dir'], opts['img_dir'], opts['patch_size'][0], opts['patch_stride_training'], grayscale=False, progressbar=True, downsample=opts['image_downsample'], objectclass=opts['object_class'], negative_discard_rate=opts['negative_training_discard_rate'])\n",
    "test_y, test_X = readdata.create_patches(testfiles,  opts['annotation_dir'], opts['img_dir'], opts['patch_size'][0], opts['patch_stride_training'], grayscale=False, progressbar=True, downsample=opts['image_downsample'], objectclass=opts['object_class'], negative_discard_rate=opts['negative_training_discard_rate'])\n",
    "\n",
    "# Cut down on disproportionately large numbers of negative patches\n",
    "train_X, train_y = readdata.balance(train_X, train_y, mult_neg=100)\n",
    "#test_X, test_y = readdata.balance(test_X, test_y, mult_neg=100)\n",
    "\n",
    "# Create rotated and flipped versions of the positive patches\n",
    "train_X, train_y = readdata.augment_positives(train_X, train_y)\n",
    "test_X, test_y = readdata.augment_positives(test_X, test_y)\n",
    "\n",
    "print '\\n'\n",
    "print '%d positive training examples, %d negative training examples' % (sum(train_y), len(train_y)-sum(train_y))\n",
    "print '%d positive testing examples, %d negative testing examples' % (sum(test_y), len(test_y)-sum(test_y))\n",
    "print '%d patches (%.1f%% positive)' % (len(train_y)+len(test_y), 100.*((sum(train_y)+sum(test_y))/(len(train_y)+len(test_y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View a random selection of positive and negative patches to see if they look right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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uHfwTu+M6VX9oxea4n3dpNU6Xva/6FZMcdUjg/WQJ3S7GO81QRqYRzZtWHvkk\n2ztoo3J/Xd3KdtmZuuItHtHk26TKmiOTRpn3SQUdWdgs5QJNvmINp2QrgQxlfAvVrKwXmUuTKnB5\nxCCTcV+kwCmGB0SPONe0ZfxrNJ+dbqc3/aQ7BSHmB4kEX5kog6rMLymDQGta0twz7Fdkq27iDSKa\nlqGqgaY4hiMuNzIHbeytatR7oy6NWBo1ZY4fz8oG9fX38g3cRdk48By9Y8oEj8XPz8Hj9RJnEPc5\n5ZerMii9I13PqU0H+Vkq12LZ4UkrWzje4T6G7YRuzuV+YlBfP9jhgLLtGJekcMmnnc3whJwOpypQ\ntTlbfl/0t9hzNgElrifiBCzOIuiUlXCAZHFeLMOPYdSvZvTvPRSlUrxwACpPHhquL3rA9Pavh1KW\ne8e11REujTLk5ZICa7PIxaILctaqteLAnrA+dmiIqmkNIngK0bMT/SFMfwjT33qje2ndgrw3mcFu\nC8igyc762bkE67XoI3wPHT6LE045u5Xumfv/R0VyovPCpia4bRnf8Z4s20h5+G7KNHNuE5ExXm2/\nr/XlYiCXuo60qtd9wkpGmJ9XMezbGl+9SReyedU5TukBK6BmKT7MtiLL91ItMcE9dO9EANOVbT0e\nSifxca17Z9lanfxa0dqddOSbvEKXnCleIZ+PaO7oOLZ4kf9U3t7bzT/l4kALRX1czkKhvUECWUw5\nJUeqDtFQ/klYva1lZiS0pRvjwOSD370TcRe1ykTUhVj//FQone2jRnrQQlaIUmZavAwmd03Gz1V/\n3Ds8Bo6EuP2BIsHp7j47b4aCCwhEEJsc6D7y7uZM9UbNZ4fjjyn/jnygw2ypxW4FRHDHIJmclU7b\njkU+Cat4hTgUJVWH5JqiHbSq/II7FopXHSkqCVnlKeWzHlS37Xe4yaFa6bmtRgORWQ0dZngFgU9I\nsc4XiD8nosBchak3lc5WxA9rU/XLgUMV4yvDuz8vRArGDzDKxk73NPVEMZWcsuKCwYuhzxYNkch1\nX6fywPQNx2sMaqGx4trJ3vyn2kXL7MRj36XoXtdO9FuY/uhM/7wz/e3mFyNb9ML4LuXe4Gk6rMIn\n+HRP6sKw16iUaDmpfEu0m7I7bPfW9jv6b+1Q8ebemn5iEuBqjgO0W8vROd/X37kKjuYqka80A+ZI\nY1YIXsWSahS/mElYe4g1T32GHXho9AbihttYer0PX3Wo9Fv5tO/1bMIyJsq16e4AK9FNN9Jd8pom\n5Y9Tuh2Wzj+sAAAgAElEQVQRovRC3lSX1FDk64YDiCtfbtKxs3L3mH7pPVMre1uZcF5ByG/hgZav\nhYNY1vaBxdcArVzyyCvkYAhq/fwRBMjvUQIb090MXrE+43usnUQGgbQEHKqRTUbVQ7zsCHUFYsL+\nziV6MtGTYIVKIoLOYn0EZ50DUIvmHVXj9Dm0Rn031j0CLZJ5GTwyJ0CoxPURmm7nGUMrAkVp/qwn\n/7X4EwxdjNUp/0Rg6nEUfoGgN7Lm4njVKQ61rIvQeZjl5RYQm5ek4Ht8WfXhlbIrAJ1Bfc7P33WG\njhSAraNhOc3s7rFBrl8xCHc4ZquDR2rJKJOriU38nt4Ip2IeC7DjZDdbRZSRvZdDREStgvlamCnI\n2ejtQrcipemKmW93z6CGZs4MwqCHSDvr8XVNSnvLncjnoL9STnGISDb1eU+i0JjE6hyh7SzsZZpT\nNcCYqLNM9BSm38T0Wxr9TT5bmdpJaXrxbv0O++zMwY4Ay/V+KpHu/TOJOuiyJqBGXa7MZtd9TqAX\nzhyqpQwi7oF1xw/N1RTgAzZmX8BcP/Z/EahWO6/5bYaY+O1sO9KCDFbLzE3Jb9xiU8zqWBVrDPmU\n6ZZ15V6hAyw+JT3ROUs8YvY6vZ5ibgUTheMJeCxSgxrFrDc29IjOQ4Y/JVn2/dYvAvvKEGbhE2+e\nkaVdhUQusiNoUy7wpP5o79/nySvm6fWVKQgvOUpbZy3nOMSrteQsiPplOxRB5s+Olq4zMEOhZ+OQ\n2z4b4HdpP2N7RTPErA6enYR8AvVIIcQajhZXxqoTPCfGQNub0sa+tKirkFAnkabKdmx6lMpIAAu+\nyrDX6/fQQkkNHR9HZUsjak2omxjrvahbAMexWtqIpA/w4pV/EFF1pEpVtuFQ5xRhdH8fZBib4TsS\nB8uyDkMHh527ZC/S9dtV5LfDTKZffGW4XCKEFAvwcmeY3ynltz4ldQs3IthLa28rUUJw7L/4YSy+\nYstETG3sH7OZ0dv4mNvh3zZg9/69KeYYlr4qjDT78FKjF8kjn2vjVTbfd4Q9HLppkYp1XLXBz/Zo\n9Hg8LpRwpb6rNSX7h+F7fmrsX4KwTyoJ01KXtST9DDVi9jlN6ITtYy2BUy5RR40/07sMTy0b7Cs2\no3wGHH2HDjDdV/T8Ydarm2d1uVpX5VzBaVeHJYZZlhzX6W3kFUz4V9Nev+N+y3kEx96wnGTpSC3K\nnGT274j29gdtSaGEB+BwpVXCia+OGi/W7ArNef6ELX9pZWpcoHCoiC44VXhtP0uZC8iKa318MKaO\nqkRKM0Hi35BmZy9mMPf1SkjxYwonE9umQ2tbBCyPiymtebhGU4oHwxR3YLALC3DMk4zIPn0vrStN\nIRLpurIVL46MvCUXB98tWOHndejorwHeRxukCRyRPRRkDiPJtTYDznroBnmOjUSGQ3VoDbSDXdZg\n3MzjitfXR0aLtv05tHKkDocIGW6xZf+4aD0CIkgGBMfXGOlJknHYbcBwOmJ5wa/15MXU47uW3UqH\npUgey0yU3yc3vdxZoYnoPV2ikCeRPOEQFtExzmMANAWmBhon8LMSubCQb7UbJxhW1z8n6E8oJ1U9\nSqXTdhzhVw4ZvA5Zj7OuFCtRqosEHAA3VnACG412tyb+9wlVR6rqbb+eTn1cpghOC+Qp3ihPk50o\nyNeVhh6HNAH4eNI1gTAkAe1jkznTSMvZhTkze3C9r3cU+2qxzGK9L4kmKMJdWce3Iafg2dacsXLv\ncJxqxUXCvqYa/KB+FVCN92cdK06Kf6Pn4BA0tE+IrWp+KVJo9a61F+v3Ex5psbXTTajwtnQbfkt5\nXkmnlHt3dujredUIsTN60ZmS6rFsmKSpJSplMdW7sZgXTYOZoWai0Ap80F3Cp3Kuq86pYX41GZfP\nZYM/oqOTkWp5OZQN2yjl2uKXGx9WzAWDncNQiq9KRecxk854Elj2KAMh6dFr87ikuVMF4ElSVrOo\nb8zcNmYHlNLFQ9EdmLuoMQoUOAX6T5IR4EL6WkJ/OHhgz+JvOLvOWzGF+qUWnvDk5P4lwmbZd6ZU\n0xid1sgSqspzC+dfyFLxcs5o3cbdg6EJMrezBB/l/nOrU7IoblEWyl0J3yVRHGOHxzx1VerZY2Wq\nkc+yi40FW12uRdSi041rxq7muePexyCVVoF7Nc1JwRemyt0KgVMQsn9W6lrv7UOLwgXY1dnuNx4T\nRq0TtdaoNaHH41Ou7ubjZxu/epaIxiSl/hxqVVzfputmbuzpZJSxXI4HOD+DdWZ/CX2MjRx6bQ9L\n6VPISVekIsHntj+dYkylOYsJXVz12YVTn+qkDShGvJQnvTW9RH15qtOmkO1V6DvKff4picTR2vmU\nxruwmsBfXMkSZk4ppFTne9FrFH1qvHiNGav0d9omF3c54uP6upQJvxlPZ6nI1Ta+FFx1pb5T7Xb1\nu8qnWfaP6MX3TK1M4nGDfUIewv8qLUMyUlkhfEe1ylfyc8cRmke53TUgV3TWqTujpUM0SSJei+cH\naMLnR+gF7h4aSXlsRlel7sXrLJPrQra05qBQlDu5VrU1pmzQ6XplbndP1ssWp++himyz3o0aMUkT\nIg1/ER6z9qR7DtzoqPyNmWGVW1a+NCJ6CFF7krRO0loYeVylIwlDxzawLZ3mHwyMgS84eJdb5XPD\nV0NDi7hLt7oyNVu4qoKJTJKD3MfW99Y9+UCjAtu4FFOGtMMhpjwBfdYGz+yiqr6uzz+irFEXOjJ9\nw5EL+/tsVUpfRB0OlZ74xjT2Rj5otOk5PlszHR0lHDf5qk6O3SZCINdcn70BpF64P9W2XqgDZmXu\nLC8XPqE6jXNEl4ckMAw0R9LttlKgwchDNTWiBzM92vj7hJZ1BfB/lZxVhCxVG+BOU1mxTKtd8JNJ\nwxmHTh/KoKe0U1m6Ku4vkKU45CfqGGe4Imp4u9GnNAnf5voPkPGUxFc8pyRoh7QDh+1faIf0Yvl6\nn8tneZQ/Z6s34YKufs/BWiuCURwnCOaRq4KYUys2GTS9K6fVPqUfmeQ7zHLVx5cefKHwsNOv5fgn\nGOwFvfXS3rVVutaAVdx8fF/BxWOjXVPO6atqXCDPg9z+fJoV09KRmpSvjeDSF55NxK7a051obHBW\nQEpM7lBhGY75G+lmdVOsUX4GxewFx/W67pL76dPhN2qrPWzajDtRI2IeUINpvGR3HKgh411TNHAn\n2RHwpO+UEHti/AmLOk8y/h5dj4v/RUSPAD/uIUhxpGQwUIiIWzhX0gJbWmicPQfA6ZQgifHiTvWK\nDlWUlV0APxWxGiyVSZRsv56AT1gVl45gZ3KgJtpvQpzbEEVfpqP367xK1i77qCvOqKcYrlD5nlaB\nLRM9lVP8T19S3WGcG1NZxuulOlE+Jm2u83ypJl7zJoswAgp2ubhL1b6WDQ6Ydd15+rKp8hJo7zl2\nhUKLgk0Um/uOP+bouqZOlTlSnzpTE6lOutQOsA3pEuF4X/DIzQ67rRpfzECxyxL7+RrYIVFBU8mo\nY9ChWotAOdyJ7YrARNmdVHToDc7FWTEi9Xo9FIrKxEroi0thfhNx+Tyo3BtkcAVf/LqiT1eqki4T\ngFaGqfwfgpUVe6CEvadc97blSp1/5KRJCJGe+632Z5Ieythup1vBjhWZz4dWvEKv9u+9A/rFl/Zm\nE5/JFJ6UJxBGa0qMZXdXnvzZuXkSwArHq4w7y4lOQc2/RF+LDK/Qq+lfJYWViz6WsDRQl/lxDz7R\n0DdbmUKQ7aeh6+cI+QGfgGCouB1TR6qRO1RxOIWUfgaTaRoHNTQNubgP8oMMwYl8EepHup+gDfTI\nTf86URfqxq9uK0bNHxy/9ZnWyVfqGvYHu/M0FK3Vo9YRLXgAg0w40o6UikTyFd0gptNrEfw61EKI\n6sslfcIZwJc5W0MWwyLZcE3yzFmaNtWgs6vWhuXeTwIDaLPXWfKL0r/nEAqBN9SLjVcEA4Lgzyq7\n0opmxMOae3v8T/OLr7561dWRGklHgthDNbFq15rr7S46wsHuHbR1phm+RV1j5BrPpaSGHLDfl0NV\nFiGpFVTYV1nq9h2hj5CDgolsedf8C1udam04VL9+3Wmn1nZvBf6R43hIRMqmxNc791F0QXYjSz1M\n6DHSDocKAAHadIbrqK9A78Ago5CQNcqpDthntLfjeMz7Tlau6KE1hpi+OI6K8v2bmc6ii69qB9yZ\ntsc1n4fjrTcOvLoXc3ni6IEmNFhj77PECJeKa2LCtGQl8EeF9/A9lzrV/ODeHQRYbuJR7dclGF8+\nw/buzwWPMbvtGFhePe/v2IKQddp+TF2TofM9U/W3x9ZbZaq2wyeF6nK9vd/IAASqMZep8qIQNIEp\nHLNm7Z/BqCTUKZ4WMrn+Ap9S0qd0pZOy8fUwtk0dpAij2DUOQJUcKfjbjlN3nEyxxurUditR0RoV\nRFG5+ylPvdVeR6/6aDur4ZUhw80rrzCEhyMl/vsRbSV1IFsPh7J4E34qj8ulgVOrIDIKN/0yIcgc\nAFO8LRZy+DovPudpyrAagYWTkY8fj4EaRkf1gYx354z+CafXgNFkeA4rpp+LtGeGVKZfRXrjHw/Z\nvCUOn4uxLb+n42R4jGEf+pjWgaeEkyTiY309rEM7iObRRecGaNXG6ga9T+v3E91Dyz0leD+nJmXs\n6uppvvjE/u7ujl0/4eiE3ctrL0yv2zV9QapFMou/0+Iz8vaX5syOVNHsq5UCpunVAKEfxg/pRP1p\nvzNAFdKw1K5tfNCIlqgHq670lStovcJpiFOWB8Gv87W3Ccbe7SsKCAyxrKI2EqDI6XaKN+8IomUa\nyJCwA/KU1N1tPq7OSmfvoqPOMzdMG2wTIX8VhXRyeY19/KzDdIABf+9WhgMTboq9arWuc0PTtoCb\nZIqnwV3L3usuhkEVdc54IfRvX2RA2Q9Ij59ZoZHvcarSn/7cZ/TWnqm0ojCvGVMIHcfl4lRhuEDw\nXgaonYukyfAhlsUvJZk7Vr5qIAXgLs3nXEgu6UNadfU6b7djcqXTs9DiXikDVehIPRWEdQNky5qp\nUbTBD87E3pGKZ68AsDtAWt3vwTQ2Dzci6rqaZqxpwtQ7U7O5XO5EnamLEDd1pCSOkR9gH5woZoq2\nqcBJdvTd8USmpgsZoIZhjz0YQlS79Do/Xn/kIq0rkx2qsBZD7oZwMRibBPNh1XL0kxmgoxLtuvdC\nVg8LY7TOIzas24oREyWgFSXcBP7ReSqqZoZrsZfDVs1cX6rRtokQC0XMYYMbikzjGRC4ZMS9Ftel\nSlLj/g7JZXAFHLM8LU8GjEyukxu9jSGrySnMVr6+vEgktjIl9Fgr5A8p+LVzpNBcp4r5L+RenkQi\nBaTjJEoaoalVRong0KBBjeBlschb17vYowDQSGDlF46RFwiSFgKcQPRyny/JHKlXcMZZZ67ugwb1\n6kPeFpJGuV9W42K25Gf6QO9bLOYPhKIhqdaiFe+OJsHOow0Ay5phIAqb76+f0M9k38jtBzoQKVqG\nhCIal+M+1WiKa/rXyrmF3YbJ3S4sE2yuy1Sf/M7aohdMn7Cin7d11/UHj85xeGfS9IIzdcywyWiW\nUzhQZ8SK/2JZVuW19lu0MwDXRCBnaCO3KsmEFctaZl0E+OcQaso8HCj7DMV+NEaq45TTchgjIeoy\nwnzMkeoiCnZpMaHIwFMAyjpAkEsMCscVsAmt/4bK6fdP571zN7Irqa76nHWflDGmMVNnW6Gy3VEy\nLLX+scmyKQKO3OMt70qw6uLt42g71jGQQBjtwTaG7xLjZMeaE4V5x1pCaqe9cBm60cedK8uQD6+m\nOlH+cmjpY3ZaxE8h83h8FN70UmLIT9uGNsiupbrDlR2r3Jjiowc64Z6VKQDVK123w1abRuBtkyjc\nB+mGO/02lOoPQqTcuo3J/a92/oDMmSY6NmCfUM1v208VMyZwHddkSgKG3/UscnxPXpOSz1yhuc6c\n0gBAKxgHu/ahqzT3vr8r1xbZffxaEbRE8MmUTpAkobTXr3cZq1P64mnpZruE5DHGdkMZt8ky17ml\nX6lwHidVCZFMRiaIcup4fYt0CY7TbHLGQf7hpiRLQapZYiCCIGgLpJmHnaQJP1a5qdEXccgKl77f\nNjRFKtjK/kw36IGTPvl0HODhUD6E/U+SzEqPlSlTpdOn6eIGUCHVcVVf3vJ65us+7VVi+DcD5VRy\npNzWTbNYRDskwOqXdUALZspzulKlvDsfa7bWztWZW9fR2nDtgKHXD6Ag48FsREINcda2rpIYf26G\n0QdSYEbG3h0E3yPnCgb4oyL/fDLmhTAkWRSuIkJC7Ly3kCB0qGx1ioSoddGFKPFBlN5BoWNAOnnI\n+8DbdrLUuIC8xs2EQjTe12HdYE25gStEbG8h0bI4gGOzfVHkf42IOjV9RpGktLEqJXpYhC/vsQMU\nhhUMxGBs/bICFhlR0eqNSn4fwvxo6s+S54wZbqWqz1a/RdbK24DQMDjjKHr20+Z6yJjwCN0xhopj\njigMxytci2DJWrjJ5VrPpNWGGrb5J5AD0HXtLVX6ZTK2eiIcp3Fipfv2aLSTQVf0mqS4GnRJV1cG\n6LDOAqbsT9ax6XCj6Sa2rtgwv4HPVxui9wFZzXskmCovFrWcco1yJKVBPrLtB4ZKscu76MuYx4TR\n/QR8kfnavqONT9g2u8UQrjpOnuxPoueTYNafiKj7k72N6ALuRNJCr4eMs5dayWzFsEXRjeP1GlbL\nuW+mla83yLHJYSr2Yga/VjYgxS+ELkFcpe3BvHIpYYPyygFjolyqGGuv6Cye0q1Wgj6X0lnH7EL7\nXtXzqAXZixpXeie3a10P+vHVKeg5m5A0lTAOomQfx871P9kGHdPKJqxoNfbPx4iIaLhvDwAoqQPd\nqKHpP/YZBpNFES9Gc1lvrGq9znW+dsaJl5wpufzdRm8wd2qI+QTp2TtQtaoYie8r9IEK9++RpsmG\nZFsH82ZlWpObE8XJYCmWDSVgwNVWC1A3GaAHvo6/UXYoVhN8GADFqXLFbQYPjN9N0VPaVqFGTJ11\n14Ae/dzYluGHge5k750iPz6XRM/EkhYx0D2X4ODewKv3AROnaT5tZNJLVeFUSDd45D37uf3+mLg0\nu9pID7H0ChtJgBwRos4RDvEUYulqWXQHmZ0kqTLmp8xBoa4UgZcrFe5VFKjf1K4FiF1ck2jczYQy\nsNJFcd3fCycLMAq2SOB0TmnBf7KwXAjZXWYyOVfZqXhHY3qY51+gbxFMzgfebIyo26YdFHeF6f9N\nuUCZ12q56//Q5N4dqF4sJftuT+3u+3idVm9lzZU1yeKPku33q8ZSm+V/0gCnz7BVI+WYAJDGJA9W\nMDuecbWwqQkW7vZHr42c+4RJUNXseull4jzKiLJ6yQvluEfdU4wW6CQfnjSKkUD5tGQovGZHCIVq\nzUq9kg1bNGzKA1rClEPu7tSpHjWR6/auE2WEjpTYfn/HSuz7JsTktXefsCZ1pEzv2uFdvTE1odg7\nyBu7cxCe+OeR9WXGK2uadescDozpDEwS4SEwTEwizX+nD656GcFJ1NUdqoKsrATPReacjtu1p3Nn\najfiDQSuzko1xWhO1JRktarFoQSmZi3KWIA5rGLqRCaygy/u0YY/R2v9UgAqEeWXmsVnUsrwWQXH\nw4H0us8UuCIYN3wYdfIZ7951iOnhdn7iN1F+1604jqO5A/OguEdn5DJ8vxSPlQ+zx2zvg2L2QydE\nDHyGHPrKW3PPiYjbOIiCAsrD3XEFGa/MsElNPLEtBn4oKzspaTpZdddcACY/IdrWL/N6Z61KHBzh\nY1nlqZvBUaMT3ryuUOr+tPz2aNOvOoPn+6gC3dhKaOxnQc3I/nGkCg/BgfP1RqOv+a1y9C5f3GQH\nT5EyHcbjp5o1L2PwR0E2J3s13hNnf4uwnkWtvKjxZa2Qz2Swvg7jLrCQ9/HmWklJN5Kkqaj6WHow\nwjzwmKSwSaiB/QKB3ruCQ/YNIzCo04G5scMyQD98fEQojw4iaS2/uRtrJdXaCBwN5WwEfGDNFMxn\n3MC2NcohesRWAehHmO1OdQURQQfCI5Gn9qTg1gU2eZHYbAUXoCkTzFmF/XpklK34i9pRs9vAS8jW\nv7sxQoHlWV4s3I/wWWPpYtzGuFodLb8iAVn9UL/iQQV26cCJqoc6HO6bcmZ648NGxRei0gZOnxbJ\nIn7NZOAdUPrTL+wNWun01+W/hnaL2Pg3o6RWCl60jTwXlBNXHvahq04cnLZ+ySvMC5L8fcZQhsmu\ntfqt90y54ruQfJ6thnvLayv0wynFVcqhPPnROsh+Rhh/mgpf68DHSxkG0PQDHCn2WVe9D3tIu4ww\nwK6vWeqNqelKbW/D0I2a6XIrKioIcbif3W5lvAxipqaOk7+QWOVrzLI16qzDToh6evurKftARkP5\nMTVuxPqiSAz3Y2six2/PwZRAelPtmgnzoD5scZTxAxQGYIJPi1+b0BlbCdSZZhYZ7zciOF0ewVVC\nDwQGGcxQRY4oUGijGPKx25w/6/VU9qeG/oBWbgqnb5ou6drFpJCebjZW8TQHCy0hoUZCnQHI2nho\nFKdacpkJPKT3eLICLhhq+Sktu+/CM0fpHAAQGHMEr6on8rl5oROwf2u/ntaMYWBkbyZANeQckPYm\nWjDGJ8imZAu7g9+yeg6Tk3RF/PnQZW2V7nllHgdsMI8Jglg1l7znlEQnHK3s+B7cIg9BFxIPIjQn\nZ8gqeUjuHX6/ATMH00ArdWPtiMOkLLrCeDW+mCiGY4WZVJwg0EhKegFquvwej+5Ob43GTK+7mOiW\nWCSqtro6Unfolrqnjgj1ctbdXhqY+iFr4yFzVF31flC9Pwe3npexf0/WLt1woMbGDE7XViWzTci4\nAkKLaHlTTJ5KyUEw9RnCmgbhJbX91p4pHeJzobUq6faBoq0PozS+Ybjrfgjnrinmk2fw2lmaO+lq\n1rISDHjWTbB7+qqQEbQSeR/6jJY6UhKamYTIQ+V6H9zrJGO5uo+XRI7tRZYe4GBypCgwgdfyTgor\njcDbQm9FyMMXiXic3jcC/fQoaYnwXTNaNDY5m6LGUwx5BBK6UYSAynCeCMe89pOBos3gDPHn6+IP\n4OpWMEW0cKIOKkERhW+fYyUTwnWeQnYARTPnUojgjbLwe9EgBiMJLtz0wkYwUpPzQVnJ743ZCibe\nQ9i1tZvdOOA1gZSBMoMakb/vRO8xTF4kXhBRa0zNjpRuMVbCSZ35uUR7TgsueXUxFHZjM35Ar1ZZ\nfPV+VA6/YzuUvwVoG0iPMtbBJqeVh+exwkIayrlcHdSDGQ776g2SWQQcVHuSiyNFhi6w7w7+C15w\niMXxabLcWKGXyqzAeLd9w+ZwENnqNpSpH7afpRuXGcu0vsZx8RmFM8XhzNmYlQzWE1mdxf4gZN14\n2EE2E0ZY5Wd4yPgQ7Vw6VadiizesM7VNXO2I4QO6DnqOCELr0YF65x1TR3oo3udHILDQFv0bPGWX\nVbufv9uEgOnda9hSkv75Sbrb4gGJaYpGWTMyfI9/x7ch+CPcefRDft8ZKBBBNJYKBmy4EujPeHrq\nTF16L0her45qYZ2X9azmzAYulzSQwQpppNTsBtxV0+rlvQseXhl894SlgPlZGMQ0e0cEBrzmsxIm\nsBTFuLkS95faxqeQhfmJL59mgMC+6qTwNd3Ph9yy6tGTt9PcqA+aA7YcY+415hiEoeAJUsbBEsOh\nirA1S8SQ2lonIsMps/cAE1MzR15I92KhbMcM6ppAjSzGzlb+ThHhZ+TggnEAggXxqyhvA+V4gIPA\ndesEWcgnWiW4Eqkidf6+fqcIjqGXG/0TIB/6FVvgPDpQdb63DwBz4CaVOztrVwW4MRH3mFs1EGcb\noZGVshI8Fqq+6p5ywsH/AHe7Cau79Woua5238SM057qRHkhnvPFPmaRwqg+TS6+J0yVWBi7wnGMK\nE8fbbNsO5mpepiqDDOMfV2usTFwRSmGXwDYQ2CjHhjzrC3n1mnQezqqIA9DHQ6j9ovH3YH2JvGau\n+aBDNbpKxtGuNnEmpV/8jcfk48NriG38kLEWmmy6LvGB9R1aEmpHvG9tbxQVRwpXpchlwR3bJGxF\n+Jioc3dQ2lrzSdfsPIaOqDbT9HNppZU2fglYC0+quuqWYZ8CXT/SJbNTVfEUyrGORtuvyOTOeWBK\ncrlmJqI20o2/jI9OX+NRHKl/EaKp1nUE+bDFDhtj+g9iOIQd7sPa2+aJ1D4JOOdj28D8nr1qzwto\nYvz6Om/fXJlC0grBsnEwIQOt9bNExk3xZ46S7hmwfkzO061Qy49SaQMQvpgNcU1WrqtnZfM9Shyj\nWsAw4SY9C1tTQwPLqXishM2qxDRL5q+foOeOFIyUXXVvIMayp7zFjW1S8OVkJXtfSZLbxelLqLjN\noHXlU3uQy7F4OVqthD5owRZTkiC3F486vAs8TfkW4xf9mmlWUUzmSCHHHAiKkL9TDsAVU1MDtDLQ\ni1ayPZfvTYbpqmOEj1kf3S2rogD9LF8EnWhzMFzWkpahxr5JSvRVapLyxJcl++JgBV6TY/RKKxfN\n8VVvy++npPYdmjsj1Vdiqkb0Nz7DvVj81lTdMplDNU1MxtP7+sBHdqLwvD+TBwTPG717A2H4G1E4\nGQJlC1bfAH6qG9bOQDDFhBSNA2mkR0RB4zZA6EOIH8OZ4qbyrQer1EOZxA8JiIMq7GAh1yhMxA8i\n+qUc1qNFOVXyHgDrzhSbHgDNNTb5pvoPvkJfS/7DFb6M/zk+KeMH1i9Cg38jZF21M0cUSxyewEtR\nQt1uV8hzClpJIiCcj8kOfUK7/HmedbwOBqPjKsB0W2Wyd6A5DGIChypWUpt/z9w5235Sdehd70j6\nMwhDpolIBSXLFsp5MsWgXn1ChITcqWIKh4poOFIu11rYZuyeyc1Vdl5yptYdZhWAQw88pM5TYS6a\nF95LHKLU8PJcHpGcr58NSuws3rAuptb+MpqMkdTr5BeLWMI9Kd8bxf4lilUnM88sNMLV4lmmIZgO\nzMl8xJEAACAASURBVOwPFIQmosRJ162mTWxtCuuZTajMl98j5nj/S8pLFDBmOIInCi5VfapchTI+\nYmnE2o8B3Zip62EKD5IoRNMRrpK43C80icXoeyUwzTkEu1WV8vLrEfwjN7M+1hYtAQ2F6mOADbjH\nRCvnLZekMLck4zrWL2lFbLD24UZ+P6F0Dkm5RpTHfOId6t1F1XxIMutBKwpi7WQ/u01CNoGyWin1\nST+D6mb4sJen56r0KUBzQBdAAEP/7tovtQe6sxaa6w2/aj7oSKWwgaKgYSVMqBNzc/nNM/05IC4A\nROn0/MXThpbO+4IcUiZQfQ+lKARQX3W1BpuAKyzj96zPHFR7/NsIPw251ds0wGpjJn6Q/43VJBRs\n1bMGeu0TThGVp5joj+Rt9JiHdIuNkaqjPmdoa1knxS8o2K4a/jMb4isSmbeOFVABVl2i8hErWTqR\nKp24DdBpTnIW/6jpPEzLJFYymzbCZ555jjcZqshHwzmJKI3PVeETgVyCmpvn4MpY58BKDEf0j3t6\n3bL1kD74s/JQP2D5kB2Gb+4cqru2otyKIWjdFWaFsNA0CeNfJPrBsJQM5MXUidjC/8gnBwfjHhT9\nigMCikT/40Mz/3KY3xUyBYEM2ds55VAFBpjb9RiTTW3WYCsn04GwQjl/MlWDFN9RpVeLOe9jY4X6\nIWUxG80MxuMRM1Cta1gQALbW4m/MAkacrykCVxj+D9bPtYmmWYGwz2iE58lClGbBEvsHqwiGl5LB\ngQSMDyvHzS4r8G4Sh1kwmVNk9dAnLVQLjz70qi60KCFfN/TT8pqMvAmOW4HxCe+bEhrd3ZoCm4dC\nzIZIQIgakzyIuv41CLex7CWFQ6yjoTODPDDLKcvDgSFPWI/L503ESYym4ldXl36dWN0EQMS4hs4Q\nlpvkeVXqytDUStllGx/Odp20iY4bya27OzuIdUVkpwh8yOJjOzXqA2azODI745OVhLV3HH+sjep9\nrEoJAEfW/RrcqLVG3Mbn6I3SUB/zOwasrkMoM5xOEmG1BLLxE1QUkwgM6QDkAcx5+XhacVFbYxNw\nI1+GokAH+x/8dn1RZUnrpvs2Rd9dpRgs6CkkrC9PNOcOzmTZwpOPicMeUK77aIqUSZZ8AMVWbAmw\ng8RzPqsvNLAB9/GKCo0iMNvee/PolGuvkAhdmccWVIvn1PfQI3TOsmTaXvf5f7S7S8yZduiOvaZC\nY9XTX+lhY7GMD8RHk7xqei7TPSZw9ilRNqqlCN6O34g3/h5op5onC+Q6Q+J7ai/Irx7NbHuoHiwj\nGACxQ0MGEgjgkb6NOu8mjHZ0Q5jfitSYGB/q4B6/MLnfjDjmFRiKZ9YdBJApPb+EX6tqkyMdBLaL\n+txHNV8fJVnYiiMVvyywQQikLg1MmwcdzVPAzzKOAifyvT3EOvvXNW9TAI18szrrHotYuuasMLjA\nBbfsuQ+mY1c/Jnb7G04QBXAtgNF1nv7FTGbw3wwxmnwbmzbIuw1o0nIdTMhwqHit3myATiC5WO2C\nCy5w4WbyME60Bv7yJ/J+9bqxYwI7QbGpcZImY++OiL+7S5qMcJ7H+HSHCgyQptTczWxWgwWKMh2x\nagms+qZfcHgXxiZdy7l5N5Dtg8G9RFjHEIUkMeVUPwo5r3tMp3bBWE41gVnMAuh9hcPApck81scy\nFCtXyHrGgb2QAlcZURcdKoIHCNbw9h+kAEUE30KLGmX5UC3iYFaGU/V8Ej07UR9HGHie3IgbU2sP\nejweRI8HMZtgt6hIAkV1DR/H2Eyzc83Rb57gE1q5ECBT+mf/+qfrQHPqYS/jJMP4YXK6U4CLa+hQ\nweoWMTt/3Pnw1Sny00QNoIz3s4m+QiDydPH/AfOf7aQViJfKXr1dLtm0pS/Gf3emOnwnIR2UJOZI\ndXOoAIc4mAzduW4J2gh4mKhGc+V2blp2lZge+ll1aOYZo2wB2zDkv2RccjWDrTxRR2rMV0db0+OE\nsoTySVNfJxJyDG21xsM9iYY/kVCey+pq3P41dDjH5Tgs9GboEMNkihX8u6RPi6yixmPC9qE8s0Wp\nJR+uSdyrDtVrztRVMGd9yTFDunKo8GSOeLhUGmzvqmPqRsG58REINGeeisBct3duoQM+omvkSYvB\nCUWhogc8mGdo59+2xNwMxMCmU4vlRcbrJCslLGCGBoGsKQwc8QBVKKNUeyTJyfsUECmHLYnfTWNL\n4Hf581lnl1lJihh7x5RBZz32uY3BL7bcnmZDoJ6BUePyTuwuMGfp7N8xAZBkAREGGFAZn7m4YZSb\nnxQn1I0/wuPdU8TEFtLzGH+kv6MIgRyrDVoxrFrtRZOSQzXLZGTN63sf0q4355Fa96cVJOq4ILcf\n1+UsvPewJJ/Iijxy0tjQzgYaNiv4Y/IB9gt2Jn6K71nxEzMZwEHb9MErlMZTAKaV1Cwrjg1YOQxC\nZG839xeeP4Xkt5D87r5a1RU8tTbCftuDSITpl7DOmjaykJSsDuHQgIPq4XicIVMBtR9T1V0mKkKz\nICrnl7rK9Lv1Q+qlmIFmtG6y6DJEnyGDgl1cnomZbdDpKodRCdF9VeL3uIF8mqzyvlveoXAw2NsW\n78uR8Q44A+pU2uj8TC0N0Olti6aTxPduPCFxyRMeIFVYiJpiCVb9ohnWSerQwAeyl7YDWEXWSd+j\n1REYVUbTTf8wOfPvEIqKbB6RL5El05j88xfLm5ysijOMxBSJEDPBT6LIyyan5lFIhB08h1LeIKUv\nArLsyILgLarENIZZDQLFA1a6DB3rEyD+qX/PsTXF/uRBRL+0P35J0g9EgUvFx1mUmWtG5X7+fkTX\nnKmTTJIcWNCifiaF7/INysEHVR0QC2Vqd2y2Y9G60fiIUWcU2qvKME9b/xBNrU0mnKAKgspIQFGg\nrE715WnwJXWn/GjYQaJGpGd97StO+mmKwU6vsZC/rFCzchX9voQ0t7F6LTCj32GXhomfoDGAe53U\n2kRiBxFCZHua6mxUt9l3tltD4LgaExn1ceMUlThpyQt080qqhfZNTtuy7wIYmEM13mjeSaRRvKNE\n/H5rTWfuIq48yp0LinF9vZ2vry4vOuZPpXmfzxXVVF8NcT1U2614qgOWvX0CVnRFB9FwpEhf1izU\nu1DvXc0Du70bBw58Rh4mZpWa7qtOYkjgdSUSPxzbVpp7pBEi8nYw9SfR89mo/2b9a+P4f+kkuuTx\neDC1B9NDmufTHiPv1mzDToVKoTNNa672m5q7rFhX9XQ8KzW7j2lmrMM4XKlzDBWgyK5FaHDOb6jI\nsOcD3MY6F9ZBqA09Yn1o/Sc0DwoDorr8j3q+NILIcJeMPTd+jDr+bYDyuxTHMyEvHNI70LTokbH6\nwbriGfWWrvKguMAjIyU1T/HEmKjqItTVsWza9ua4ifX0TyLudkKahT/CqjPl3glxEyjbcErsaYn0\nhgjuMf/DHGeb/Sqt6rLSt0wD5yaznhytkgE+yZBiJ1CaF2v6H4eip7S2BzuC4bndNWNsiB4LHTDg\nl9qLZ6fnb6H+JOq/hxM1wnTHfqmm+6X6L6L+JJJfRCT2yhrDF9W6VL20aLHfz9+P6NyZerEjXVZ0\n2sXf/4CKzwYW2u6kZyUL3aISe4CgQ1qYEgBkgQF+sSVTXM3dhErJ/h0M8+V5jwODR0RccaSNiP5p\nhku3gErctUmUB9t+lqzkbEPuopqUZshoDHamOO0vblapi98TO2UGjO/RxpEiojxTH+Vmg8mhjD0M\nAp9AB8o+hew/i23vQsTC/nJjc6rcZ3KZl/TJttfB5G7boiusOFHWr2UGQGjVt7XofN8cqd5Fj/0N\n3orICB9l2FwOeYS/FMAI8/dZ3IvtNCcunLm/d6qI+Ax6ZN685lBVg4llW39lg1JrFxMOI2yIZIQN\n0ZPdmXrqJAW3ERLbiImo+ULBu9SxNg5GRn06NQWqCGaEhoc00pgDaHOmbEtoelmsHb+Jnr+Znn8w\nPX8LPf9o9PxNw0mUTl06cRP69Yvp8Q/sIITB/wcRNe4xaUAGIsJJwthHm3WNvmE/AW5hNOPQBj6W\nktdoljmh0H1Zicb4FtSvlg/PIVMRvq6hdsrwyZnCF0l5iaMegS3CRcnOs+kQeFrlA+YGySxaKcTv\njHyuc25PJpk1M/G3RWBooqtde+UBOOhE5Ef6oZ23BsYcB2u+pNv2RccnDadJaISl2mSWrcyZQ1Vk\nE3vdWpPbo31D1ZGK9fZboZXYSFrluoTyC5rr5WpTcVi24y5cZHHrbiGLrAacDZncBUV4TSpGPuIY\nl8+7KK3UBzZJ9ZyesfFYqueySMv6ukYRoS6dnl3o+bvT7z86Pf8Q+v1bIwF6H3JJMnAsiztS0nXL\nNT+JmenxeBzYe8PGq1YgKqdLk5inztRp33BJZZxiorwyJUVKYfhhH7EHlNBKeJagAHtHM4wNiQgA\nAw0cyHGcZCe2tFsE6EONKtMXNJBUmj3zAffojA+U2mhvQPLgKBHpSx1tpSkrQJ428IzrnpcnhxUp\nuBaWfD6pJRr3A0AW60Uzx4wPadgoOO84cAT7AAyTfVbnE0CaQx8FExZOYeEiy1aDwnSwWtKm9ki5\nsaS9WXmV0JFKjkytCvYvV+XE6ki1sqosLj+NmdhWp1y2oi088TwKTjpm24aj9gXlU5LmPH/GB1tk\nWvWq/pLkOc7t3TlOEX47ckonQE054RiFdNuDgEBfiMo/UfgmT501tGOpYUq3qzG8Z1MK1LsYv6wL\nsc6QAsEf6gFhkicN5+k30e+/6d8fMhyrJ1F/2uESGs5nupLZV/v97BWK0Zn7K48vqMDI1y0mTg4p\nwJPS7hvYiTIjrtdL7ZBVhaWzTOmzNq4FlOPmqZSDEOxtmcAEWD1c2SPqtgfKOgD2Co/fPN7/04re\ncVNatfEnhHhEa7lBaWMfF8W7rzSkrOlBL90drb3RcBAoNEJtzUnSdrL+Nw5FZN+CphVza2pH/Oe6\nI87Qb2CoWPOYJqVtH/HtUODA4nm1OV1L4WlilyFiJemRCEzj9M/ckAjXLHfSZKCmyAniQ+ALE5U4\nw8BqdfKUDznxAi3GZoKa2HaZktaulfoDRckZrtPTMiaofj/177fQ7z+E+u8BquQ59jd2Hg5VyCrR\no42wv8cvSrp1OcwkfSQcmazGBYaeOlOteNhEVkH2UbkKRUhgblmRYuk4f6/e5PooyM1I1OXsdPdg\n4C6Ggn/w0YO3khkEdSbN6EoVwnphyoWI6ixRUOiUzX1VEBmyRVx1pAvQG8/RVLe1Qc2g546RL+Vv\nLh/4ZiscPPbsUKOxdMxj71Pl3ahigK2ou62WtDHTri+PFNZAIVud0lAM5hb81U9btVrVmAj6ixYD\nGqoyxf5aKOKHxByzkXGN9QpD/+GYlNS99oxIAMfqTNVjY7EtBuL/utUk1D135vtee8zArxyn81Pt\nFvdX3Uj2Q/saHanUt/N1UpDqM+tPGitT9huMv6/cfsjXPSc9KI6E4EWPMJ6TY+kr5Y1IxqmcIkTP\nLvT7ORyoP/4m9Le/Cf3+m4afPHWsa8hJ4/FcF6aHEHmUhrXZAHACarsWXJWRhHLuQlNz+cx65D7q\nQ1xhgsNpOCyR8OAPvnSaxA5VgXBGrfsSVTCRjLg2spewuz5FSAJo0ha0epNxyA1pPfRQFNMv/IuJ\nH0JsLwH2Uya1n3hmxWf8XGcW9st0nvgkm60WSY9JCDs4Y+zhyRzz6BYZeqE7f8h5+GCi1kd703Hz\nKTQy4k+Gzm+mFXLdUzTHkAkPQdR+DX/gB1andhRGKP/epQVHIOZUEP9IKEwHaicZw32u9fECzZhz\nztKiVmASyoo39RL52Tg6aOMlOsaZr1J+GXLB/lRbNW6MbYxj0kAj+8YEgu2/JXJZ5c7U9I9Nr/gq\nebXbOAYPxiM4xFfofGWqzkbryPbF2uXOzGCN/W0rJKUZ8OMaUMiwP4UDgAaUWsW5GpHTqkI3oqiJ\nHwLrUvAdHSqi/B2ftXtnZZ7XiyHkYV9jM2D5fn0qnJJdDX8aGoOpnx3BxsTSiMdUH/UmxM9u5iK5\nBkQG+jBnk3Hd76Mvj7SpveFMkcadqyli0RUYy5mTePkE40rny0JuoI543cfJLc5HziP0ATiBaYhU\nOaGS3lan4oV66UWWnJ8LWftTzG6hM33zLl1pz0+NjnXZeRWCCJc3Vo7U+B7TDqwPIpwikREeBy9L\nFZvhZhrXGwcA/iEaLUMjvnakQpD1Q8hXmJ99hJn88Uenf/5Hp7/9805//K0PYKsvgfXXR3Sm3m3y\ngMPBYIpJnNLcUcfKg3yYyHVSJXK3Q4WrwOBIUeKtHiiDDlbab5uz1CeGRnXD12ZAy0S+bd1UpyZw\n903AUYun/XUMHkqIx6DJ0NvDmWA9Xrn7S5fT3220zizMSxpF3pV+zkEb98b+kq5OKnmYOu4ztQms\n8U5p8a3ApmcfjejRrd3sh0ulNgt0GmPUz7B/FuruZer4wiNV0KGK9gZO/JgOVSqAmtPCePoVAa3R\nH2swtpJZuzEZtfK9Ig6FspBPjL4ACAG/K3C7Y/BfzaM2NuhwAlQyK0YT2OVGJELDx35b0sN/aDhU\nlo6JuBM91OmKt6+85kglTesslstceMmZ8tUimNGJUiV46qUHSGoSp+Ji7Ry8q7KVhVBZ2faJK1Oz\n6NsDAFTfBGM++HkvLO9Q2r2FAN2NOLTPL58ZVfC4XDgk3XXRuQKyi1zNuoGXiSdfzz6hndsl8Fuo\n7igwvuhsj818AqgZRkTGzJy9b0M9olD3PXhrQs2ks5njrxrdcC7YNxCLoU4WotaTYXKK/b7Qqm1z\ntxFS97y8b848qSCfuDBmobXk/ICDczuQAmaWga2eGgD+X7EqtQ6Zu4On69HMk+yUkovePOve9Ut1\nA9jk/ZKmaGwlYV0AdiVDS8RBloJnwKt2WigRqzNl4cQUY+pTtm5j28IzSvxNDkCWc9E62zk00ome\nnen3U+iPJ40Vqj5ORI99aRSgvI3DJwZAb34trXZY2bP9psyPXbguTz8njPfpkMG6vbG3lW3S1XQZ\nh4wzyJ2J2pBL2w8UClBALkmG82Cre643sGD02SxBI92PxGMFp4wlfPWHh65DxndAUy0t/bKZ+hRs\nw95obwzqXCEN1bd2xNDNeErDAUl4zOw3oaarWr3TmM0XpoeMvVItyS7URce6HdIxzaJhe0gozmoj\nmChhH1fICnux+CeEw6ncIXPgnSel2kmnTl/CrNnhHm7i0gw3Jby5Bzb1IlYchA2cznSoDkSCCbFu\nRwGbeqOJZLnuREQF9dvZg8A6D3KDe6iVk0BztHeIlQq/TdLASvJYtS46HfJ13MW1zECQ2U6fc+Ot\n90xhrKSp2ARhQbjGaRrknT+O3gxDHcoVvf9sDaojFQDHzqWsdaP0fHx/QyVqWwIzfq5WJyeKQgBT\n/ksgty9f0ufrrgovvkWOwT8uynQYzU2V4ynCtpojfpuRUgC3KtmPnfV/tf4YO/9Qa07jRD4PY7QK\n04Nwps2dqV99PNvEnxlgSJKRsx8j1EKosW5wbyOvlmb8YiP5hDNXXVpCYu+kyZmCIc7AUZ9Fo6RW\nQV+BYpOQIWOZ9wqvJkhAvYLxuEqvHDgxTx5dMA5v0KvaKK/6wUh1h++T/kfgxiC0pn8BP1HoddXq\nQ/4oQquZyd/VM76brKDE2CcvZg8/IeCFs2ueTuOUEvQYrFiJRHjJU/9+d6KnMHVpYyzzOAyoNaLH\no9HjMQ6eePxq9PilJ/rpyX62JyeBA+BD0k2Jjq7No2U0/Q6GolytJqnKH1McpMN1nEsa8+OrHQij\nciJZLkYxbnihjZL0RtQ1k50wa86SDxOp6chDtMO0set4w8k/oAZGzTMg0PIp9m6j4921H5ryV9tl\nTtOocpk4EJ3Mljbe5Wez+yL0kEZNbM+qOpL64lP7XZdRbWJkrXLExxCLje/Sl9Yks7F32a6lstae\nWw+X/AhT+ZHTe1e4PkMC42cGcV3J8ljlYyk31W3ojFDPuRaMD33K05v2sW73AyK7tKjA/0QmF8yN\nWhPq+mqe8bJkCx2VMSHNNMJzH0TtF2uorugQbsAKlcfcUNdVXjeiCGXGdCf09kt785KjHSTBaaAx\nMekusbFyb+8wIDW2JpgOrDtC7vEvOBmzM0Vk4UL7Acnl+zWVGPtC8mOvHTW8Jmwf1ujYkZpzOLr/\nTnCID8fp0ajlHOYVhq/Wal/LDP7uMVIA3AUi7gX5jK+01/L1hDkSIXooeO5qWLxiUddoLQ8H6kFE\njz5mnUnDBoXTi+ZGNYRI3zhPNEJPPDzF31LbYvaPmKRpK6pCr+2m7DD8pOE3gxIzwlCmjulV+b50\n73JDYQw4+HqTWZ3onRP8Aujds/9syp/2/ZRxwV2FH5W4cqjsegDeyKf2le2nMBw6HrAVqwhZKYDK\n/m6hhSNlMxqL5Zohs8XB0xPmhDRmvws9RcbKVGd6Pnm8o5esbeP0vtaI2q9Gj1+Nfv2D4UjZX/s1\n3jdlgL1U44AqonJLe5rJfTog6+qRb3WkQF5NQYCD5JPLZABH88IJFcIN4uwrVH4qIAmscojKl5Wl\nBW8GaWsNFnqqQ6/l654ubDVx7F35CX0a+0fqINASFdwP9uh9XY3irg4VkTsrNvnTqMqZHYYUExe9\nD8DYZJwyaWPXMnBnNTpvXCiMyHjb2gFRHHY8obc5koajdsMbu9dGJ9p0sCkzJsoxn5Xcp0uUhKPq\nNazPGlRBeleOc5NKtlGWuPjXA5kg9Yc01+kw6VmSgt1N/G0fnZ8AKNZt9sqUoTtbU5kX0YkG3afe\nujpTuu/PHC+T3wlrwmFBFP0q0MGD1yX67oQuOFPYObtMQXJWyZvoIlLeD+TS4A9IfDWVpzG//u4H\nC62QaLQpQ9faEUTt1Uj+6KLji+6ZZsvSGPuQRATYFBkmfbBg6ZQPUUVd51QA5XSaGZmI5avTHIgr\niJJ2qXhnTRf6Rvz3J4RsqCwx1eM9me6Pkdt0H5PowKWur9BeygqTgUZ6MKk1ouGZmYUzEdXCVKbt\nayPx46AbSYT/ATeEaLzE1mp/ZNV9YGEWnwtr7vdVflFnby7NtmGGsvGj+jhzmQagtAffnHV7z6EK\nkHIbgaqbQxEE7sV1xVVYM6jbAqwcUJ0QQjzgY4jMyJW83flAAMtQnzCEhsjEPe/qcEWY7acMzu0O\noDEaoyY0JWLQw5wADw4z8by1jq1Re8R7hht3atzH3pNfjdqvps6TzZBy2CNQl/WVFKOEFQ8210o/\nZ0AltMj+dUrocq18bIUpwqFCP46aYkgeuxO0qpuFezH2hxDIILRbZYdE4jAMBZlDpUO4ttsry8Mm\ncaIW4/jusrsWzdtNCgBt1DQxq+PES1oAZ0dQltZeb6L3GovzORwh9tUpkfHaDnu/5DjFr7nzFJ4u\ntF1/dGsACpcqe3Sw451z5gDD3hV3/LIO+ITSvhYcBmnMscuqUT3ILE8IsMtR7JWq8s8+QYTvVIPb\nQIFAapfXxGksQxUDCYRxZb9X6VN9qkgQ9GJcn79bmeCenKxKwZielBmpumVd8ac4CZZGP4jp3sb0\naI0ev4TaLxq6uZEf8R9CEJDIhxWUnKqU6jrzYEcXnam60lFq5OnwZwQAElHsnnSHSaB1GUR6Fr72\nx37MrnQiftqSfQx8aSrsaLggVw+rRMbuai+U0wCnP4enWKda0LUhkBQqKAQY9j7kEvHaXJ8TGtQr\nNeVFGqtzXF/3+qf0egvH5Ng4HtZDUOHFiORctRLUkDGNN283M+hDXqVzyHudidHsLEC124VG47AK\nonFVN2HnSFYwdJVrJuTW7z+xlDLRQglS7gEpt7P5Wj/jt9JFM3ifg5rX8rgDlc5ZLmlS2qYfmIjR\nGGfD+8lKuZtmDoDjTpMsqir5a2aNOlSjukNPmVPFElEKTLE/xY+j/pDJWVlTmv5BuwMNYEJ/gWcg\nmQ5MYOLWqP0aYeq/mKk/mBo/1aGSEdqnIX2s4Sakp6PZMdfIM3Rc85dK5cZpdy8AyhuU9oNNBeup\neOAcWMkaiRMXzGk2FrNrqVRnt13mdJPpYysSUCN8ekSS22nr/QACnqfjBqsw8sqg6pp3t3C1DJrs\nBI9/BPittQcLbFhMHXU7IVXGFooRSmoOVQBKc2ZEiFq390xFfwWrONUPzlkEh6FDZ5KOE6GBExHX\nEVn4oXR7XlthK5YsGtVxA1WY4jJpbbKw0tqL4JwYDyRkb6yszz3P9gDIHeY4Vw21EuC2g+a7SZiV\nLdQXy7iLJku9Tykw2Ut7m1SdKDb7oHJjUs6qW+xAn8fD5Jf9ujyImDo9mk5m/RJ6PDq1JhoFAHYF\nvGqzDhFpEXpn375rDtWlML9pE1baPFzKmPT+GLG+oj1qB3nBICS8Hn9s7yvpTPxbSJ6hYJ1Puihg\nTkboSzCewtCVhJOum4ZH6iwaNwx+wQ2arxH7v8AEERj80DUq2HZi2pXc1+N2o6kWNbPvxddbPPNu\nOOI5rWffc1mOoay+j1FhN7pWS1hJJLFZD45Nzc1OiSKzIePUJM5gYNgY/d2rQ0XUGkQM29saW9PN\n37HyU8ddUqJ/ihMV5EqpdiPWDWTy2HBES1bprgDuO8Jw53rdaaQOCpm+g96FjRs+q+bDH/dNHdcU\n9cHIGVdnwsiY4Qsna2MgadYrA5CaY6XGjNV8Nv3OYew8Fv4TWnh9aLunQ2fNUbCvCRQNY0zPHjmx\nhpB0pgc34tbo0Uc4GtNzOFO2N+qhYX0acuKOpJnyBCpkMy42si6rHwttzXepAgQecS3yz6u2tk8m\nOwz5bzZ6roFdLhHUDvwQeU622FemBG4GgGKTPa8XKiz7PvQt7j9cAeifoCF6oTAZbywcqlxvgOc8\nxIybns5ndgrw1zgBsOha/6f8EC1dDx8RAYdIZ0TYFXwnf0cQQT9ouV2dvQT9eDh/nyrX9HjtVv2O\nYHjI7s6hsn6AyaRQvaEPLbtS+HY1xpNXFDLunNqtpPtnSUjJbhr3Wx1kKXCieGkJdmQ6gsm2Mupg\newAAIABJREFUNIwj9MV7gIndkfIDjISpM7ljztTAmXqqzu0aEmgTdaOcFc5Mq5BT/dY66ojOnSmG\nDnKJmovY9d/0CKDHeQbIBi1sGE5hJjNgsEvhfHL8hsETwyTyO2KPTL+uCsoFAkMexl5yEeZpFRYl\nBZ9Gt84xg2L2wLZk7a6I/CpFtqhL6CaxMjbxbOeHJeP3Pu0UyHRyoNh3CTvMYJJUiQ4DJ9FsB65E\ncZoUjdiJ0AAxm2tCyOjkNuCBkFAf76fRzcAD5EbIhRs8sXYAqC4cs7Yh0r5RYrEU2r28daXoHZyX\nO0vHyOsMhmKRbndKTw0DrMbp5dUPaM81rfFSxtPPevjFohrkwIgizM+ePXJ6dk33FWwdCOg8DXAQ\nM6cRkoODuQL6LJzMNFZdFUSxUMS0NyKCGPcfIaxeUrGwErJIThT1ag+mh01WMZH0cRqldFj/UJ3Q\nWmmfRkrgdGGUlLUlWHesRfk5nkkysEiJquBd8mk1Dm4RJZNDZqlN5BqP/WSpaI69Um6Dl3XDHoGw\nYdTHND+Ks8xER59VblEjqRvg8h2NTTKxqvYLtJ/0CISeZSTAu4fJYTSKGyKJTff6aJILtLMcfRjv\nk0L7LvGg5iN2MmCy6fCsDHumuw21OeqQWMSGSKyqD4FX+/cZ+ueU5SyvJp9n4yGNH8nPD3kUb1cU\nSCWddU+xPVgCe1cTTtxO+TJBpRD3gSJDnbESqzdJpxaohrhPYbBkeD2v6JaA2ZTeKzmBMlHsNRyp\nAXdk6NDHuNfV+ZY+dG5jSof/tEcbpytbpBqqgN3Qm/D9YmzmBizp8gEU54p534tcvyzAVc6ieJJq\nd8z8iC8N1vzdtJFQLHPHce7HlGNV14v9nxqomglrWZkZdSAf5IMzrJDJKszvfHWK4a+UzbMUCn4K\n3p1KvtCOw4q9QCtAAi1iilnqyall/85Eii8lKS/fC8Q6WN0IulnxAn2J2RwqRVbMjYSeRDTe8u37\nCGSsTg2fa/Qt7j1iPDIvtQp+y+7ePeTjiYiudFpWxBG+M6Wzfy4YvauEjsU7+62yXgkA+1eTOU7x\n3dpowPK11Tl3dpnCeZI4PGJPsxZPoWus+6WauDNlIUW+UdhfEnq5uu9RAZPjG7vNQFsiIIetEckv\nogczkc54dnWkRkJdntb3/TBJcqIwVFBoP2J2owK1+/oBDBMSB9134KrRh6OPqw2v2EQ5QEQQ0W8Q\nkqNuwH6goju9xNz6XZvqKlZpQeTtsrmQWwrHYNr0fyN1wcC54I0VBrUIOaQKX4bMhitveUGb/AFx\nvSV+mb0fJIHgtX3fSWfYfVG8JCTSyXYD25iP/e2BVxQ7k3Qh5r7K/jKxrW4hOJ/wiqSJoT2J1zca\nKJkFKduRr4dAipWldfN/Bp8Q+YVtB7vNu4KgbVD2ePSVVaFrFOwsB75x0gYkpQ4uSYvq+IqlD2jg\nNaQ3pGRfWI/aT+MAwkQbx2mprRG1RyduT9C9biy3e7mPR/o1bXrhPVOneeSVpFKF5Y8i6EllFk0m\n8EUAsCd55pFnrAaYkrH4a8t+z5DlDHhupTPjc7AXsBJBZFXui0qkwQiIytN7rDl8r7QFlBN7rL0W\nMrM2g3u2+nDMBdxtoaiIOzpJYP09XM++MzrkMANPIC4+6PWepYGjn3222MUONv8yxXs0JkVErqi6\n2GlSMoBbUS4YW7A+tTSp6Qscu0ZXZf3A3BLWaGkz9AYanrsIZf0lR8p0FFTqRIW8R5fqBIIJcpgd\nKry+OmhjrjgXvSFQHSFW552LsAFSN4YkZxPqbCtZTP4aAhIaBhJPXbphZSrZeyY6XJUUbbtXLpNr\nUB71arpUzY1JHkz+Ml4IPxk8epKFm+U2xZz+Vv7XlYAvyPSD5BT6+tOhlJy0WmE2mYlZ6RF0onZX\nn0uyC2ASnaXQEvBLoHyDBpAq17Ggo1LXeHkwkc3kjyyrvc2O1MzAz5WTdPE9sGmOFxSkR5/YB5pP\nG+dhbJwHdtiDTQQzgPo0C0gx3vEPDChUGA9XKHw2rmlUxXCkrHztMKlOn/Wy6ZFOskLeLxCPYjI2\nct2N7ZGiJzjfTkkL00ukh1SdGCWAU0XuEBNnDjBVMY26xHWmPHkK5aTVKbqd/IRIKmG8Cc9Rltta\nkYUetmu+wGoYTOXL9jYKEVEzJ2r8de6wIp8XS+zkv7Fn8Dkm8BqUbYUwp75b9+LEDcrae02Xwvx2\nl9AwVOc9Va7WgeFUP82ARahz0/ch0DhsQkGsiAnp+OvqVRlgZZVMj5FEwIp13lX0CqFT9iFNhgbK\nGLf2YGgCc9qmMF9oLN6tnSpcPE54Edp1Jf8EI3Tw5LLEB9JnVGYuViDJQZ9tRI02xloo7Kvj3IKU\ns99TAQVTz8Q+aU00jp/t43SLqKCBMFMcrnHx9DNK9dUfsA8Lpdu08A0CekpZwYe9RjmpabJEzrOE\nH1qEC81+5TQ/D2+I0xTud6SQbuy29crUCxohnZ5WKwayJyGTjhZcX2saGwcMgIRpdqRubP968zP2\nvY72MrAVTpO/J4fEV5v0zQcKHruHLIkP/7EnBBmAYIE1R9MPU/2sZhw1RNfjyKCHPcnX7hDXyYfC\nLicwqcZOqeUmuF6ub8r0f+pF8peJHg/j3LGrFeq0r6eWx/nrncNefHDkBryELaSspEq19wusAPZC\nKE6V7Q5V6wOWt9THiUj3QBku6526dBLRze3cocvBPoBzI9qOuwBVOoQEnZEbeq9iq5xzhuM5LaUR\nXJGp42g4MZpqGxYwcSRGTAXQqoLzGyggM+ynT47oXMlVqGO6pjYDpIscJ6QDlkbW4+0xjTLurw7V\nSN/st+ePnK894iUtWCflmWN6cWXKTEImVPMrfDRXzvIeo2AMhPE27s6sR3jqLJFtalQnamxkHKfJ\nMBExN3izvO5J8Zl9BmGLPUUvEzhSd9h8HFDpYtE6OV0Fo8o/tWQhmjqY35ntcUOeFUQe5YvHFkXh\nMjeGLuSpOJlKe5eYYtDHCtSS0ykcwp4J/wWlFWQ9jTR0GGI3ROy7iuTmRMUbxXWmDwe69yHM7XA2\nmGTtqujF6uFo5hq/XieUvTxb5bjEkZQsZALnre6GKNfo7RPjXGyl4o2P6ac48f5BHGYkq/ExOrD0\nBby5TOjxbmxpbIO8vlLgT5sDIKLJsArCfQT+NqZ1xbjp0dI6/ERE904R9a6uD5sj1mN+RSx9rCAs\nG4srqMuBjBYW9c86VeTzPu0cKfs+OVREukIgAJx2NhfgaJVVFxSen7hkg+cUx2N/beOsvJr0E4rX\nukA5DPb6tHFgFwS4JMOS+ml5m4rayglKeIc88bAl2yOU8JtNAOrkQdcVqd4tvE8o3t4uLhhgcb2R\nNZrmI2I6fM/s9f2z7Pfn/W2rMQnPULQxr+Ys/mycOOTXPEDsBb4vi8XJyxkifkZSf1r4d0I6pUKa\ndmsr45rAvwkb+LMw2dpoHB4nRBbtYJFErIsoLkXmF/gVkzWsY/6OaLtaN2zTGXx4I8wviqrqfVWW\n34dlU9dblp8Oti5MTdqIuG1CbMvGTDpM1akijcH3N8rDqlRFv0VJckWBU00XPKifH479XRV2+VbR\nTTw1R88HJiQ4FPnjknCpOe1OYxgGaQQAvwHbo5L2X/nhW8ilEpfjS7US7tNjNu3yEKMyUSDVeAuI\nEoKuuM/ESVBaJ+pMY8B3VRt+go3OkDOcymLTK6WzplWVOrpX4vsTKH2ZqUxVQqC1z+MGIwo5LWsG\nfNutTKXDHtAB98sj959amVop70x45zrP5gMtLljbJM5V82Einr67oQS9IP5OCjw1kHxPEeMBLh/Q\nXnsfPLC4kDk9gKG/5YANH8asfO/DMez2QnrbY2mZCdHKQiadXoYx0c5GzP0XPSTl2ufCejb5kB2q\nAIPeZCfx9C+VdeHS6Zjk3Y/6IBO+fsBSbOHPJ+SrPQwre1AfyVN66VH4ks0op2thd2seEd7X/TMf\nGU1ExIJ7u5QTsBJtk9vSO/UuY2UqOVOAucRywPwWbfqUal9fUNi7/s2TwePbGt+aLY/oixR2TWHO\n47tN0lhBFafZiavQx7zgU1LBgf/um54ubRUCW3qUPiZTVl1gK/blam6sfhrv4uXqVFajmCqmdx74\nii2uUpkewme0dwRZWm3cuThddqZwQcGMoosPANNpgmkChfM1y6+RrUwxden+HoWBRkd4k2j8uh0w\nQfCHL0gMTMrejvPgN9l8XzTkLuKFXOG9csFmi5C5/oI1c3vsXu0LutqKTYXqw2VWxEVvQoc/hEIr\nsSmibLjXs1Vm4I8sfI2TjnIsY5d7kzQ3jloGj42RVqKInpQj40SwkYceVstxDlZtQ61n/l0E5UfY\nHYZiWYAp/FLvOYfD7N8eZat8PUTlwKGqM5bHoYBn+uMVgpm3q41OxuaDkt9etfIcyMfPhidp7gQU\nnK1C/Uh430FthzqU+O3aEoArIiBCUCI+xKxZPo4VKXouWtiwP7g3Nu8cyZWL+keMgXj9gsVH9ukK\nJ+6lpQwlnSkLPZYSHxi/N+niEPVQbHjMKo4HE30+2tZkeFKIslNK4YzGCKv9nkGZOfb2Cg0bfDGB\nqTKlNsnD82i8b6pHVv6MuVwmj77a2Afe81Up+/S+Rikf3xMMwNMDPeWnMjDneO0ZH+hTFSbAvdS9\nOrLZIDzeYR26hjPGd/bvTOG8c4AU+2BYt1o5Hjv6IZjqJZucXiinqgepYkykvBcQQMUZkGSwKgpm\ncI9mrTryiqiisFCRM8Ez9zHsgjMVe0smxUhxjyi8aaJwmuLo6TBmKX/Nh9lWpsayMzceS3vJYVJH\nyrFj3MN3KjDTWNniEPbjQVsB1T7t26FCpc3HF+DyIrHJX3xBQQEB4/okzRK+qsauia5LFsKO+6sw\nbcYnU3a3EWsoHcrGYUPg0V2WcNMj62SdgD28YsiIAzHdbNxUbnsfMb3SNf43pgJd0WKNjsWNvQ4T\nILxJSUzgp3bsglYO1RnG4UWiT8bakZMUe+bWNVo5VPeuSv2wxTst+w5Hytqw0p31jnrJbJEE5J93\n2bRVi0zn19AwSSnE/icyuVWEi86Nm27VAZ3j1VFPM9HFafRQlNAG64pXzLaC8AwpDrvvL3SkiHw2\nna0qHNfnxPbPnXUenb2VBy6/zXOx92EopZNe76ydA6SsSTlE0W57Jd3h8hsYFhVGyV/eiwKtf5Fv\n6L0uw5GKqJ9ceOwjtklzUWdKV2KFxjt//BG0/UxhlwD9JQcPJ38/oRSQfxulAypcTKvCKprFVlBM\n7t1Gm31XRwBX6tO72f6/9t6d55qlZxOy1zu/alIkgiHiIJBAGkAEMwGCmJgICYnomwQQECA00giN\nREA0IT/rWybosuuyy66u7tXrvp/9TXnv+1l9qIOrymVfrlODBuBsuWav7c4oZJiojm+QmzTh7J0M\n4cqEBHWa1854I9Ikqivp/gsZjdIwzlA5nGWHbXxGS0ej96no7B1MDbeAvZhiegqPm3Xx4Yq5+5zq\nRPEL/uRFrwOBEhPbh7n015b9vToY0unA3t0jiBL3c4RxC9usnDjL9QmVM2RFunE1lwWVHgDXH/cw\nGNEaaamvCfzbGRF3HQW2x2STc5MPG8FlDJV2otvE3FcYeG7AECG3qvF0arhMuJWjz0QZVJOuy+KX\nsi299p2dNzWn6q028EX29XA9Tpap52EyHBCJIVIirOtv0HgMeuvZbt0/IAErdEQtB88lpiKG4kRI\nkSIgT4l9GUIhf5mNwqyyKfTIy7fIjDbOpZw08Ljz35sUZ/MzmcGb0JcHCx3bPoA/eGWalEFs++hW\n5/1DGvdEAKYjGkd3kWG8b5HcscUtHIey6tzRsUjXowstYtcrmezn7ZrXxmiz3IxFVHAPkBsyS2Rl\ntBENsqgMcOAxoaOKH+pTTsZP3oNYIw8GxIaRsyrRz8h6WOxqihHtNrPIgFQEQKyN9Lc/7Ydt5gqX\n476JbWaqi5CQzo0yaRuKfSxVzKHSPLtdMpwomkosrfaad5ePT6vVGA96JdRVlFWPPSGpIaa2gCkT\nh6NiD3C7cMxecbgnsOcQ2YATmIFIA5PAT6aub1Ghm8JoctpNCEQQ7mP66lDGw6lcKK1y6biISXEJ\nCPLAgIWE/Ppgd3vSg6bxO68rdOE7U77Aw0cljYe+3MyUBISr+k1f1tBNlTpCr9eL6B8d3+DRRuqO\njTpTrbLBiJlRYyJce3YIwImSRzln/H1EUiGjlUAVqCFCy4Uu1SUWcEQuggu9ZN82OY+JDApRd6R6\nWX4Akh55DWs5iLSu8AXWwBihrn91nmyNc9STDFWnjhSpslGr8yadwtKPf/alAJCYF0T3uyLTd8jN\nNPenlMoivM2eVG0ebcogXjMbaRHGSD7Xdu9OCmKIBqDxZ4SzU8AToAFHoJCJJxW6dWoQULYPcGOG\nTTD0CId6dK8r8DQtZJuIYCYKFXRrlbVTBaakfMVkulbKlo6wByx4qhap2noFnX/8vuRwoF4kiq0c\n4YIKsvdo3MmlV+lUf2zLrH8/3/c1V3JczHKVWMsX8jgLkdgXIqdzp/Ghgdg35cDJ0Qe+VZ+BUHAR\nNHHn4zjV+G0OSqfDbjirbCgWv910JOx0jOifHHumRFtZrMXNiSI5Vn20ZHvcHhoHiM1ZU7lhLCjg\nPBF6fJnnQDz8i1crSKR9c7g3D5ZHIpaAd5ARh2eun7jPQoirKZdiuYeVhkHkZyhL0FsqO4nQBfU6\nre5Kvk/62S7/Lq7YOeeahyfHFbZ8tHKe0auabHGZX+zxGahmFwJ0gnXlU8bkWOeLM10vJqK/EfHr\n1QZbRoGKRstwJkOjBJs1U8KWAwBVffGovC4lFus67zVmBC4asuxjpoI+iDpDLv8E7abty6SnNZpS\nGQKxpfodkp6LW5fsxyXY/zOMwBBVMqPvdBksgB9Vum3UiN9tSQUz6Yf+xg1mx0r2/sE5h8YsYbH0\nsR+0NB8UUpyVHh2qSP5wmWqZnlNzWRAne65ZkiwVAODcwOhI5QbIP7OhCJDXn4BUKGsH+NCjirt0\nGqdDv+zker+EsBCqOxTaqM2Zl362FHDn4o4juH0xEDXHwvOm8ktdOQ+N/rnAms3RXyGntw8AIz0w\ntb0IeDyz0+9HStzRICZERNQcKaGXvO2xqM4LdmgkfIjc+6fKda/WTCKzZ88oga4d1x2q5by7Elug\nTJib1TPdVOibzJEqc+jtkFb1owCALB/Tca36BF7Y3idBabDYSf0fHs9RJSP4tQNUhEiE7ZhzjW0n\nWJojpd+PIsNgVt+to6m+Qq4E9MGxPEvf9MWFJOJs5l1aQz64+kFjZRRSgu4vQzBxwUZ4kC1AhP6E\npkm8+2QyQETDue+QkbePP2GxRsJB5D4rdbY39yi8rvrRvox4C/GVz+883eGZ9DR1K4zq2BmtdvnF\nZX4BdAi5CohShHMkyowQJ50mEzPf+Y8jaaHzUvQoEQyL49etigLbrmlxM6a2HLFl6fZeQTr9+hmB\n9Z0g9lTNJ6JYtjda7zjAzpCOBw+Qqvg9IXF0hhWNEA2ATaLWD3zV97317M4p8M+s1JoyJcuQod4z\nZRTXtp9xiTOy1idMrhpYbSf3N+wFjOvdq4dViGpD+i2wXWZ1LzaV7R5/QDojrUsc85GoEUB57iqQ\nk1+7sZuYWEqqAyKkzqLXiR1iDx1qcIyfIV9WNJ/+wmvQ8NL1w3atyyZybB656LyIGvcAJ+M+SAr1\nAbKgLaD7Fvssz6FIdWCr+ojyE9Wb7pwwUQjoA/VbCzNiulHvursWxxwngrZleM7dvDP58HleTUdT\n1yvIAU/ijUzeJ4ERFHc9ZCLhsj7Exc/6zDv4KPUy6B3UR70r8dBfcblyxlpQ/z2ORPflQzulfUVi\nP9YACqKxzlUhSAfVzamx9Xnm6OgZfd0W2fK/1kDNNWpOFJOu89Pld/1UPttN1Wem2jSNzmpp0oP+\nZotJlrszTL0TfDoJeG772ZvO0zYE3sg7OL6t+mMzGS4d3AedcRtCW3rBQT416N1gDEuob9Ko8ZNc\ng1zhKYYzNo8MoO9Lj+/zx399IuOp3B5xdP56GPxWpwbvrSEWDulKVS7NTM1oVnnuY3mkhqFqoDDp\nhjLL1JQmAt9urKuDLY6LcXOwVaEoAPHKjFumFdj7FFhp+o7PRlXdjCHC5k3pgoGTcbYW2qIjIEIN\nqHGEdGQVciEKgo3mvYOw0YXux6n2508Zpmv0aV5V/FZ2oW6EiMgsHGn9tIUTzHQcY9biKpADefZJ\nRxMwKo0YdmkW+ALhx06RxhlrDPWTbRvpXt42CKiDDaN1/BG61nbYIwvt6jpt4nAymS4ck3HoMknX\nkNMhd0yjD0bdmTqS8ek846hKuOYO8lRRsiRh3+TqISYJAMd6VhMNgWVKHKreegwbdDRgxZY4Amvx\nEaEvR/g36s+szZ8QXmUW4GQqnFD4syzRQBHaDkyrSucEMhtOwGfs3pd9ZMieSWddHlUBOuCBfSfU\nXXe43hjN2VJ0yBwgbCskujsVZjuESOTdHKlj/xPbpikhac6YNKfOTqvVTJoOEbg9Tvpz6I106S6e\nniaEWOvVZryeUrHrqZSzGpzfZBKD3cLUnTVsbhdjCn3wD+oOROGnVpqmdKNh1rcZcJdlwt9gOEyN\nC8WZudFBXecxPXzKBq9axq7p1tK/NTM1MujuLE5kPJjw1llPGFUFo84UkwN3XYGK58NuEkerKVXX\nuYXMAPUZKW+sHh2dLhqo55hL83GQQQKWg10dcEEp6MH40DEKBTjfpxPMerx3AunXCvaVa5p4ysGH\ndGIrn8ukkS0TQsWoRkwIa/FY3vfqRkYgLT5qjwm+OYX5GQhJaivqJW2Hx7Rx32jaB3f86XgY7ucp\nahYkCeFmhKARCvvjlM6xnFAX/HFJZgySOF4cHsePQRZ8duPWw3t37QBVo07V+8XiLVPQ+4NjF8PF\n6zSwU2VHt1WbJjaTj/XO8EdEzW7ZlzsCzAVnqlUeWLiBl9Mqe1yhEhF6prFLibs4SQ7sLZBzqlKh\niPY8C4N6CsDwNL0sFfUFTNmVYa+TWPuancSMXcijrl/S8I86MkIGxLtTJhbHrA/3neg62yHNYXrr\nKRJCxG8hendnCs2S6KAyewZRfodyEFHbTagaiUx+HD6xL7hdq8IbtNQlSl3XeWR8lOWiwkOtjQKG\nPJ7nfaUfdEEdz/+2Q0W0UHlXPC+QIT2tO3kHgbreGR4XeQqlFZd/yoGCI3WfLs5MqUVUoKzmEjw5\ndJ6cQwW8W9oxM1USPW8VLFUah3H2+QKGDDUi8KfAz5fNxbXnABJGFj+maomdDB1uJFNc4fUhExmY\nxR4hg4z1vs4NGIRdE4mUQe0fQRLc4pe9+ZNm8PpJyw9F6UrbMlszqOmmxITPvlG/pY3fR3CAVM1O\nB53CQvzu4VQOh42sbs1Q58HKNAW5GP8TGvsNPifC6XlJQ1zO8QLfPWjXRwMNOoGGcOdG61GEmpOg\nExXKoqo3eYbhvX4bkofnoBPbP3gUr5vx50Q2XeYIsL2iR//fL/MLsvLt6g192GvdmHkGDwHoEBhm\n6MP90yBNl0JMvzg98jCmX4fLSeAqP2fsOnlnr5cZrVQ0FRZ+WAniWRq+/cdWlf5ao+PNSWf1q0hi\nm2NN+XvHi3YPoWeF0/E2eQkADzmE7VOkM8m63E6afbdZJ7GdS7Yk6/jY7vtwmkSI30IvTaP9xT6h\n9h51p+UY8BSFEH1JlVi7HQO25PrHRwSrfJzUpStvulyPJ4BWnGj6Anc9WYcxTG4CSuKxbUdM0dN3\nOE8V9MjSYCY+dQiylNYcuqK/p+FAttyBUFXYMwYStFzVW0zf+kWPh/qt1BEJLZ7mN2o+v5Y0vB0c\nqm7AtW/lH1LV+B4aHUtHYA2wAX5DGP3HBLmRIKfREz7S8+oWG9uZDXv1A7BqSvnSRoZOjYoi26tG\nNlLq445hxF+QOQw9p/ZvXitoIM/w2HfpJLMMh0sWMpoAhEntaH84nUzsrx/OgLr33FBDQzmUUIaG\nPD+nelkPkVd2THkju9Qe4GhGZ3WZNTK++z3q/bWYlRpYz+s6fhfQvxt9Hk2pp93hhgEor2YTxhIe\nhagvF+YmxkkHK3TPfcryiPzdybAvZRpAmCbtlo9kcInIv472JrTREo22av7h6Ssk4Xcsu+eBqc+q\ndwlyMg3L0LAtmH2YQei46VUDEBW/TK7Wh07goVJmR12+3yIeLsi35SEILN1hQntkjgqBM9UcqcGZ\ncr9vc6hIDqfGHC2oj74yRvxXF8yuRQuDKErtoBDz+xjkNUehf4Ykc07uUkcmC2GzZV7TdBMe4zYN\nO9xKZbTHrAeixv7F+FFjdQYipsV0nPj8NDJddaCyeE/xGvp7S9/tzZ9+9LjbSfO9dMXNxfItH42O\nbPRuFLQa3HqHSmWBTVgqfehHlVqeQu3kpQ7dcXtuKUPmSBWdvilzdAoGQe9Y4tedKKTKobKrMFJK\naa2HDeGETmrWwrlrIe7dWEujIgr338CxjnEODwvpKxwqn+ghdb12dGmeKs76NDQ9hIWJ7DjU/p2p\nnCX0eqGfJ+HqJL5HVx2TM2frUz7OHLncNH7jSPk75PsZ9kt8HMFXkDap3xGBLmYK7xGOYK8u9KuE\nWgw4MNqFGtw/aVgnWSjwqNXUPAkFnME3i6SHFmQQ2YP8hIdi1u6cvuVQnTlRmrf+aaHeg0316lhC\nO/TlaoxAZphVUmbOyjVR5E4+uyYfywSXX1MPBbKwVQ79cHJSNrQYxpPfI2XO1Bv2RRH+HgdM6Ad+\nWQgWUEREcVSWOW7Ipggw4dupS6MQyds904Fydab+JEyVka+PQh7NqWGvozs6H1LKCA+wOgZVoI5H\n6AYPXA2f5rNKETmfh37aobrb+eKhYtExgqcSJZ6TfoD81LTgTK1UkOtlJ8+9M9N1Qo83xMoxEIRI\nhJwpd+YL8keD/w64WuLB6fmsQtZHXU64OXk2zwOh2e3srtApaxwcy7sJ1Yze6fr+W0fzjAm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HtAtNZgFZ9hxcKJQzVP/69IcugtPg5jECFb7ycvSsy2OiXHq7fNTgGacSIbRqgV0HOPY59P0OQ1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\nTrwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6dd878ccd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_samples_to_display = 10\n",
