Mnist_MLP_Multiple.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.autograd import Variable\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Args:\n",
    "    pass\n",
    "    \n",
    "args = Args()\n",
    "args.batch_size = 200\n",
    "args.cuda = True\n",
    "args.lr = 0.001\n",
    "args.momentum = 0.01\n",
    "args.epochs = 10\n",
    "args.log_interval = 10\n",
    "\n",
    "basedir = \"Mnist_MLP_Multiple_models\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=True, download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.1307,), (0.3081,))\n",
    "                   ])),\n",
    "    batch_size=args.batch_size, shuffle=True, **kwargs)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    datasets.MNIST('../data', train=False, \n",
    "                    transform=transforms.Compose([\n",
    "                        transforms.ToTensor(),\n",
    "                        transforms.Normalize((0.1307,), (0.3081,))\n",
    "                    ])),\n",
    "    batch_size=args.batch_size, shuffle=True, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdgAAAHVCAYAAABSR+pHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VVXWxt+VThok9BRI6KAialAYRbArY8GZAbtYRpgR\n7DO2mU8ddexlrIw4Kuo4KnZQFAFFUWpAWuhgKCH0AAGSkLK/P+7NXifJObefW9fvefLkzbl7n72z\nsnPPPeusvRYppSAIgiAIQmCJC/UEBEEQBCEakQusIAiCINiAXGAFQRAEwQbkAisIgiAINiAXWEEQ\nBEGwAbnACoIgCIINBPUCS0TnE9FaItpARPd62beUiFYQ0VIiKnbR7k0i2kVEKw3HsoloBhGtd37P\n8qLvQ0RU5hx3KRENN+mXT0TfE9EqIiohots8GddFP7djWszfdvs62/pk42Db103foNo4nO3rbBcz\na9hX+7roK2u4ZduYXcNNUEoF5QtAPICNALoBSAKwDEA/L/qXAmjnQbvTAZwIYKXh2FMA7nXqewE8\n6UXfhwD8xc2YnQGc6NQZANYB6OduXBf93I4ZKvv6Y+Ng2zecbBzO9o21NeyrfWUNyxr29iuYd7An\nA9iglNqklDoK4AMAlwR6EKXUjwD2NTt8CYC3nfptACO86OvJmOVKqSVOXQlgNYBcd+O66OcLQbEv\n4LuNg21fN319IazXsK/2dfaNmTUcSe8Rbvr6QljbOArWcBOCeYHNBbDV8PM2ePdLKADfEtFiIhrj\n5dgdlVLlTr0DQEcv+48nouVO94Wp66gRIioAcAKABd6M26yfV2M6CaV9Af9sbLt9Tfp6Na4Tf2wc\nEfYFYnYNh/17hElfr8Z1ImvY3jXchEgKcjpNKXUigAsAjCOi0305iXL4AbzJDzkBQHcAAwCUA3jW\nqiERpQP4BMDtSqmDno5r0s/jMQNIQOwLeG1j2+1r0TfYNg57+wKyhoHwfI+w6Ctr2IRwWsPBvMCW\nAcg3/JznPOYRSqky5/ddAD6Dw9XhKTuJqDMAOL/v8mLcnUqpeqVUA4DXrcYlokQ4/jjvKaU+9XRc\ns36ejtmMUNoX8NHGdtvXqm+wbRzu9nWeN5bXcNi+R1j1lTXckjBYw00I5gV2EYCeRFRIREkALgcw\nxZOORJRGRBmNGsC5AFa67tWEKQBGO/VoAF942rHxD+PkUrNxiYgAvAFgtVLqOU/HternyZgmhNK+\ngI82ttO+rvoG08bhbl9nu1hfw2H5HuGqr6zhFu3CYQ03RfkRIeXtF4DhcERobQTwNy/6dYMj2m0Z\ngBJXfQG8D8ftfC0czxduBNAWwCwA6wHMBJDtRd93AawAsNz5h+ps0u80ONwOywEsdX4Ndzeui35u\nxwyVff2xcbDtGy42Dnf7xtoa9tW+soZlDXu6Hhu/yDmAIAiCIAgBJJKCnARBEAQhYpALrCAIgiDY\ngFxgBUEQBMEG5AIrCIIgCDYQ9AusjxlA/OobK2P62z9W7BQq+/rTN1bG9Ld/rNhJ1nD4jtkEb8OO\n/f0CUBzsvrEyZiTOOdLGjMQ5R9KYkTjnSBszEuccSWMav/y6gyU/SksJgiAIQjTj8z5YIoqHY7Py\nOXBsCF4E4Aql1CqrPkmUrOKRgEQk+zRmLWp86utrv3AcsxqHcVTVkNlrSZSsUpAWdnOOtDErUbFH\nKdXe7DVZw/73lTVs75iu7AvIGg5EX3c2biTBp5Ed6LJHAEBEjWWPLC+wKUjDKXSWH0MKC9Qsy9fE\nvoFhpvp4s9VrYmP/kTVsL67sC4iNA4E7Gzfij4vYo7JHRDSGiIqJqLgWNX4MJ5gh9rUfsbG9iH3t\nR2wcGmyPIlZKTVRKFSmliny9VResEfvaj9jYXsS+9iM2Dg3+XGD9Ki0lCIIgCNGMPxdYn0tLCYIg\nCEK043OQk1KqjojGA5gOIB7Am0qpkoDNzGaOXHqK1nNeec20zXk5A4I1HUEQBCHK8CeKGEqpaQCm\nBWgugiAIghA1SC5iQRAEQbABv+5gI5nCu1eHegqCIAhCFCN3sIIgCIJgA3KBFQRBEAQbiFkX8Ttd\nfwz1FAQhqMQd31frxz+fpPVjZcO1PnQJ5yav37svKPMSBACovvBkrX+YOFHrbt/eqHXP6xYHdU7+\n4tcFlohKAVQCqAdQp5QqCsSkBEEQBCHSCcQd7BlKqT0BOI8gCIIgRA1R7yI2JpTwJHJ4yLixWqdi\ngS1zijXiUlO1rrzgOK3LzmF35KhTFmp9ceslWl/97Z+0zljHy/VwfgMAoCG1ngdSXD2qYxd2b47q\nwuebObRA61hzgZYPzdJ6QDLno53cjSuDDLzkz1pnvzkvOBMTBAD7e/D/d71q0Pruk7/R+jOYVpEM\nW/wNclIAviWixUQ0xqyBVHGwF7Gv/YiN7UXsaz9i49Dg7wX2NKXUiQAuADCOiE5v3kCqONiL2Nd+\nxMb2Iva1H7FxaPA3VWKZ8/suIvoMjiLsYRWeu/10dhvOsYgc7v4huyF7fDbf9jlFK/E9CrWu7dxG\n61/HsRt3xZCXTfvGGT7rNYDdQ2suesXlmFb9jKw8yq7oWUm9XJ4vmkk6b3eopxCTXLuWy2Y//uZl\nWuc+OTcU0wlbVLzvfQ9cNUjr1u+Fz3u4z3ewRJRGRBmNGsC5AFYGamKCIAiCEMn4cwfbEcBnRNR4\nnv8ppb5x3SU4GAObNl72b7ftc35UbtsI5hhtfcuTH2h9SZp3geUHGqq1fmDHWVo/nzPHtH2fKeMA\nAO0X8MfeTtf/atq2+r6OWlP5Mq/mFenEt2ur9Z09Zpq2+aCSg5/aLazQ2twfEH3suOM3WqeX8W+d\nPtn3O6G6s07SelQ67918/JQDPp8z2un3uzVeta87k218sIDvFVsHbEb+40+5uk0Ajg/gXARBEAQh\napBUiYIgCIJgA1G5D9aqgLoRCWzynf3XDNb6zUee0/q9CnYXLz7MAU8fz+YABOO+1VEnL9J68jxO\nk9bnHt6vfFH/m0zn0HvhUgCAqj2qj9VM4tcT8vO0pq2x5RY2svmm3lpfnjHLtM2kKzlVolpZYvuc\nwoE9Y3gN/3wXr+Hf/vnWgJx/x8kcqRsHDrQ8si09IOePFuLbsEN3UJtNpm2eXHC+1r3A7vZDuUla\nq0QbJhcA5A5WEARBEGxALrCCIAiCYANR4yI2RrMCS92273GHuIW9ofZsjti76K/fa90jkZfQJ1+d\nqnXHYnYF9/jc3NYrDO6hvlirdX1lpdb0s/nf0l3cd93WbW5axAaF55lHVh9pYNd63Hrep1lv1jhK\niM/M1PqqW6drvbWOI4fT1u7V2h9bHH8hP+aYXc3+yz6vBOb80QKls8s8I67atE3fx/drXU/sbt/b\nn9skHg783AKB3MEKgiAIgg24vcAS0ZtEtIuIVhqOZRPRDCJa7/ye5eocgiAIghBreOIingTgZQDv\nGI7dC2CWUuoJIrrX+fM9gZ+e53gdOYzguIg3PM8RtEa3tPG4MdFF6mfhU8HHmPqw4yPrtT47nSNN\njakKc2ez2zFxpvvCyPX7ZdO9HVRcxxGy3/V43vBKilbHTB2vda+DCxELrL//GK2nZs3WuvfscVp3\nX/eLz+evPZfLYU/syilBJ+zncevXbvD5/NHIulu7aH1t5hStH9/LNlPlu7SOz8jQevWVbOP+r99i\n1xT9wu0drFLqRwDN63pdAuBtp34bwIgAz0sQBEEQIhpfg5w6KqXKnXoHHGkTTXGWsRsDAClItWom\n+IjY137ExvYi9rUfsXFo8DuKWCmliMgyqFMpNRHARADIpOyAJv01ulmtIoev3cwV9OyOHDZGMrPL\n2jCvy4ytzY93P93gxvZgvnba98AJHbT+rMtk0zb37uDfudV6duXUBXIiIcZOGweKhNwcrW+7j/9W\n6XHsFn5yb0+t+97PLv9QR7MGy751HWq1LjEkKOn+UmAsUNeKHYKtKMlFy+ATrmu44CTzaP+Vlbye\nGyrZgXr498bdIt8j3PE1ingnEXUGAOf3XW7aC4IgCEJM4esFdgqA0U49GsAXgZmOIAiCIEQHbl3E\nRPQ+gGEA2hHRNgAPAngCwGQiuhHAZgCj7JykFZ6Uovt5fj+t7YgcNrqFC+9e7aKlZxh/p/PuGOD3\n+XwhoZPjkfqDj7/ptu1jnTjq+e1vumo9bddxWq8s66x1hy/YZZm5gRNKqMWxkQM3oMRxqb6NY9n2\nV2VMM23+7wVDte5VUWzfvMKIuBReb8+cxq7zET/erHXP+UsCMta2s8j0eGocu6MTCjhqtq50S0DG\njWRG5rjfbWCkopcfVdlDgNsLrFLqCouXzrI4LgiCIAgxj2RyEgRBEAQbiMhcxOyWDV7OYaMrePvp\n7Apy56Y2JrewwhNXdzBR9Y7crDvqWhuOHnHb7/pMzmk7OnOz1i+27aP1rUPXaF1cw+6eq6eznXr9\nOTYSH/hLQod2Wq/54wTTNpMOciR43+cOah3qyOFgUXMaJywYkTZX67/uD0x9M0rmsnRFJ5onkfhT\na/5f2PMFJ0qYe3x4RRqHExsmcZnFtpgXwpn4h9zBCoIgCIINyAVWEARBEGwgIl3E7qJ1A5Vz2JjI\nwsqNa0xksXPwwRavW43fpLyeMdFECPIlN6d+924AwAdDTtDHPkzl/LYbxuRpnc7eL+w7kR2PedPZ\njZ4xe53WU844W+t3nn9W65KLOK/oDf3P17ri1OZZOoVGNl/X3W2bR7/8ndbdV8VeiUbiSnSoMzjG\n/zhsttY/F7A70iqyNy6Vsx9VDucI+bobuPzcV4UcpWwc6z8Humm9YJgx6V2F68lHKdUXnaz1sFTO\nlf1jNZcTbPu6uVs4/xmOfu+Xxfm0E8M0s42v1XQeIqIyIlrq/Bpu7zQFQRAEIbLwxEU8CcD5Jsef\nV0oNcH6Zb7wTBEEQhBjFk32wPxJRgf1T8Zx3uv7o8nV/IoeNrlsrt3ATN64XY3mSlMJYui7UNLqK\nm1Pwt62mx9uZHm0asZr6KSemuPHgbVp/984bWr9bMEPrU69jN1DWpMiNJgwU6lROPvLRn54xvMIu\nzIp6jvju/QLneg1TL5qtJHzHiQyuLz1X63cLZrH+hl21//jhEq2zlvLb4zljeO091sE8YtvI0GWc\nPqD1cGN0cWy6hY1su4xzQhcmcCKQEYvYZvlYCTOUIYd0t7v5bxLfhnc8hFOEvD9BTuOJaLnThWxZ\ncJ2IxhBRMREV16LGj+EEM8S+9iM2thexr/2IjUODrxfYCQC6AxgAoBzAs1YNlVITlVJFSqmiRCRb\nNRN8ROxrP2JjexH72o/YODT4FEWslNrZqInodQBfBmxGIcCTaGFf3cJGjG5ho5u7ybk/i51IT2N5\nu1rFjp0GcOhnp+t/1bpmUlCmFdZsOZddwX2TzOt6nvnkX7TusHWuaZtYZPc9nK/5rhc4kvXZTpzY\n5JoLX9M6/iK+/7ir/ESt+74zTutBwziH9ltdZmu9v7i91q1hnoAiVjmlsNT0eO2GDNPjnlC//4DP\nfe3EpzvYxlJ1Ti4FLBzmgiAIghCj+FpNZxgRDQCgAJQCGGvjHAVBEAQh4vC1ms4bJsfCBmO0bupn\nC1y0dGDlFh4yjj83eOu6bZzDnFdeM33dmKAiUPmSI41dr7Ry2+bvXaZq/eCx12jdsHKNWfOoJCE3\nR+v7L5ts2mZxDUdXdprNyTkazBrHKHE/ce7ykts5iUq/8VzSsq6M3e4Zpezg6/Qqu5EL6zh6dd9s\nozOP6bQgFmO2rdkzhhPVfNKVk0v8cpTzkfeaEH0R75IqURAEQRBsQC6wgiAIgmADEZmLuDHq1sq1\na3TLXns3u2J/faqv1p64jo1Rvz+fPshFSwenDlql9TtdW7qGAxGJHE2MKljsto2xold9a96UTiZt\no5X1z3BE6rWZe7SuV+wAHvnVLVr3XOl+bcc6cXN+0bpgjvv2xvQvDYYc3R/04KQTt2wfpnXy10v8\nmV7UkX0Zu3+TiUsF3rF2pNbpmzcFdU7BQO5gBUEQBMEG5AIrCIIgCDYQkS7iRvfqtYPY/WuVn7jJ\n8VeM2v04Tfq6yX/cnEZ3sNEVHKryc+HKwFa/um3TZwpv6u+9kKNAwydjsz3EpaVp/UzRR6Zt1tVW\na91zvLiFg8X+HvyoohXxM4yvlx+rda+GYsQ8cRwhnJZw1LRJ+doOWveEexfxlo+4VGD+v/j89PNS\ns+Yhx5NydflE9D0RrSKiEiK6zXk8m4hmENF653fLfMSCIAiCEGt44iKuA3CXUqofgEEAxhFRPwD3\nApillOoJYJbzZ0EQBEEQ4FmiiXI4EvpDKVVJRKsB5AK4BI4MTwDwNoDZAO6xZZYW7Bx8UOvuz3OE\nrlV0sR0YE0YYo5SjLadw/TDOxZq4n12TDUtXmTVvAp10DP/w9H4tT03hKOJEYnfPo3u4fa8/8wb/\naHcLG1n7Sh+tL0772bRNj0RO2r7ujSKtc6cZXHOfiOs4WKRuSHLfKIZI6NxR6496TDVtk/uDd//V\nZxWs03rUu7y273rkz1pnvxU+ZS29egbrrAt7AoAFADo6L74AsANAR4s+YwCMAYAUmCcnF3xH7Gs/\nYmN7Efvaj9g4NHh8gSWidACfALhdKXWQiHciKqUUEZl+FFFKTQQwEQAyKdu2mxBjMNF5d3BRamPa\nxO2n+7570lgIvekeWr6LTkXw7xaCZd/0f5Rp/buOfOf55h2Xap389SKt4/v21HrLOVwMeXHvSVob\n0/h9e4QDR96deobWBQj9p9Fg2djI60MmuW1jvOtfet5LWv/+3XFmzcOWUNjXH/YMMk/k13Z1+Cb4\nC4WNV9/dxfT4+5V8L5YxhwObvC2UPjiZexzqyu/t2V6ex0482qZDRIlwXFzfU0p96jy8s7GqjvP7\nLqv+giAIghBreBJFTHAk91+tlHrO8NIUAKOdejSALwI/PUEQBEGITDxxEZ8K4BoAK4iocbPR/QCe\nADCZiG4EsBnAKHum6B9Gd26Pz0I4kQhn41fdtb7sti9Z/+dVra/YOFzrP+V8rvXQVkfcnv+WD/6o\ndcHfQ+8WDgU1wwdqfWKSsVC6+TOzi9efr/WBp9kdlzJ7oVlzIUC0m29427yQ5aFO7LJ3Xycq+rnh\njNmmxx9efJHW3Xf/YtomWvAkivgnWKd+PSuw0xEEQRCE6EBSJQqCIAiCDURkqkQh+OS/wQXOzzrz\nMq1nHfeh1h92/0brBi9LfWetDvvgUdtJnbdB67W1vMc1sY7TzF3z5u1ad3mC0/Gl1O6weXaCOypO\n4iji9i7axQpfPzJU63v+VaJ1x8993y9c8ndOldj7t7xbJHeZtzHIwUHuYAVBEATBBuQCKwiCIAg2\nIC5iwSPq9+7TOvmpQq37jOKkBn84mRNNLK3I03rD+s5a9+q5nU/6N94S3np+dKWW9IX6igqtH+x2\nkmmbfHB0sTjVQ0Pr0hqtDzZw2tBhx/FjlO0Q0j/iHRwXfsTrOd2PhDxJ0/mxSM/pPp8maPhTTech\nIiojoqXOr+HuziUIgiAIsYInd7CN1XSWEFEGgMVENMP52vNKqWfsm54gCIIgRCb+VNPxmjrUYq76\nBgoKuShEAfVx38nJT2oa4pEAAoEQh1PIfAtuiSrGHpQjCckYTOcCAGrVUazAfFThCFohFcdhEBKp\nZSSbWd+NqgTb8SsS4Yjq7IFj0Y46N+lXrY6gBItwFNUACLkoRBfq6XZcq36ejGlGMOwLAGtnvcZ2\n+o5tPMnid+2FrQAc9v2hiX232WpfV32DbWNv7OvrGvZ1/bqyUzSuYb/fI76bqvsWfXwnKr6ejkPz\nFyAh2eGz7