    "pos_indices = np.where(test_y)[0]\n",
    "pos_indices = pos_indices[np.random.permutation(len(pos_indices))]\n",
    "for i in range(N_samples_to_display):\n",
    "    plt.subplot(2,N_samples_to_display,i+1)\n",
    "    example_pos = test_X[pos_indices[i],:,:,:]\n",
    "    example_pos = np.swapaxes(example_pos,0,2)\n",
    "    plt.imshow(example_pos)\n",
    "    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')    \n",
    "\n",
    "neg_indices = np.where(test_y==0)[0]\n",
    "neg_indices = neg_indices[np.random.permutation(len(neg_indices))]\n",
    "for i in range(N_samples_to_display,2*N_samples_to_display):\n",
    "    plt.subplot(2,N_samples_to_display,i+1)\n",
    "    example_neg = test_X[neg_indices[i],:,:,:]\n",
    "    example_neg = np.swapaxes(example_neg,0,2)\n",
    "    plt.imshow(example_neg)\n",
    "    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')\n",
    "plt.gcf().set_size_inches(1.5*N_samples_to_display,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Neural Network with 488382 learnable parameters\n",
      "\n",
      "## Layer information\n",
      "\n",
      "  #  name     size\n",
      "---  -------  -------\n",
      "  0  input    3x25x25\n",
      "  1  conv1    7x21x21\n",
      "  2  pool1    7x10x10\n",
      "  3  conv2    12x9x9\n",
      "  4  hidden3  500\n",
      "  5  output   2\n",
      "\n",
      "  epoch    train loss    valid loss    train/val    valid acc  dur\n",
      "-------  ------------  ------------  -----------  -----------  ------\n",
      "      1       \u001b[36m0.36777\u001b[0m       \u001b[32m0.15799\u001b[0m      2.32782      0.93933  28.52s\n",
      "      2       \u001b[36m0.12145\u001b[0m       \u001b[32m0.10520\u001b[0m      1.15439      0.96125  28.90s\n",
      "      3       \u001b[36m0.09681\u001b[0m       \u001b[32m0.09278\u001b[0m      1.04338      0.96664  28.26s\n",
      "      4       \u001b[36m0.08882\u001b[0m       \u001b[32m0.08770\u001b[0m      1.01280      0.96863  29.03s\n",
      "      5       \u001b[36m0.08398\u001b[0m       \u001b[32m0.08470\u001b[0m      0.99148      0.96944  28.79s\n",
      "      6       \u001b[36m0.08053\u001b[0m       \u001b[32m0.08179\u001b[0m      0.98465      0.97086  30.24s\n",
      "      7       \u001b[36m0.07790\u001b[0m       \u001b[32m0.08068\u001b[0m      0.96560      0.97117  28.22s\n",
      "      8       \u001b[36m0.07585\u001b[0m       \u001b[32m0.07968\u001b[0m      0.95190      0.97145  28.95s\n",
      "      9       \u001b[36m0.07412\u001b[0m       \u001b[32m0.07874\u001b[0m      0.94132      0.97165  28.29s\n",
      "     10       \u001b[36m0.07269\u001b[0m       0.07930      0.91661      0.97158  29.38s\n",
      "     11       \u001b[36m0.07142\u001b[0m       \u001b[32m0.07742\u001b[0m      0.92251      0.97226  35.95s\n",
      "     12       \u001b[36m0.07033\u001b[0m       \u001b[32m0.07703\u001b[0m      0.91312      0.97252  37.72s\n",
      "     13       \u001b[36m0.06937\u001b[0m       \u001b[32m0.07682\u001b[0m      0.90292      0.97266  33.58s\n",
      "     14       \u001b[36m0.06849\u001b[0m       \u001b[32m0.07669\u001b[0m      0.89299      0.97258  28.73s\n",
      "     15       \u001b[36m0.06768\u001b[0m       \u001b[32m0.07562\u001b[0m      0.89500      0.97305  29.28s\n",
      "     16       \u001b[36m0.06699\u001b[0m       \u001b[32m0.07437\u001b[0m      0.90078      0.97342  28.86s\n",
      "     17       \u001b[36m0.06635\u001b[0m       0.07446      0.89104      0.97347  28.28s\n",
      "     18       \u001b[36m0.06572\u001b[0m       \u001b[32m0.07435\u001b[0m      0.88393      0.97341  28.90s\n",
      "     19       \u001b[36m0.06516\u001b[0m       \u001b[32m0.07397\u001b[0m      0.88084      0.97349  35.94s\n",
      "     20       \u001b[36m0.06463\u001b[0m       \u001b[32m0.07329\u001b[0m      0.88184      0.97378  38.06s\n",
      "     21       \u001b[36m0.06417\u001b[0m       0.07343      0.87390      0.97380  37.46s\n",
      "     22       \u001b[36m0.06374\u001b[0m       \u001b[32m0.07285\u001b[0m      0.87490      0.97400  37.92s\n",
      "     23       \u001b[36m0.06330\u001b[0m       \u001b[32m0.07229\u001b[0m      0.87566      0.97419  37.49s\n",
      "     24       \u001b[36m0.06289\u001b[0m       \u001b[32m0.07222\u001b[0m      0.87083      0.97421  38.04s\n",
      "     25       \u001b[36m0.06251\u001b[0m       \u001b[32m0.07174\u001b[0m      0.87139      0.97445  37.46s\n",
      "     26       \u001b[36m0.06216\u001b[0m       \u001b[32m0.07062\u001b[0m      0.88028      0.97484  38.07s\n",
      "     27       \u001b[36m0.06182\u001b[0m       0.07091      0.87177      0.97466  37.36s\n",
      "     28       \u001b[36m0.06149\u001b[0m       \u001b[32m0.06962\u001b[0m      0.88325      0.97539  38.14s\n",
      "     29       \u001b[36m0.06119\u001b[0m       \u001b[32m0.06944\u001b[0m      0.88112      0.97547  37.42s\n",
      "     30       \u001b[36m0.06091\u001b[0m       \u001b[32m0.06929\u001b[0m      0.87909      0.97553  38.53s\n",
      "     31       \u001b[36m0.06061\u001b[0m       \u001b[32m0.06866\u001b[0m      0.88279      0.97580  38.38s\n",
      "     32       \u001b[36m0.06037\u001b[0m       \u001b[32m0.06849\u001b[0m      0.88133      0.97582  37.56s\n",
      "     33       \u001b[36m0.06013\u001b[0m       \u001b[32m0.06826\u001b[0m      0.88084      0.97601  37.36s\n",
      "     34       \u001b[36m0.05984\u001b[0m       \u001b[32m0.06802\u001b[0m      0.87974      0.97601  38.88s\n",
      "     35       \u001b[36m0.05960\u001b[0m       \u001b[32m0.06764\u001b[0m      0.88120      0.97631  38.74s\n",
      "     36       \u001b[36m0.05936\u001b[0m       0.06817      0.87075      0.97607  40.44s\n",
      "     37       \u001b[36m0.05915\u001b[0m       0.06810      0.86866      0.97614  37.57s\n",
      "     38       \u001b[36m0.05893\u001b[0m       \u001b[32m0.06752\u001b[0m      0.87272      0.97635  39.24s\n",
      "     39       \u001b[36m0.05872\u001b[0m       0.06759      0.86874      0.97630  36.58s\n",
      "     40       \u001b[36m0.05852\u001b[0m       0.06781      0.86294      0.97609  29.01s\n",
      "     41       \u001b[36m0.05831\u001b[0m       \u001b[32m0.06711\u001b[0m      0.86881      0.97646  28.24s\n",
      "     42       \u001b[36m0.05811\u001b[0m       0.06733      0.86303      0.97626  28.95s\n",
      "     43       \u001b[36m0.05791\u001b[0m       \u001b[32m0.06666\u001b[0m      0.86867      0.97662  28.26s\n",
      "     44       \u001b[36m0.05774\u001b[0m       0.06728      0.85812      0.97626  28.97s\n",
      "     45       \u001b[36m0.05755\u001b[0m       0.06682      0.86130      0.97662  28.25s\n",
      "     46       \u001b[36m0.05739\u001b[0m       \u001b[32m0.06661\u001b[0m      0.86155      0.97669  28.96s\n",
      "     47       \u001b[36m0.05721\u001b[0m       0.06666      0.85823      0.97665  28.28s\n",
      "     48       \u001b[36m0.05706\u001b[0m       \u001b[32m0.06653\u001b[0m      0.85770      0.97666  28.97s\n",
      "     49       \u001b[36m0.05687\u001b[0m       \u001b[32m0.06641\u001b[0m      0.85635      0.97677  28.47s\n",
      "     50       \u001b[36m0.05669\u001b[0m       \u001b[32m0.06641\u001b[0m      0.85368      0.97659  29.92s\n",
      "     51       \u001b[36m0.05652\u001b[0m       \u001b[32m0.06597\u001b[0m      0.85682      0.97679  28.33s\n",
      "     52       \u001b[36m0.05637\u001b[0m       \u001b[32m0.06557\u001b[0m      0.85968      0.97707  28.46s\n",
      "     53       \u001b[36m0.05622\u001b[0m       0.06587      0.85347      0.97680  28.16s\n",
      "     54       \u001b[36m0.05605\u001b[0m       \u001b[32m0.06538\u001b[0m      0.85737      0.97714  28.90s\n",
      "     55       \u001b[36m0.05589\u001b[0m       \u001b[32m0.06509\u001b[0m      0.85867      0.97735  28.14s\n",
      "     56       \u001b[36m0.05573\u001b[0m       0.06521      0.85464      0.97703  28.67s\n",
      "     57       \u001b[36m0.05558\u001b[0m       0.06530      0.85107      0.97700  28.10s\n",
      "     58       \u001b[36m0.05545\u001b[0m       0.06523      0.85004      0.97709  28.74s\n",
      "     59       \u001b[36m0.05528\u001b[0m       0.06523      0.84744      0.97710  28.12s\n",
      "     60       \u001b[36m0.05514\u001b[0m       0.06539      0.84323      0.97696  28.62s\n",
      "     61       \u001b[36m0.05500\u001b[0m       \u001b[32m0.06459\u001b[0m      0.85150      0.97725  28.08s\n",
      "     62       \u001b[36m0.05488\u001b[0m       0.06464      0.84898      0.97718  28.54s\n",
      "     63       \u001b[36m0.05475\u001b[0m       \u001b[32m0.06440\u001b[0m      0.85017      0.97734  28.08s\n",
      "     64       \u001b[36m0.05462\u001b[0m       0.06455      0.84628      0.97726  28.55s\n",
      "     65       \u001b[36m0.05448\u001b[0m       \u001b[32m0.06388\u001b[0m      0.85284      0.97756  28.11s\n",
      "     66       \u001b[36m0.05438\u001b[0m       0.06394      0.85049      0.97762  28.59s\n",
      "     67       \u001b[36m0.05424\u001b[0m       0.06418      0.84506      0.97748  28.06s\n",
      "     68       \u001b[36m0.05411\u001b[0m       0.06471      0.83619      0.97729  28.71s\n",
      "     69       \u001b[36m0.05399\u001b[0m       0.06426      0.84023      0.97751  28.15s\n",
      "     70       \u001b[36m0.05387\u001b[0m       0.06434      0.83733      0.97723  28.54s\n",
      "     71       \u001b[36m0.05376\u001b[0m       0.06436      0.83533      0.97736  28.06s\n",
      "     72       \u001b[36m0.05365\u001b[0m       0.06465      0.82981      0.97737  28.61s\n",
      "     73       \u001b[36m0.05354\u001b[0m       0.06487      0.82535      0.97731  28.11s\n",
      "     74       \u001b[36m0.05344\u001b[0m       0.06420      0.83230      0.97742  28.83s\n",
      "     75       \u001b[36m0.05332\u001b[0m       0.06401      0.83312      0.97746  30.13s\n",
      "     76       \u001b[36m0.05321\u001b[0m       0.06393      0.83226      0.97755  38.21s\n",
      "     77       \u001b[36m0.05312\u001b[0m       0.06434      0.82550      0.97725  37.18s\n",
      "     78       \u001b[36m0.05300\u001b[0m       0.06411      0.82675      0.97742  38.28s\n",
      "     79       \u001b[36m0.05290\u001b[0m       0.06434      0.82219      0.97739  37.48s\n",
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      "     88       \u001b[36m0.05202\u001b[0m       0.06437      0.80814      0.97741  38.32s\n",
      "     89       \u001b[36m0.05193\u001b[0m       0.06398      0.81171      0.97746  37.57s\n",
      "     90       \u001b[36m0.05184\u001b[0m       0.06432      0.80592      0.97744  38.14s\n",
      "     91       \u001b[36m0.05176\u001b[0m       0.06418      0.80644      0.97749  37.39s\n",
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      "     93       \u001b[36m0.05158\u001b[0m       0.06419      0.80353      0.97757  37.76s\n",
      "     94       \u001b[36m0.05150\u001b[0m       0.06417      0.80251      0.97752  38.18s\n",
      "     95       \u001b[36m0.05141\u001b[0m       0.06445      0.79769      0.97732  37.69s\n",
      "     96       \u001b[36m0.05132\u001b[0m       0.06417      0.79971      0.97741  34.99s\n",
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      "    100       \u001b[36m0.05101\u001b[0m       0.06448      0.79116      0.97723  28.85s\n",
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      "    102       \u001b[36m0.05088\u001b[0m       0.06480      0.78516      0.97713  28.81s\n",
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      "    500       \u001b[36m0.03710\u001b[0m       0.06516      0.56935      0.97882  28.64s\n"
     ]
    }
   ],
   "source": [
    "def CNN(n_epochs):\n",
    "    net1 = NeuralNet(\n",
    "        layers=[\n",
    "        ('input', layers.InputLayer),\n",
    "        ('conv1', layers.Conv2DLayer),      #Convolutional layer.  Params defined below\n",
    "        ('pool1', layers.MaxPool2DLayer),   # Like downsampling, for execution speed\n",
    "        ('conv2', layers.Conv2DLayer),\n",
    "        ('hidden3', layers.DenseLayer),\n",
    "        ('output', layers.DenseLayer),\n",
    "        ],\n",
    "        \n",
    "    input_shape=(None, 3, opts['patch_size'][0]/opts['image_downsample'], \n",
    "                 opts['patch_size'][0]/opts['image_downsample']),\n",
    "    conv1_num_filters=7, \n",
    "    conv1_filter_size=(5, 5), \n",
    "    conv1_nonlinearity=lasagne.nonlinearities.rectify,\n",
    "        \n",
    "    pool1_pool_size=(2, 2),\n",
    "        \n",
    "    conv2_num_filters=12, \n",
    "    conv2_filter_size=(2, 2),    \n",
    "    conv2_nonlinearity=lasagne.nonlinearities.rectify,\n",
    "        \n",
    "    hidden3_num_units=500,\n",
    "    output_num_units=2, \n",
    "    output_nonlinearity=lasagne.nonlinearities.softmax,\n",
    "\n",
    "    update_learning_rate=0.0001,\n",
    "    update_momentum=0.9,\n",
    "\n",
    "    max_epochs=n_epochs,\n",
    "    verbose=1,\n",
    "    )\n",
    "    return net1\n",
    "\n",
    "cnn = CNN(500).fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make predictions and evaluate on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = cnn.predict_proba(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v2Rc4hzQ+99KIOKudMi3A2cBA4OWI2LOGYZtZE6q25UxRx2YmSSOBMRGxb/b6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\nZmZm1kCcnJmZmZk1kP8f+ijr6vxhQ0oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6db7ff5bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(test_y, y_pred[:,1])\n",
    "roc_auc = metrics.auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "precision, recall, thresholds = metrics.precision_recall_curve(test_y, y_pred[:,1])\n",
    "average_precision = metrics.average_precision_score(test_y, y_pred[:, 1])\n",
    "\n",
    "subplot(121)\n",
    "plt.title('ROC: AUC = %0.2f'% roc_auc)\n",
    "plt.plot(false_positive_rate, true_positive_rate, 'b')\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.ylim([-.05, 1.05])\n",
    "plt.xlim([-.05, 1.0])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "subplot(122)\n",
    "plt.plot(recall, precision)\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall: AP={0:0.2f}'.format(average_precision))\n",
    "plt.legend(loc=\"lower left\")\n",
    "\n",
    "plt.gcf().set_size_inches(10,4)\n",
    "\n",
    "#plt.savefig('figs/plasmodium-patchevaluation.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6db813e910>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NgzVrWnMEERGp1mSSd869BexroljEWtCHD1dNXkQkUiLVJj/GzNaY2QIzG9KaA6m5RkQk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\ngY6KCn8QaJ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6de5087410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(test_y, y_pred[:,1])\n",
    "true_positive_rate.shape, thresholds.shape\n",
    "plt.plot(true_positive_rate, thresholds,label='True positive rate')\n",
    "plt.plot(false_positive_rate, thresholds, label='False positive rate')\n",
    "plt.xlabel('Threshold')\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Examine mistakes to understand network performance: false positives.** \n",
    "\n",
    "Find the negative-labelled patches with highest prediction score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gbiCjfkJ1Yk5/3Qe/+o/SJStqxtuiPQmSWENT/qYSFek7wyclGCgN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7c7lXZaRjqeCoUoSW/y2wh31poxm89GruuhTlCWnDeAANP4G6g5u+o3oNks8RH825dA73ZC+ZAB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\n3ZwvoUd6G94BffZsV6aQ4Lkid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/7Pk3Q7g379MAjnyTl4+NLv4Gpdfj2w+3mN/Jwp8Arw\n0SHvioHXLIflCfr+c6Vsvv+64pt3Yv4Hq8a3dQG4VEuXtN6ps4vH9BEdH75fLlVf/rD22SPO/Z2O\n/2+EGanJAftTkbtZGrvxHA507SEHPqOz/gF0x+Vx5btnntsYd3G7CZ5421/7jZ5iub+Ef6IOrn2Q\n0t/6+GnXyR4PbmvgWHxVB7q+gPmZVlJ7iJejbAdyrsrbF/c3SMbkCl4oZ4rAObj8sQj6vmCy0dvL\n7D4/99Nn/qwL+iaiMikqFHr4Bn3f2Isr2afJvN87F6tnH8H+jNFz8IOsvQjPeTScjfyTA7AG+wD2\nchj10kTT8/Be5aeXP14DuSDwlRxuHyeS7iZT3wlXGS2/emcXzDwKxR6ei/5PDjv93wO6y6n5AT40\nfQwfmr4fPjR9P3xo+l74Cz2BD00fwYdH3w8fmr4fPjR9P3xo+n740PT98KHp+2FH04eB/gc+8IEP\nfOADH/jABz7wgQ984AMf+P8XvHqa4Qc+8IEPfOADH/jABz7wgQ984AMf+P8QfAL9D3zgAx/4wAc+\n8IEPfOADH/jAB/4XwSfQ/8AHPvCBD3zgAx/4wAc+8IEPfOB/EXwC/Q984AMf+MAHPvCBD3zgAx/4\nwAf+F8En0P/ABz7wgQ984AMf+MAHPvCBD3zgfxH8v1D95K5gYlFrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6db82ec250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "neg_indices = np.where(test_y==0)[0]\n",
    "neg_scores = y_pred[neg_indices,1]\n",
    "neg_indices = neg_indices[neg_scores.argsort()]\n",
    "neg_indices = neg_indices[::-1]\n",
    "\n",
    "neg_scores = y_pred[neg_indices,1]\n",
    "\n",
    "N_samples_to_display = 12\n",
    "offset = 55\n",
    "for i in range(N_samples_to_display,2*N_samples_to_display):\n",
    "    plt.subplot(2,N_samples_to_display,i+1)\n",
    "    example_neg = test_X[neg_indices[i+offset],:,:,:]\n",
    "    example_neg = np.swapaxes(example_neg,0,2)\n",
    "    plt.imshow(example_neg)\n",
    "    plt.title('%.3f' % neg_scores[i+offset])\n",
    "    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')    \n",
    "\n",
    "plt.gcf().set_size_inches(1.5*N_samples_to_display,3) \n",
    "\n",
    "plt.savefig('figs/plasmodium-falsedetections.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See highest-scored test patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b/mO0hyt2UqfkDuuSg28Yvp64v9iONmaaJg184drrNxiPoo8mq75s2lj6sjYP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/F71e08lP0MbMUiVnlNT7kE4TVMH0Tauby1SRtsQknWZ0nKI/pkK6RNWNPQjU/V4DfSIF98dR9t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\nQ5p8s1V+/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e9/2WEtmMd3LMNlbLBeGfs6rtKJ02S1ojDhgMtBYqfHwlCDLvRZaGVC0Om0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6de51b5e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob_range = [.95,1.]\n",
    "\n",
    "tmp_scores = y_pred.copy()[:,1]\n",
    "tmp_scores[tmp_scores<prob_range[0]] = -1\n",
    "tmp_scores[tmp_scores>prob_range[1]] = -1\n",
    "\n",
    "pos_indices = tmp_scores.argsort()\n",
    "pos_indices = pos_indices[::-1]\n",
    "\n",
    "N_samples_to_display = 12\n",
    "offset = 0\n",
    "for i in range(N_samples_to_display,2*N_samples_to_display):\n",
    "    plt.subplot(2,N_samples_to_display,i+1)\n",
    "    example_neg = test_X[pos_indices[i+offset],:,:,:]\n",
    "    example_neg = np.swapaxes(example_neg,0,2)\n",
    "    plt.imshow(example_neg)\n",
    "    plt.title('%.3f' % (tmp_scores[pos_indices[i+offset]]))\n",
    "    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')    \n",
    "\n",
    "plt.gcf().set_size_inches(1.5*N_samples_to_display,3) \n",
    "\n",
    "plt.savefig('figs/plasmodium-detectedpatches.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See lowest scored test patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lRqlbFm9JkZNHpkjYi0HxAjRPGZcUUHBHgUJZoI2t3oEqtDVE5YMUMv7oO9vN\nIxhDye9MhMfoEAfR7l4WGUNJmfTsGA3nmfom2LriKoKrKF61bCBcQdh8nhnghftA6GRjknuN6SlY\nNBNN5KpoLwB9sPPoQsCFLAKfaVEjCjUcewPs9+LDIgHIRR9tsOkvsX9Q8b/N6F8UVaU5qisfyyLX\nTjlHA6OWkbjMBYYHO4sNDYPETS6Lrj1K2zTOJfTCZKAWeWDecZ4uNOurt9pkgNAo1kpd0boVWlrE\nC6Gr5eik442AWMlAiBBr02StQHFBLeTTRgbwMkCDSAQyg0oFVtRwfrZHKFYjmKMkxcqpMiZrMLgh\nn46Bz/GPps9vbUfAf1wrskRyWlVzgM8ug4ESuSWvm2BTqUYlaox+Wu+iGLbJu+m4H96wpNpBAPa8\nPwIi/lnV5omOMHQqif28fNtnN3N9RcRZdYFu7uJ6PX4PH71hxnVAWNFZLMGCS/sUc8KyJT3zJabZ\nPEAi7lz2ed6mDhRdBb2L23VaAjaAeN2LfDAqbb3TdYqslwGMqB3HeTqAPlPyXBFA3zLyGvu88UXd\nAaVoAoviqvVXA2YUMwXHsBpSA8OJM96fCpB6lvagApyOM1DCbj/qx/fk7REIq+cD6r0vaviYjVT+\n7gpaBazdHiGi4xEg8hPHlLxuToDeHfibfW8WWuglt5vcfomg9+3qbY9x9/qpgxcFu57QrpBrxzUX\ns7GVB9hXwIgiotSAlRQbBFcVrKti3azwcGITbeUJrW+pZ5FEn7ZuY/JBYTUpMv0JZjjWgNH4PL+J\nFmgRnyuvCuzM7/ad9inB/C+mqoPKyxAO5HkB0xNmzQKDPjhzdRtG8nKseDNkArG64ybaYTjKaua2\nOWPyEYZzeXfbqgfmd/s8fSXQn8F+DNQwv+8YhTuDqgz4Q0k/PBbj0DDUd9GFUG5ww80vKxiFFswZ\nYsuYtebev2aCtrMvACiC1hWdrdBaRA6z4IynfkWUFs8A+gH28+nj0yNUOkOOMWujYFtPhTGEcXa6\n6s3PTlnzmIP3gjCGYnCMSNN4o2x7IgP5amA/xRIBWAj0wqCfEfr/99DLH/DijkGVfWz+zvtjZIQo\nuz7jjAJji/mHmgongH4DmWeWx2Bltr4mCyy1rCl00YGTyGsksBlcFs2PNoh0NsKMSQLsV2H8g4F9\nZ1m09xFpRPSrnVmMuwD5W4J8cYAfG3AF44qokupTcMDonjdpdqSlMzcR8KY23WRTyCtGEZ8ESl4p\nPDJHiBDZHr59AAAgAElEQVQVgtWzejYJoK8J9KluV1ibbw70tx8CkP+iQT5w30hD2fuYLq7omBo1\nondUZLLJGnK5Mi8tFZG4hykN93hWGz8Uso0iClXPM9m0d2TMwPOIhkYOmUdKVsAVYs7krliaYqGa\nmKoIo0FBEK+8L/DK+grkFB4hePjJ5cwAHVFYLaI1MVI0BcQzItBV14e+mIF3LZ4aAKC72GnAm836\nOVBfdMduv4PfGO2gGc3nNJh9bnRTL4ToLcYBbA3cjjn4I6Kf2ROTbH2MPgPyq0HstkH+USO5NxQO\nrWIb7Wwk5D6zqwhWhFjhxWObj4QwPG/vNKJ0fk+X70qS1b8tEmsBlNbKVVI2GDghd8Q9GtEXBzU2\nrc5X3lBbzrX3PpbZdN0tEtllYXeGDRPvXJ/HHAjGOvLoHbwrulyDel8KU8t4yFT6YSMsi2LrtmTj\nIrZcZjgmWob2PW14Ynz0CqsdYJ8InOeZLfKWZv46orAXPRvpdsqJN+bOFq9PegDwA0Uf2VtZeVjH\nf2JjMZJLN8/GYO1ouoFXW2wYxIDXfMh9gH2v02LTbgnaGcKSkjjVWNqCQT7tcQf2H+Ps9ioJ8rkD\nnQVXgtdvKGB/YV8hhMFNQY2xQnGF4KIb1ivbcqAbsu5EYIeYujTqXhTgCRvvTDp0Mk/tdjMFFNnf\n4u/KI/t/L5duRvOhja0/Etg/sn9p+hzjCRj63MbXkUQA4MCec9yDUED+AP8J8Bm7lTYGyI89p209\nsh1tUzjfoy9S0RH5Ll9m538h0A9wfwD0a0R+zxIMJT1HTirA1+lvmvbxZxEwENymqSGdNHUuRRAj\n1uw0MNY9JSYj+qLg9BCH97vqTMpEhEc90Zl6JPH+8e/87sHD0tCfHSR6+NE6lhYnxR3Paw7O2s4F\n9JOMNkBV1vMzu7lEtQ94BIxgEX0m0MLAhwX42aNV92eqyqZSGSCjw4xzAxgqEKsWoAOSISFAlBxb\neMRYLEJ0Jc9raTHgMVK2NkVbKNPLhmJzSeygaTwxAT2ijm7Gc+kLwcfyerNw/oLO8ibtivxp/RAG\nSZggIw6W0X2tIL9G9AWbdLxKx1U6XrXjVQVXVWywKvshfK2XETbY9IguhE091VmtKm+mj17teFdk\n9XITF1S630gpVbFA8AD6bgh2zwiISP5GBvQd8OP6EEvf4nb5HG34o2jE6b6ya9Hop4PKmIlos6fl\nju7iRkO8hgyJgHJtSxV+QkRfD+RYeZ/xfYk613eZU00T4Oq4TJ4feTsL4Bt51g9g8iCm86RFOUWh\n1VFk+ktC/qgtnWQ8bUinqyKV0aiO7mNd7ToGNgKsPmryV51/rw/OBoYMFt38hKbtcwB+/m3dB0No\npE+nsewGc4uIKtLgYvJpEmRLHjLF0o4W0Q+HNNwQ3j/KM5wnszNqfr87dCPP9+cPSXFP3/k1HExG\n/7GIvqITg3ehkYOfl3uMO2iKALisJwfRqq1knWDcz6dv2bLHAVzfT1FXSZXQPT3fVlVR9E2wXR3s\nSynAqr6EKooZr7W+kCbL7IOPWXfYIwzvqAnh487GsaXnkoN8bsDSGH1TtEX3wSPXQcP2KI6mUaXP\nv5r5pAeZoM/SFSYjCR2qJgcoMzurPgj5WPpkdTAd9cfdI1YdV6+lZUpGODetL11JcSFBJ8JCMOeL\nh/0HuB9F+CyaT751C7q4g4/YpldZFsawNwKy7gtFPsbb7VO3WmDdVgFIPbvLQCKfbqRmL/rqQVco\nLupZKgvQhdG7IOQn7fgHrKlzhoQVGNCUqJPArimyIHfpbqrFjgSi5stXD9XIJsJeYunBp/fTW7op\n9oo9/qwtfYtZqHy+vayfQ9P35W+TBaZDyPXOmOIz7Ku0g0p9mbxWnQZw8157boazZ7zHMX1FMb57\nKTn1IaaBf8O0owcZ3tU9zd3jrY0Of6f+9LakiXi/5pTVDMEacyc0fm3/Mdk654yGrHKsbMLnUdt0\n9ch31ZQ688wnEUUnJSCt6d25nxsw8/dS9pWHMgnqavgeOXnMCHSoilCdAfxmI07D4sdmIL8psCjw\nQrZ992yg/wZvQhjq7sDx9/6qlo5MmSZoa2IyRG3rbMvudPK37Da6qVvxuLYQ+gb0xQyRmB6qql4d\nnqxgS6jRvH20l/fL8PjVt5yk8PBGP26eHjCm3qh8QxF4zM/dlbQZX4Ktd2y949o3bJvtr7Lhqld0\nbOgk6KS2GgbbfLJeor4dUbBM0dWW0oxosapPj2D1FD11I0B2FfgjOQgaQN+MxIg2xQoBCfR9akDs\nsT2ZpYf0JeP6h7rzUHVDVYeM0vwi6nlopE3GWAm7o3nfOAzwKHLKkDIepw3jAcIIgo+kXhrP5Vhm\nFi0gXGH1CK6+eSNTAbnJjJhlu0KxgrD652pSWP0TIsZGjA0MxoJGK0C+zB4pmLqDT5t3GhFO0g5o\nA0GsEE+t9KM2u9+Y3rJ1OFot0zEf5WmR82o8DI4Gj7XOWYpNq9w/sgWm829A/h0DK+8dz2bFdFtj\nMDNaUzS2KRPrYsW5GhFG2UIad3WgT7EHQ3VEcEJARMYVKCI3z5ClezgxMuAqryowqmgP2PMy9m/Z\nAjVrwf4e0z9MhnL5m3NsaoKs9IX7rar/KhxMqSpdVnexCKaIO74cU4g7AlgA0QaeH/crSfwekcbc\nu/ocbcX2Ktiugm3rJaIPA4Wz6bqzf/ZfURRtjKl1VYGU93eNA6h41oiB/b4ouqfu6xrTAc2G1MUZ\nG0OX4LV+SmFAqk9UxwD7HT2jRW8t4ffRhlgs+K0pm/cxAKbjOnZZbEhHh9r9ttqenJl3xn/k9El1\n2wB+zArwYayQ5bUYtBNk65a9u2EscUcWXRcml9e+TrpGFPW5dpO++rJ43TMD4cUJSc1Z0QBtlA4p\nErUizwtwCbtPOTM3dHKc645n4WSntLoj4i+wFTiav+MwE/cDMUaB2Zb+eceTI/4MZ8n09v51SCA7\n9nhU/+gCNwP7DlU5HPmjg1vjShWLWceJNP7hg4+lPUeWzogQh65HZvm0zDobW1V99Q3o5n3qc8/7\nt+kLl9e7B/bHLQ5E5LRh+jzTgXC4OXeyJnfAtAgRst8K4IpNwMq25JErNAawwCP66r5s12AB7Mc2\nloODF+Z7iFZGjmrWktBQO1a8U4TKilZxoVS5cix445rVeCiKaidYI0Mj+FvbvE9bGHsB8uNZ49nr\nFveN+gkucRuAhaw+3wcGvnuGwV/7250Bf3N4Gig1guKe4uhPAfRjS5CvDb2RFU0hU5Poruw72Tzw\nzQpDBdhXAORGFmkoeF+LnDAKx5Q29ccARxpl9NvKAQoZQ8+xTZNL+zuF6RjA3uouxOeI6DvIF0un\n3LaObduwbZuD/Cs2uWLTDRs6OomtdUsWFVEvQtN9iaiu5kiJlL4O2c0m8VWecpWE8PZTIyC85Goj\nGepAHzQVaNcB8F0xB07UHwXov9F3f/A775XxzpBIuQFEFL8G0skzXKojkkiBMPZzOoyWLdLXHiMt\nzkdKqWTfDO9MGK6bWVCwWhhGGxSfpvN0GEAaXDGtMZYJvYAc6Mdo7SDEPPCNFmxY0WgB0QKOQl6s\nIJJRfAmxqKfzQpdiSVDZh3zuyJK/dkHkXPPDDLqvZehe38e8XUq5L7CMBoVmbZ5wglT5W/txfL8U\nXr5lDxw+WPZJJkZjYPFie0sDFgbWxliZsRD7SgWM5ArFMoUBfgMtsKk+N8BtQ1n6yFMqn0LHRvJI\nb3befVYEhAtjjNlhwwDhoBiBizp8o36KF7DjiKD6Pv+DQ4MxpqIrRkZK8En8mSNq30Vz1VzPsLYV\ndMWdCvK4sS9KmSnQoxDbppCror8KtteObRNzAHQd6c6zyFHXtLPDPDhG8FWKSp9IOxNI77Hbo8TI\nKSJLI8iikMXtRnfajaUdMbL/eLRTZFhEcO/WO2ErR4zpOw5QH+Sp2trKZZvsw8MN02eUY0H+7nfP\ni/4fMtcctpbKj1zyT8wP6vpaE/zLppCFyzKOUXjR+4UX8yV2k1vJ4+EMJUak9NtUSTzXdnrtEDGU\nF0s121x5F/WNLdLvYwQCA/riKtVRCDwbRHpaXCUjzh0/Ob1jwFh2pwJH+n543kpbZJ8GMAamX4vg\n193Xgol++sabo46TtFnrvR6m916HDvaEIedm/Or6KhygxUG6k50avCpuArdLY1pvbORLvg5Yt4f2\nQ+LWd6Xjvz/Dhi8E+vPgnBs3vDkhFCIaHefuH3CYYfNDHz2x3zsUoZZjcbz+Li85gJBF6wSbwCru\n+0C3zFPxJ3EAXfbkxqAZqQ7yPar/EC0US1RjpzAyIqLIQhnldXLgTZe7Tbqzv27lfjVcC0K6ceDc\n8eJS/b7XK/qnCvZbcVDAc1fU8qJXBa0EXBj8AeAPCn55sOh+nb+ye+nxx71WOxwjCkOOfbAr0siy\n0r4SBOwmMENIfEoIvFQ8QJtaBdWm4KsNbt40K+3autsEsnxzMGNXIRZQmwfp0cAhVIo28nSrXYGR\nFM7vp93SfTvHgub38VftTeYEGVH93jVTKjeP7Petozvo3/qGLh1dxbzOFKlmyGWToAJGrD/u86Fh\nvLJ7uuB8BcZqEsZHXgy4UwvT3p5/UxqLQ0ZUOrIUxcG+p5pC4EbEQyz9JaEKxm6/QX4bCqsm7cZA\ngBtUGGAfzcYFfOqKwPo2bKxwpLRj3PrxCvFAzTIaZm/Iz+qsDCBPdiyLSkVEvzoyZ65UMBtp+w3q\ngDdks/mWrP9bwb3mMXvegYSoxk8uXGZzY1+wB6WpaprxoHjr2RR4H82R5tkGoDxmhnHlzxF4PzKa\n3nBIUD133DbmmxIRloWwLMC6EhYvarU0wrpY8bPGjEa2Gagf8+6j+B6KYyRy09TWSM35reS667lR\nvv0o27febNjd+/3M58k+y3R9IFbHsP7nm8IdqRgGKnzMR3q/YtfXxoi6LRALhFgoTgSFp/DTuL5G\narZaVtADNLLFgL4ptiuwXRXbVdBfuwH9a/eCqy6r0jsR6pMQIN+KEuvobz6dg0AQn9+8m5GTTRH2\nkR8iZIaeNDKgmUDf7aSwL4RsHe5m+mpxZK+EMVOPYjf6Sq38QY/ao4V0CuaEk284GrzkdUm1HzIC\nuO2zc1+tuqfa7jWA5+eoIlZm2a3c0GlXHFE2hTA8Xd/7l8/Vl2mjBp8K4LY8er4aqVkGUdw4nvZh\ngbqZ4SjuAFK7mb2mkGW21vkcpQYLseWddURE3zpHBDQoQCOZFAsHAOVKOcZfC3BEVhPMuRG1i2Lk\n73QMShp/2Hr7tt2p7sIo1cAk85QYPxoG9UM0y8rPnDtnR+3+nDUq3fkcP/DzI5sUYZNjMMUFXuic\n4LtF9MeUjWjDGOPRgm91ub0qivdh4A058AVAv75wNSNmZtTBHmB//n1sR4ByFhRDOFB4SLKDFIOz\ndKDRAOWR/JoBnsQFha1nZADL1H0pzOPKIDzA6utFa0qXx9LMefF0nQqmd8ZNGEw3rufy3RGN7jrm\ngUS6yTwoqnDdF1es83PhA3ak95R2y0vWPlENPi49HDaBaFXQquAXBl8I7WeC5YOgXQSfPsu5z9Fs\nUB7RrXtpL8Jcy4JLlzSLRcTAYlQNJh3mrsKqugqs9gO6jfIsxhEDupmnvy2WPtYWu2caUVyWQvH5\nN0pkxVN0asb6R32R51n8qIUAdrf2fzOCH5smBkT3WgZRUGfrY/6kAX0D+327ondBF4G4gmMgi0ZF\nWQnLlrBCJ5uGR3vox0gWSd74g1G3zApqvlSZ26edyixkdcUWkX1xuaNkFdyruPlB6Ye+Qenfu3u5\nkt4ZbuOFTfHHI6pb2Zqiw1J0rWpwg9VQaIiN0TKp3Zf2DNl+tOzIV9PsmJzebTcWRm/VYtDeGA67\nzl5Bfl3DfTZ+yIN7zl+lYf9EEUKXpyJzn3LdUzR/rmdexj3FvxUHOzAbcvoxnlKaBXtH714KRB+q\n/AD2QuctOVzPubf369LIkoi5+LbUVMOysAN9tRT+hdG8IndW3CeMaY/k71CdlMmy4KHL6GZFfGOe\n5WP0pumG0UnvAaaj69SxOl3KhbF6dXsRBzA8+lXkkWSv1LoJRBkk4tdMS9RT/KspHynk47HtepMl\noTruLWKO0wdIRNE3xXUTbJun6r+q7zfbruL1YZBAX50X5vBBeV8DtAOIxFuxSwmJoTp4nWPbDxAy\nos+Zlg1LklFLEwfY2qXDgP4C6GLZKUoM5ZZLpDGV6C209BTnueanInse4ipGix2ZEfYEt6uazJ+B\nIRdGRuxti08CDpFCGTLUZbUCEBnLOTJs2UI2kB9TNtSj+LHKwlhlB16HS3NJ7czOTJR1B+A9qKIo\nfMukUAfW6iDbuMYgkmFTeR9C2E+wosRMVqcI5EuEd2DbQiYqXqlgqxxnAIRtWhi1XXQ/jSzPNNvp\n+uCAZ48OaeksySYLfbtnlfVJd1b7oBkYODIPnk23zohBerzP4PFnxs1hQbyy92mIxnP15JXBTyu0\nxwXkq+MCt0djyY1dtwvruuifSX+Vg2++wlcA/Xxk3DIzbhRm8wwsq2G500TTZ5Rr17fWIUvzaOl0\nufe5SpUJafCrFelgQNsA7xmBopjpWBRWLAEjFFJlSJkHiBvbPGJf7mJ0Oi6drmZEzDT4st/s2PyL\n23RW4Jb33X870iPj7Awc7+5ZnRAhzKuTYhhpYDKQ/0GB7zr4wmgvhHYRLN8p1u8E66Xj777Nts9Q\nFU9HPCuDrhyZsXHR3tbuSj6tV60/dM2CbtZKZMtkUXy2asYZAd58ELNa+qdHh0QJS7b1UKfMGOmj\nrsAN9MM9pXQT0d9hmP3lnkJRUR8lwh+3qVvEAPZg3wveSQX5UkC+b11zjmfaleQ1Dxy8eJLEDczK\nuVEemJ3rdlJXK7DX3HAqzRtBnigqmQFeQVnejNIA/EH00y3Hf+DrBxer3KDSbaLvhRtrPFeuOuJp\nD+QgH+KKTDVBvsW8A+RHCvUkZ59CM9A/uLKigGCFL9yImwrzlfc5PkO+1Y3rSYU49Uo8x3Bm+L0j\nehhGpp3l86nDIOaSCog8J5T+zMFYXu12Xt/Xk9UesAGlmbkV/FniaXGcso/pM6bP+z63P79uftxT\n7IkMOFkkn7AubGA/KlUH0PcldKPqOUeVfQA5NSIU2uR8SSeXA332zAB+ekR/fvfKl5oNGccrj+pv\n6u9uD2mUpOhqNg+RVSYnwJbWHeZRHRkKd656dFE9GyDAEGW/PI7bjeuOKGFoDft+BF4eIeljStj1\nKjYv/9Xn57929GuJ6G86AjfqY837SK5GIGWta7WnDhd86Jgdp71JNE0d76/Z95ArPEl3fobc7QBW\npI1prHX7QHQnnm28j2mSI6zl0b+Yu40Yl49Q2PDVqkZp4fr6o4L43o4MsG6yoVop8a+mjVsj+YIx\nfSWm1ghU++iHHYCvgBA2gk0/AWQTSGcD+4KsyzAAvgdrNGyGkLNup6q/qYbMftKYj+LLCRZd7jTn\nLonXgHIbK6McksEjgS0xHDJx8+J+fQO2cGamNrGOOhzJkiCffS+siJWebJmioNKLjmTeblxHMGoE\ndmfrkFzO5M8VPyDQr/eeHvrmc5GrRz+5d+mja+n4O6P6ISzyNj51IkB+i1pUAHYgfyCwPff3YJ/2\nX+E+7jH6AqAfA7aqA9zsDVwXEzzzxt3a3hmU1aiq3+VT53Vod15EYsa9Z2bkM+W9MRooX4E8JZ9N\nWDBBmXMTYgdvHqnNu9ZneT+RN7J61cvhVfKHVQztgeJRz/Fz26CHTXwYxY9jFejfMXRTudxcuFx7\nvnMx1ohtfvRKoBcBvlPgVxT8IlguhPXCWF/UlgxZHtT6aVgf9aWjUazTN7NhGlrc+7WMv3OaHymY\nBb0TOjM6CbYosmIqBPDicmBv98bgmFqb/dvallyRaaTvO+CPKHMVkreCkoaz0fv747K08LOA/CLX\nPNAzDCEr8uQAXwPk27Z1r3C/ac6llKtAXsU+b6acw1+oGCDeohvDhCiPkI4FXw1oL0FMj0K6gpv5\n6iLSF1MmY8jFBKSdAeeeWXU888MB/beU049F4Xi0Ssu6kxGTgkv5ahkPxh71TXyONGOhEdkfYJ8Q\ny9E8Z+rzvO576CJgP5aBsWJIh+r14LcovwPGNCQH+MOqH3u2zDADh74WMmikjQdwZFudI6KJ+55q\nnY3Iq0Zi3lwflulQGeHzTili8uhx+7RG6Y/0/jDix1b5Nt7ndrTOhtLkGA6Zmz8zuTaWKiOsq6Xo\nL4vNgeamWeTIlqfyPdvnKIYUc9UzbZJiM5ltt49+aYX+6nJ8j9GRvqz6qdpL935/7xnqtR2wiH/s\n8PnNPm1E4Zpp2Ba1GkfxKCNs1nisI8svM4Tz+/iDjl/FL/oMp6n0zcCdp+rLq5oueRXItfsmkM2c\nzCJAIEQzVxUgK/omnk0W0wqsAKwDTbBDWndreJ/Jri7ZkSxgxAT1SHMTpFMjsplYfepihylKHdNK\nGrmO8roHFqQNHawHDYCJz4/qjZibNtv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fXaA+RiiXxh3t4EPAx044yuDBqDiWtZhkg8jV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\n4zX+F43/A9kwnD4c7ApzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6de50d7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_indices = y_pred[:,1].argsort()\n",
    "\n",
    "N_samples_to_display = 12\n",
    "\n",
    "for i in range(N_samples_to_display,2*N_samples_to_display):\n",
    "    plt.subplot(2,N_samples_to_display,i+1)\n",
    "    example_neg = test_X[pos_indices[i],:,:,:]\n",
    "    example_neg = np.swapaxes(example_neg,0,2)\n",
    "    plt.imshow(example_neg)\n",
    "    plt.title('%.3f' % (y_pred[pos_indices[i],1]))\n",
    "    plt.tick_params(axis='both', which='both', bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off')    \n",
    "\n",
    "plt.gcf().set_size_inches(1.5*N_samples_to_display,3) \n",
    "\n",
    "plt.savefig('figs/plasmodium-testpatches-lowprob.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Example of objects detected in an entire image\n",
    "\n",
    "The white boxes represent annotations in the training data. Red boxes are detections by the convnet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reload(det)\n",
    "\n",
    "fname = testfiles[5]\n",
    "imfile = opts['img_dir'] + fname\n",
    "opts['detection_probability_threshold'] = 0.5\n",
    "opts['detection_overlap_threshold'] = 0.1\n",
    "found = det.detect(imfile, cnn, opts)\n",
    "\n",
    "im = misc.imread(imfile)\n",
    "\n",
    "plt.box(False)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "\n",
    "annofile = opts['annotation_dir'] + fname[:-3] + 'xml'\n",
    "bboxes = readdata.get_bounding_boxes_for_single_image(annofile)\n",
    "for bb in bboxes:\n",
    "    bb = bb.astype(int)\n",
    "    cv2.rectangle(im, (bb[0],bb[2]), (bb[1],bb[3]), (255,255,255), 2)  \n",
    "\n",
    "for f in found:\n",
    "    f = f.astype(int)\n",
    "    cv2.rectangle(im, (f[0],f[1]), (f[2],f[3]), (255,0,0), 2)\n",
    "\n",
    "plt.gcf().set_size_inches(10,10)\n",
    "plt.title('Detected objects in %s' % (imfile))\n",
    "plt.imshow(im)\n",
    "\n",
    "#cv2.imwrite('detectionimages/detected-' + os.path.basename(imfile),im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Evaluation: compare with classification based on morphological feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*****************83%************      ]  575701 of 692165 complete"
     ]
    }
   ],
   "source": [
    "featureset = [3,7,11,12,15,17]\n",
    "centiles = [0,25,50,75,100]\n",
    "\n",
    "pb = ProgressBar(train_X.shape[0])\n",
    "train_X_f = []\n",
    "for i in range(train_X.shape[0]):\n",
    "    if i % 100 == 0:\n",
    "        pb.step(i)\n",
    "    graypatch = cv2.cvtColor(np.swapaxes(train_X[i,:,:,:],0,2).astype('uint8'), cv2.COLOR_BGR2GRAY)\n",
    "    train_X_f.append(shapefeatures.extract(graypatch,attributes=featureset,centiles=centiles, momentfeatures=True))\n",
    "train_X_f = np.vstack(train_X_f)\n",
    "\n",
    "test_X_f = []\n",
    "for i in range(test_X.shape[0]):\n",
    "    if i % 100 == 0:\n",
    "        pb.step(i)\n",
    "    graypatch = cv2.cvtColor(np.swapaxes(test_X[i,:,:,:],0,2).astype('uint8'), cv2.COLOR_BGR2GRAY)\n",
    "    test_X_f.append(shapefeatures.extract(graypatch,attributes=featureset,centiles=centiles, momentfeatures=True))\n",
    "test_X_f = np.vstack(test_X_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "clf = ensemble.ExtraTreesClassifier(n_estimators=100, max_depth=5, n_jobs=-1)\n",
    "clf.fit(train_X_f, train_y)\n",
    "y_pred_CLF = clf.predict_proba(test_X_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "false_positive_rate_CNN, true_positive_rate_CNN, thresholds_CNN = metrics.roc_curve(test_y, y_pred[:,1])\n",
    "roc_auc_CNN = metrics.auc(false_positive_rate_CNN, true_positive_rate_CNN)\n",
    "\n",
    "precision_CNN, recall_CNN, thresholds_CNN = metrics.precision_recall_curve(test_y, y_pred[:,1])\n",
    "average_precision_CNN = metrics.average_precision_score(test_y, y_pred[:, 1])\n",
    "\n",
    "false_positive_rate_CLF, true_positive_rate_CLF, thresholds_CLF = metrics.roc_curve(test_y, y_pred_CLF[:,1])\n",
    "roc_auc_CLF = metrics.auc(false_positive_rate_CLF, true_positive_rate_CLF)\n",
    "\n",
    "precision_CLF, recall_CLF, thresholds_CLF = metrics.precision_recall_curve(test_y, y_pred_CLF[:,1])\n",
    "average_precision_CLF = metrics.average_precision_score(test_y, y_pred_CLF[:, 1])\n",
    "\n",
    "subplot(211)\n",
    "plt.title('ROC' )\n",
    "plt.plot(false_positive_rate_CNN, true_positive_rate_CNN, 'b', label='CNN: AUC=%.2f' % (roc_auc_CNN))\n",
    "plt.plot(false_positive_rate_CLF, true_positive_rate_CLF, 'k--', label='ERT: AUC=%.2f' % (roc_auc_CLF))\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylim([-.05, 1.05])\n",
    "plt.xlim([-.05, 1.0])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "\n",
    "subplot(212)\n",
    "plt.plot(recall_CNN, precision_CNN, label='CNN: AP=%.2f' % (average_precision_CNN))\n",
    "plt.plot(recall_CLF, precision_CLF,'k--', label='ERT: AP=%.2f' % (average_precision_CLF))\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('Precision-Recall')\n",
    "plt.legend(loc=\"lower left\")\n",
    "\n",
    "plt.gcf().set_size_inches(4,10)\n",
    "\n",
    "plt.savefig('figs/plasmodium-patchevaluation.png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = {\n",
    "\"false_positive_rate_CNN\": false_positive_rate_CNN,\n",
    "\"true_positive_rate_CNN\": true_positive_rate_CNN,\n",
    "\"false_positive_rate_CLF\": false_positive_rate_CLF,\n",
    "\"true_positive_rate_CLF\": true_positive_rate_CLF,\n",
    "\"roc_auc_CNN\": roc_auc_CNN,\n",
    "\"roc_auc_CLF\": roc_auc_CLF,\n",
    "\"recall_CNN\": recall_CNN,\n",
    "\"precision_CNN\": precision_CNN,\n",
    "\"average_precision_CNN\": average_precision_CNN,\n",
    "\"recall_CLF\": recall_CLF,\n",
    "\"precision_CLF\": precision_CLF,\n",
    "\"average_precision_CLF\": average_precision_CLF,\n",
    "\"opts\": opts\n",
    "}\n",
    "import pickle\n",
    "pickle.dump(results,open('plasmodium-results.pkl','w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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