DB0OHo36ydrOHbWsBF/qumcCmA8EV0LoBiOu9wKkz66igOBMBjnIQWpWIhZaKdykE6Z\nHo9/EoYiiZJdtslBV+SjO0rAzwNLsQbZ6IAC6oNStQalWIOe6O9RXwDogp7oSs3/ZQy/Iwg90R+Z\nlIU6VYuFmIVs1RHlKHU5rlU/T8bU5wiyfQHfbRxs+7rq68m4+hwBsnG42rfx94qVNWzHe0Tm0NPR\nqd+ZhiNzm7wuazi617AVHkcRN6+mA2ACgO4ABsBxh/usWT+l1ESlVBGAW+IQj1RKRxzFoSPysduG\nUIAsao9ENP1UtBvb0RldAQCd0dVyXLO+npBMrZBJWQCABEpEKjJQgyq341r184Zg2xfw3cbBtq+r\nvt4QKWvYV/sCsbWGI+k9wlVfb4gUG0fqGrbCoztYs2o6SqmdhtdfB/ClRfdGcuMM1/MUtMIB7HPR\nvCW/YA6ggFx0Qx5187jfUdQgmRzZQZOQgqOocdOjKVuxEeVqCzKQhV7ob+o6aqRKHUYl9qM1sr0a\n19hvP/Z4NaaTkNkX8M/GwbBv876hsHEk2BeIzTXsq3173DEfG9U2lGMztk6Zo39XyBpuQYys4Sa4\nvcBaVdMhos6GguuXAljp1cheUoQzkEKtcFRVYwnmIE1lIIu8z5dCRF7tb8hDd3RDPwDARpRgHZbj\nGBSZtq1TdViOeeiNAUigxCbjuBq3eb885fmYgSJQ9gW8s3Ew7GvWN9g2jgT7ArKGgfB8jzDrK2vY\nnHBaw564iBur6ZzZbEvOU0S0goiWAzgDwB1uzlNmTJ9XjSoke1FzIqXxEwiloD1ycNCLT11JSEaN\nctzy16gqJMH9M4RGkikFRAQix8Nvq3EbVAOWYx46oQs6UK7H45r183TMZoTMvoDvNrbbvlZ9g23j\ncLcvENtrOJzfI6z6yhpuSRis4Sa4vcAqpX5SSpFSqr9SaoDza5pS6hql1HHO4xcb7matWNSABlSp\nw2hQDdiJrWgPzyKy6lUd6lSt1vuwE2lo7VFfAGiPHJRjMwCgHJvRHjke9238wwDALpQhHS2DAZRS\nWIVipCEDXamXx+Na9fNkTBNCZl/AdxvbaV9XfYNp43C3LyBrOFzfI1z1lTXclDBZw00gpYKXDyaV\nMhQBUFDIQQEKqa9H/Y6oQ1gOR41QBYVOyLfsu0ItQAV2oxY1SEIKuqEf2iMHKzAf1ahyGYJv1rcC\nu1GJ/SAQUpCKvjhR+/Mb2a/2oBizkW5YcD1wLDKR7XJcq347sNVyzAVqFg6qfablA4NhXys7eWLj\nYNvXVV9XNp6pPl7sDAhpgS82Dnf7urJTNK7hSHqPcNXXysau7AvIGrZ7DRsJ6gU2k7KVq71Tgntc\n/WHFvoHB1QVWbOw/sobtxd2bv9jYf8LyAktEuwEcBrDHx1O087Gvr/3CccyuSinTyAKnfTf7MXY0\n2cmffu5sLGvYv76yhu0d09K+gKzhAPV1aWONUiqoXwCKg903VsaMxDlH2piROOdIGjMS5xxpY0bi\nnCNpTOOXlKsTBEEQBBuQC6wgCIIg2EAoLrCrQ9A3Vsb0t3+s2ClU9vWnb6yM6W//WLGTrOHwHVPj\n1wWWiM4norVEtIGI7vWwm2dx94HtGytj+ts/VuwUKvv60zdWxvS3f6zYSdZw+I6p8fkCS0TxAF4B\ncAGAfgCuIKJ+/k5IEARBEKIBn7fpENFgAA8ppc5z/nwfACilHrfqk0TJKh4Jur6et9Sixqe+vvYL\nxzGrcRhHVY3p/qskSlYpSAu7OUfamJWo2KMsQvBlDfvfV9awvWO6si8gazgQfd3ZuBGv6sE2Ixdw\nVtJ2sA3AKc0bGesQpiAVp9FwP4YUFqhZTX5ubl/ZQO4/M9XHm40/yxoOLLKG7aW5fQFZw4HGzMZm\n2B7kpJx1CJVSRb5+khCsEfvaj9jYXsS+9iM2Dg3+XGDLAOQbfs5zHhMEQRCEmMefC+wiAD2JqJCI\nkgBcDmBKYKYlCIIgCJGNz89glVJ1RDQewHQA8QDeVEqVBGxmgiAIQsyQkJ+n9fDpy7Qe14ZDfQb+\n7c9aZ781LzgT8wN/gpyglJoGYFqA5iIIgiAIUYOkShQEQRAEG/DrDlYQBEEQfCWhcyeti778VWuj\nW/hIw1GtD+fx1tNsm+cWCOQOVhAEQRBsQC6wgiAIgmAD4iJ2RVy8ljs+7aX1spPf13ppTY3WYx+8\nXes274R/hJsgCEIoKb2+m9Zftf/GtM2Qh2/TOn/iXNvnFEjkDlYQBEEQbMCvO1giKgVQCaAeQJ1S\nqigQkxIEQRCESCcQLuIzlFJ7AnCesCC+J7ssNj/RSuvlA9/Vut5QgOi4pEStr76HtwR/+U6WTTMU\nBN+4as02rV96aqTW2W/GxuOM7Xf/RuuEw3y8Np31mSMXaf3DuwO17jSvkhstXGHL/GKGk4/T8p2b\n/mV4IUmrn6sbtO743Q6t622dWOARF7EgCIIg2IC/F1gF4FsiWuwsh9QCIhpDRMVEVFyLGrMmgh+I\nfe1HbGwvYl/7ERuHBn9dxKcppcqIqAOAGUS0Rin1o7GBUmoigIkAkEnZvlV3DyIbH2V/0apBk7zq\ne23rNVpP/c2fAAA0d5lV84AQafaNRKLFxg2Gz9N7TqvVOvvNUMyGscO+CQVdAADrH+dHNSWnv6R1\nHNzWygbuXqDliYuu0rq2mF3NBa+t17p+926t43sUal3xEts984KN7se1gXBaw6UX8XvsSclJpm3G\n/Ge81nkbIity2Ihfd7BKqTLn910APgNwciAmJQiCIAiRjs93sESUBiBOKVXp1OcCeDhgMwsilZcP\n0nr24GcMr6RqtaXuiNYLqrkM7sj0vVqnExcy3nSpo2/3yP3wZUlCbo7WFUO6aN339pVav9HlJ7fn\neXpfd63/PfMcrXvdv1zrhiNHIDBG26+7pavWPZ9YpXX9/gNBnVM4svGp1gCA1ae+ZTjKd60ltZx+\n74rX7tQ6oYpbZ2zlkJqk6yu0PvFCDnJ6bux0rc994C6t9wzkvhv6/1vrgX8ep3X7CbERXAYANJAD\nmxZe/5zhFQ4k3VZ3SOv8p4u1jliXEfxzEXcE8BkRNZ7nf0op853CgiAIghBj+FMPdhOA4wM4F0EQ\nBEGIGmI2VSIl8sP1w5ezS61DPLuFGwzOiTO/ZjfS0gtfMJwpGWbUZ0Tajq2WUAIvjwOjOIfIff94\nR+vfph6CGfUe+HXuzOIAkTtHsr7lVA4i2TSY56Dq6tyfNMqp78BBO2uueUXr05bdrHXm+/ODOqdw\npGZvqxY5QINsAAAgAElEQVTHXj/Aj3amnnms1nk73D/HSfuE9XZDCtWrsi7Uun2nfVr/9Oh/Db25\nfau9kezw9J111/D7auu4ln8bADhv0Vit82pLbJ9TMJB9sIIgCIJgA3KBFQRBEAQbiFkX8d6rT9J6\nycBXTNtctvF8rUedslBrY7RwNLP21RO03vDbV922f3gPRwquP9TBtM2avXw8PZkjOc/tvFrrl3LY\nZXf9nGFa7xrPLj61ODpcSII9FHzuSLVXcxE/VuiWtEvrzaM5gr3L5xla16/d4P7kDfz4p34vu4W3\njemjdYLBLbz4KLfP+OIXrWPKWdy61n2b4tb2zyPIyB2sIAiCINiA2wssEb1JRLuIaKXhWDYRzSCi\n9c7vktleEARBEAx44iKeBOBlAO8Yjt0LYJZS6gkiutf58z2Bn5597C1qcNvmxpw5Wp/fyruEBx3m\nxrtvFKYcvNKReGPO+eZJNw42VGs98APeXN/zIUOCiMO8Md9Ie5gfn3bZMK3ve46TJrzVZbbWj77J\nkZ9zjzdPsRbtbDvX3I12KNeQjs+i75z9vbTu1Nn87xAtJE13JCo4545b9LF/PvG61stufVnrqX9k\ni9311dVa9/o/fgzRUGmopmMg/pjeWi8YZ0ygwOvzxqWjtc6pWYVYZNIQ83yc62q5rFH+t7ybI1rc\n527vYJ25hfc1O3wJgLed+m0AIwI8L0EQBEGIaHwNcuqolCp36h1wZHUyxVllZwwApBjugoTAIPa1\nH7GxvYh97UdsHBr8jiJWSikisryjD6cqDkaeOOcDt228dQvPquLo4nYzfgUA2J0awQ77jn/wIwBA\n53jzf8TTX/qL1t2f4ohf9073plAy2+vwFe7z5xYmcxToXOR5OZrvhNMaPtzdPBqz1R7305o9j13s\nk0e8qPX9Ia7RYad90z/iijhPzT5L6z/ey1HE/xkxUet1Izla/o3zeI09+c3FWnf7nMu9rf8Du4Jb\nEeuNdZzUOP9ufqQSqvQzoVjD8e3ba51JxhJ5/H//3WF+bBGNOwN8jSLeSUSdAcD5fZeb9oIgCIIQ\nU/h6gZ0CoPHJ/WgAXwRmOoIgCIIQHbh1ERPR+wCGAWhHRNsAPAjgCQCTiehGAJsBjLJzkoEivl1b\nrTvEL3fR0jfGfn+d1r3KFwX8/KHk7FWXap338lKtvXULx6Wlab3hgf5ar7FI9iE0tdkzwz7Uukbx\nA4iUisjPfW03xoLo3e9i/fTTXCrx5hvZdXz3NR9rvW4Uu46rRnKClPom8a7s+rx65XVaZ61fj1hk\n7/k9tB6QHPjkPMZHTGjgv4MylCIMNW4vsEqpKyxeOsviuCAIgiDEPJLJSRAEQRBsIOpzEce3zdZ6\n3b09tR6SMiMg579+yzCte49nt3PYhEsHiEM17I5J7cjRgQ2/bnbf2VDea8f/OJ/wmiJxC3vC2ic4\n+ndEGic/qVLsoN96HrdPOZ7L/Rm587ypWt+wjJMfdMJqs+YxQ92OnVrn/5P1R2+eqPU/7yjUet4V\nnIAly6L02uzj39P6xIdv07rrA/P8m2wEses093sonlnK7vnCIaT19tvZzUsEU7446TWtS+s4Acut\nbxjK3j3mvhShncgdrCAIgiDYgFxgBUEQBMEGotJFHN+G3QWbJuRqvfZU9yXXPOGYN8Zp3X3iFq1V\nzcGAnD/UrKvu5BDpHGk5/wROzFH+AyfguLWUo4t/WVNger7Hh3I05sh0jq6uM2y77/vJeK3fvXCC\n1oNiozKgSz698EXDT4laGRMbbLjk31rHE39urlfmcd7P7GOfcqcAzDEaqSvfoXWvV9nWm0ayPsmQ\nEvu5Cn4EtbmqndYzr3ta6wt33611x5dC6760m4Ju7tMj/G8w54cuGMJu4Q7xaWbNm5GuVfdEfi9Z\nMu4FrU+Id7jn8x8Jja3lDlYQBEEQbMDXcnUPEVEZES11fg23d5qCIAiCEFn4Wq4OAJ5XSj3Tsnlo\nMLqF07/kX6uk8G2z5rht+2Ctr2/HkZkDktybpPBp/VkDdRZlrCKZhaOPBwB89dGv+thvUw9pbcxR\n/FH36dyR9+hbcnPZqVr/+NUJWndaw67MU/9g7uKMt055HdXEWfzexjU873WOeM0oM4/e/OdLHHWJ\nGLWlr2wZyXmJT0riqPhDinPszhhzmtZxi9doPfQ/XDJv3t3sLr5queOxSNwPvwR2shHEycmJhp8S\nTdt8dSRF69s/uV7r1HIOL152Nz/+SyY+T4ch2wMwS9/xtVydIAiCIAgu8OcZ7HgiWu50IWdZNSKi\nMURUTETFtaixaib4iNjXfsTG9iL2tR+xcWjwNYp4AoBH4Min8AiAZwHcYNbQ1jJJhh3IZZNytF5S\n+J5Z6ya5W0v+fpzW05/er/WAtu433VdccozWrf8737O52oQd9m1YugoA8GrffvrY82ezO7d0BNv9\nqkHmG+c/mzxE64L3y7Su27xN6y4NHNlXedkgra0iXx/6jFNed0PwNuyHulzdQ1su0rrsdc7vmvXB\nYq3b1ZrbI/6Y3lq3iTO8sSqL3fshINT2tYKKOMHH97c+bXiFk0uMWjuS289dprXxl+jz9z1at53L\nfW9/430AwIs9+gRgtq4JVxtb0XfizVp3e20T63Je5/GZmVpPvpkfEY5Kd1/6Mlj4dAerlNqplKpX\nSjUAeB0IcUFJQRAEQQgzfLrANtaCdXIpgJVWbQVBEAQhFvG1XN0wIhoAhyekFMBYyxPYyJYHOIpy\n5cCX3bYvmnC71vnT2T05NfsMrSddcorWq4dMMj1P3tgNWlf+16OpRiSqjl3qSd9wgohe33CbRYiH\nGXlg+7rPSArsutj9c6GsGE2Ze/h0TvjRBqw98fOpRP77pJCUtPOG+jSORjXmHC4xlENTf+cSmAR+\n/OEJpyTvBQDE9WcXccPyNVbNI47SbZxsA8eYt9lSx7sTLnyBk3B0eX6B1nUN5ut2w7180lHpP5q2\n2eycQy+UupuuLfharu4NG+YiCIIgCFGDZHISBEEQBBuIyFzEjdFjt132hVf9Ct/m0mpGt2Xm+xwJ\nfKC7odTXEJiydmovrXOwx7yR4BXtsjhhh1Uu3fazOO+zJ25nAajuyElBChJSXbQUmlObav72OPcI\nZ1ShectM2xg51L+z6fFGt/P+Y9voY5nLTZtGJHlT2X4159ZqbUwEMbeKy1fmTlyhdYPRLWzYLbLp\nSd5tsOoq42NBfhTyay27nfu8cNhxPi/nHijkDlYQBEEQbEAusIIgCIJgAxHpIt483rEB/KbWs922\n7TH1T1r32r7YRUvP6bSgKiDniXVqhg/U+v1jnte6XrEr891KLqamDrHrRxDsZuu55hHyn5WfYPjJ\nPHI4PouT2xU9VGzaZle9o+xj9vel+lg0PfpI/ZQjge+4j5+3vZrLj+Quz6jQeu1Pe7X+7D/DtE7/\nLZcNXN+fS1nCYgfDee//VevCZcFLSGOGJ9V08onoeyJaRUQlRHSb83g2Ec0govXO75bpEgVBEAQh\n1vDERVwH4C6lVD8AgwCMI6J+AO4FMEsp1RPALOfPgiAIgiDAs32w5QDKnbqSiFYDyAVwCRwJKADg\nbQCzAdxjyywBxPco1Pqtmxor1pu7CDbWsQu374OlWtdbbFiO78dRwX++4ivTNi/t78bt55ZoHfZJ\nPcOY8sG8/LpYRLg+vOBCrXvuX2L7nKKNQ7nmJcCyFpofF5hunxuSn3AabGxYwaXrehhcxJTA63nN\nQ5wDemonLqVm5Op1VwIAEsq3mL4eTax6gHO/3/oIvw+/mMMJbB5sv4r1faytmFXF7//3PDZG68K3\nFvo8z0Dj1TNYIioAcAKABQA6Oi++ALADQEeLPmMAjAGAFMg2gUAj9rUfsbG9iH3tR2wcGjy+wBJR\nOoBPANyulDpIhr1JSilFZF7BOVBVHI705rRbxoLHZoxYxJkb83e6T5O85VH+NH9zm19N27y1ntMy\ndqoNn5x9kVYlw0hq/wrT41vqjmjd60W+iwjVLxfJNt4z5Kjp8Tabak2Ph4KwtW89T6XBsPpUgmGK\ncfxedGBkkdbr/2B+13qwoVrrmgmO/bEJsP8ONtQ2Tv6a71RLjp6k9dMvcKnxv2ZvdHuen6t5R+sT\no6/Tuu1PoQ1mssKjbTpElAjHxfU9pdSnzsM7G5P+O7/vsmeKgiAIghB5eBJFTHDkHl6tlHrO8NIU\nAKOdejQA79IqCYIgCEIU44mL+FQA1wBYQURLncfuB/AEgMlEdCOAzWgSBhB4Nl/keYHotK8y3LY5\ncDWn3JpW9IzhFX4+0bhPDQDybuFUftG0Vy3YGO3+xQnmdt9Ux8WT1eISCEIoiPtpqdaXrOOi97Mv\nflbrB4su0Hpavrlb2MiJ39ymda9PFrhoGb0kzOJ8BDOP5ffqmRjg1XnisNR9oxDjSRTxTwCsrm5n\nBXY6giAIghAdSKpEQRAEQbCBiEmV2Gqb66lWKY6WzNxiHjmZkM/711585CWtc+PNw9bPNkQj520V\nV6XPGCLOE67mWDgru//pE97T1g3hGR0YiXx1JF3rlEUcsSll2D1gLBdc3zeddx281WWO1vUWsbl3\n7+Do4l43LTJvJEQlcgcrCIIgCDYgF1hBEARBsIGIcRHnPzpX6+GPnuiybQLMq+bUbeW0Zv9XONC0\njZE8iFs4IAw8VssfjnvbtMn9u/hv2uMfXHU6VIWSo4XkrUla/+WT0VoXVojr3Rvq17FL/X5D5PCa\nh3tq/eNFHF187kKu4lV4uzGhSpk9ExTCErmDFQRBEAQb8Kdc3UNEVEZES51fw+2friAIgiBEBqSU\n67SUzjSInZVSS4goA8BiACPgSCxxSCn1jMsTGEilDEUAFBRyUYgC6uPxRH9S0xCPBBAIhDicQuZb\ncEtUMfagHElIxmA6FwBQq45iBeajCkfQCqk4DoOQSEke9d2oSrAdvyIRyQCAHjgW7RwZIjXV6ghK\nsAhHUQ2AkItCdKGebse16udqzAVqFg6qfab7koNhXys7ufpdt933GwBA+ZcfIKmsGO3bxWPp9/mo\nQz0efWY/3vrfIex11opod/Zw9Htvf0Ds66uNZ6qPFyulimCCrza2076u+nmyfl3ZKRrXcCS9R7jq\nazWuK/sCsobtXsNG/ClX5xVEFE+Iw2CcixSkYiFmoZ3KQTplenyOkzAUSZTssk0OuiIf3VECDocv\nxRpkowMKqA9K1RqUYg16or9HfQGgC3qiK/Vu0V7/biD0RH9kUhbqVC0WYhayVUeUo9TluFb9PBmz\nxRyCZF/Adxu37j8QHz1Vhutva5q2+pabMvBW4l8MR+Y2ed1X+7rqCwTfxnbb19f1C8TWGo6k9whX\nfT0Zt8U8ZA3buoab49Uz2Gbl6gBgPBEtJ6I3iSjLos8YIioGsJIApFI64igOHZGP3dju88StyKL2\nSETTT0W7sR2d0RUA0BldLcc16+sJydQKmc5fP4ESkYoM1KDK7bhW/bwh2PYFfLdxapfuyMry/rG/\nr/Z11dcbImUN+7p+gdhaw5H0HuGqrzdEio0jdQ1b4U+5ugkAHoGjitgjAJ4FcEPzfo1lkojoD/FI\n+KjxeApa4QD2NW/ukl8wB1BALrohj7q57+DkKGqQTI6N4klIwVHUuOnRlK3YiHK1BRnIQi/0N3Ud\nNVKlDqMS+9Ea2V6Na+y3H3s8HjMc7Au4tnHBh7ygPz6zI/Y37MWkgzl4fvVZ2F02Gwe+W4rk3f/Q\nvytssG/zvqGwsV32dYc36xeIzTUcCe8RzfvKGrYm2GvYCo8usGbl6pRSOw2vvw7gS69G9pIinIEU\naoWjqhpLMAdpKgNZ1N7r8xCRV4VF89Ad3dAPALARJViH5TgGpo/nUKfqsBzz0BsDkECJTcZxNW7z\nfnnK8zEDRaDsC3hn46zzi9Bu5OnI/cMq2+xr1jfYNg6Vfb1Zv4CsYSA83yPM+soaNiec1rDP5eoa\na8E6uRSAu8rmZQ2GXY3VqEIyWrlo3pSUxk8glIL2yMFBLz51JSEZNcpxy1+jqpAE988QGkmmFBAR\niBwPv63GbVANWI556IQu6EC5Ho9r1s/TMZsRMvsCvts4oU06KD7ONvta9Q22jUNlX29+z1hew+H8\nHmHVV9ZwS8JgDTfBkwdijeXqzmy2JecpIlpBRMsBnAHgDjfnWdSABlSpw2hQDdiJrWiPlpFgZtSr\nOtSpWq33YSfS0NpNL6Y9clCOzQCAcmxGe+R43LfxDwMAu1CGdLQMBlBKYRWKkYYMdKVeHo9r1c+T\nMU0ImX0B179r3aZS/fXV2e1xYH0CJvfthNzflaDdpcXI/V2Jy9/VV/u66htMG9ttX1d4+nvG+hoO\n1/cIV31lDTclTNZwE9xu0wkkxvDwHBSgkPp61O+IOoTlzqTvCgqdkG/Zd4VagArsRi1qkIQUdEM/\ntEcOVmA+qlHlMgTfrG8FdqMS+0EgpCAVfXGi9uc3sl/tQTFmI92w4HrgWGQi2+W4Vv12YKvlmJ5u\ncbDLvlZ28sTGwbavq76ubOzpNh1PbRzu9nVlp2hcw5H0HuGqr5WNvdmmI2s48GvYSFAvsJmUrVzt\nnRLc4+oPK/YNDK4usGJj/5E1bC/u3vzFxv4TlhdYItoN4DCAPT6eop2PfX3tF45jdlVKmUYWOO27\n2Y+xo8lO/vRzZ2NZw/71lTVs75iW9gVkDQeor0sba5RSQf0CUBzsvrEyZiTOOdLGjMQ5R9KYkTjn\nSBszEuccSWMavyTZvyAIgiDYgFxgBUEQBMEGQnGBXR2CvrEypr/9Y8VOobKvP31jZUx/+8eKnWQN\nh++YGr8usER0PhGtJaINRHSvh908i7sPbN9YGdPf/rFip1DZ15++sTKmv/1jxU6yhsN3TI3PF1gi\nigfwCoALAPQDcAUR9fN3QoIgCIIQDfi8TYeIBgN4SCl1nvPn+wBAKfW4VZ8kSlbxSND19bylFjU+\n9fW1XziOWY3DOKpqTPdfJVGySkFa2M050sasRMUeZRGCL2vY/76yhu0d05V9AVnDgejrzsaNeFxN\nx4RcAFsNP28DcIqrDilIc1mkV3DPAjXL8jWxb2CYqT7ebPWa2Nh/ZA3biyv7AmLjQODOxo34c4H1\nCCIaA2AMAKQg1e7hYg6xr/2Ije1F7Gs/YuPQ4E+QUxmAfMPPec5jTVBKTVRKFSmliny9VResEfva\nj9jYXsS+9iM2Dg3+XGAXAehJRIVElATgcgBTAjMtQRAEQYhsfHYRK6XqiGg8gOkA4gG8qZQqCdjM\nBEEQBCGC8esZrFJqGoBpAZqLIAiCIEQNkipREARBEGxALrCCIAiCYANygRUEQRAEG5ALrCAIgiDY\ngO2JJgRBsJ/yO3+j9f1j39d6ZPperYseH691px/3aR134LDWdZuNydkEQfAHuYMVBEEQBBvw6w6W\niEoBVAKoB1CnlCoKxKQEQRAEIdIJhIv4DKXUngCcJ2DEZ2byD4nmv2Ll0J5al51heMFYH8FQaOg3\nJ63VevE3XJWv6+PF3Lz2qNdzFQRvqD/jRK133Val9S8DX9a6wbBwGwx9F973Ev9wH8tZVZyb9oG1\nF2ud9dv1fs5WEFzzr9K5Wm+ta631uI//qHXBwG1aX5fH7V/9dajW5eu5+FX3yfw+HDfnl8BN1gfE\nRSwIgiAINuDvBVYB+JaIFjurNbSAiMYQUTERFdeixs/hhOaIfe1HbGwvYl/7ERuHBp8LrgMAEeUq\npcqIqAOAGQBuUUr9aNU+k7JVIOoQUmISAKD+N8foYxuu488KE4b8V+szWh3y6txxhs8cDU0cbOac\n+uCtWrf9zzyvxvKFBWoWDqp9poV+A2XfWGem+nixVTxBKGy874bBWk9+8Gmt8xJaaR1neLZhdBF7\ni3HNn/qAY223fSOw61rWsL24si8QXjZ+rpTXVp/EwFT5WVdbrfXNYx1rOGl6sVVzn3Bn40b8uoNV\nSpU5v+8C8BmAk/05nyAIgiBECz4HORFRGoA4pVSlU58L4OGAzawZccf20briqToAwA/HT7RrOI85\n7o8rtd7+nxBOJAzYNZ73YjYJqDHQ713ei5n7Q53WZUPNl2L6Fv6Q2OHVuaZtooWGISdo3fox3o/6\nSeHzWidTK7jjUAO7AFfUcgDT/et+p/XOpR21fvb3b2t9QWql1iNvnwkAmP1eW55jNd8dCE0DKveO\nYI/awQJet6kDOQZ04YkfaF3iDIq8b9gofayudIst8wwn4lJ5TSaSuZdwTS2v4Qu/uU3rvG/Nbxr3\n9Y7X+sU/vqb18Y8tBQCsW8ABVPX7D3g5Y9/xJ4q4I4DPiKjxPP9TSn0TkFkJgiAIQoTjTz3YTQCO\nD+BcBEEQBCFqiJhUiWvuSmN9/L9dtt1Qy67H9/ebPxaeOmmI1gePqdW67UI2Sbsr2F3zRe/PPZ9s\nDLHjdnYLz7qLA3AakGTaftU1vF9zz5W8jzM73jzAYeL+Hlp/PZPjjurXbfR+smHOhqsStV7Xbbrh\nlcSWjZvxzL7eWn/54Jlap366QOs0bNK6Z9v9PO6FnfhEBhfxVa0dewi/PZVddAmzFrudSyQQN4D3\nsm+423ytjuk/R+vLMpeZn8eg8xI4vvPnanZ9Lqkq1DqeuMf6ox0AAOow/x/EApUXHKd194SftB69\nmddtxUh2I/cqW+j2nKkGfVNn3kO7/vcTAAADrxinj7WfYH8waiOyD1YQBEEQbEAusIIgCIJgAxHj\nInbHJWtHaF37KLu8Er4zd2l1wlyDZiiBTbL/0gK34y7ekad1Z6z2YKaRT/0wTtf31Z1PaZ0R590+\nNiu3sJGeyTu0/jI7zUXLyKfrFN6/+qRhj/c9bUu0/raKbfDY/ddp3WZRudapv7Jb2IrVT3bT+ous\nGaZt3jvgiGqOGrewIXr1pEkrtP66wwqz5vi1lvfQv1Vh/qjpv19zur7cH/nRVNoSfry0azjb+pZH\nJ2j9wFtXAwDydkd3dHxzMtZzFO+iGl7zGw9wtHpmme+PgFR6fYtj1dlut6zagtzBCoIgCIINuL3A\nEtGbRLSLiFYajmUT0QwiWu/8nmXvNAVBEAQhsvDERTwJwMsA3jEcuxfALKXUE0R0r/PnewI/PabP\n81wUuu8RR7KCxHYcfdfjtp1aqx2+u7TWvsKb/dcc/6rb9mkftnbbJtrYdyf/Ldp74OY10v+1W7Q+\n7xKODnyyk3lk38Ttp/MP85d7NVakkTxtkdZz53IiiBGtufKTOsKJHtJ3z9eanZPWVFzHKRcXnPeM\n4ZUU0/bv/O8cAEAeosOFWXsyJ6t5tMObpm0Kp96kdd/nKrSuX7vBtH03mK/bOmKX5J6Tu5q2Kfif\nI5mIJ3+7aKJh+Rqtr/svJ57JGrjLq/MYXf7rHuuv9aJznjW0ciRmabuqpds4GLi9g3XmFt7X7PAl\nABrTv7wNYAQEQRAEQdD4+gy2o1KqMapiBxxZnQRBEARBcOJ3FLFSShGRZfkOZxm7MQCQ0mQ7sHcY\n3Qo9x7V83Vs3C53AUZprx3F+13UXcBILY5bMzXVcxPe8Wbzxvu8MLkodCidEoOzrjoTOHGs9vMsq\nt+2NCSJe/nS41gUPs7vx23pOUvHkzcHb/O0twbKxkSb5Uv3InWr8u33xMCcCyYozz2k85TCHU+Q9\nHhzXcNDW8CH+H/78cLrWI9I4WrjdfH5LtHILe0J8Ntvx14s5Z/pNW0/l8+/wziXqD6FYw57QZh1f\nOj68jnNi/znXkJ+5bLvWxpz09NJBrdf1Mj7O47Xd48uxAIDeX3Lhdd9rTXmPr3ewO4moMwA4v1uu\nFKXURKVUkVKqKBGBKUckMGJf+xEb24vY137ExqHB1wvsFACjnXo0gC8CMx1BEARBiA7cuoiJ6H0A\nwwC0I6JtAB4E8ASAyUR0I4DNAEZZnyF82HsTR1Ee/S3nYl0zcIKhlflnjmtLRmvd6wYu3hua2LTg\nc+BUjoT8v/ZTzds0sAvu7X8Z3MKvh6/7NxqpP4MTgVz8CieRaBdv7hY2Jq947bpLtSaY59+NVFQx\nl5Z8/JFrtL7jDF63/WZymUCvo3sNkcNrHuppeOE7Pv7EsVqn1rhPCBLtZH3KOwOeu2OY1uue7qB1\nXHw7racO4vfq7gm8np/c21frrx/k8zS6hlUt/42DidsLrFLqCouXzgrwXARBEAQhapBMToIgCIJg\nA1GTi9iI0RVccTpvzF9yxnNap5D7X31bXY3W+5a21zprAJe3aljqPqI2Gmi9oEzr4at/r/VFndnF\n8+rUC7QuDJBbeOMn7GrrhN0BOWc0UjWCc+Ue+zf+m4xpXWra/sdqXsMvjL5ca5q7NPCTC0PavDPP\noPm4P0kfEjrxbsVNv39N62+PcLnB9K/Z7W7cpRCrNBw5ovWO6myt1ww1TwRijBA+bt61Whf+lcss\nGnNxBzNi2Ay5gxUEQRAEG5ALrCAIgiDYQES7iBMKObJ18Bdrtb49+3mtEyne2MOr8+cl8H6xFaNf\n1Hrf1ew6/rCSowJfMbhIe/3b4VKtK+WyVZFM3dZtWieczce/RhutCy3yslpRdOFKt23altS4bROr\nxPdl9/m5D/+o9X1tjY8tOLJ1RhW71/51+UhuURwbbmG7WX0/vx/VK3YA//Ulzm/cqTo68jr7Q8NQ\nzve+5Wbeh/FlwVuGVrxuPzEkP3n20Su1zv8v5zKvawjP/RxyBysIgiAINuBrubqHiKiMiJY6v4a7\nOocgCIIgxBq+lqsDgOeVUs+0bB48juZx1Nnt2ezmSqZEs+YeYXQp11qEoBk37I9rs5H1NS9zI+c+\n9gEvc3m2vCcNG8vD1KURTP6e87XhJ45qPdJQq3VcfajjAMMLYxKJ+/4zSeshKRz/2mCInZxVxXln\nn7/SkA+meIU9E4wxjH+P1b/j//851fx4qdMLsekWTsjP03r9U221Xn36G6btDylOBjHzCEdk/33J\nJVoXvhNZSWt8LVcnCIIgCIIL/HkGO56IljtdyFlWjYhoDBEVE1FxLSRgJdCIfe1HbGwvYl/7ERuH\nBkqFd0AAACAASURBVF+jiCcAeASOfbyPAHgWwA1mDZVSEwFMBIBMyg6ovy9uDpcg+k3x9Vo/c+zH\npu1f3Mbhr5tmFpqflAzaYrb9L+DSec/nT9E6O75llYol41/QesQ03hjdsGy1+cm9xE772sGRS0/R\nuk3cz6ZtLiy5Wuv075fYPid3hNrG+27gxCmTH+SSc3kJxtzCvHDvKh+k9YwpA7VOGMqt82o4d2ug\n1qKvhNq+3hKXxrmbh77Aa9iYi/uxazlymBD6KG07bUwJfBnZfisnPLlzDL8PX5OxQ+vPD/POgwfe\n4v/1rh+Va32kN+cfvvPpaVpP6cHnr9/wqz/TDgo+3cEqpXYqpeqVUg0AXgdwsrs+giAIghBL+HSB\nbawF6+RSAO43NAqCIAhCDOFrubphRDQADidqKYCxNs7RIzqPYDfXszjGohW7IPIN2lsqHmV91t/u\n1vqXm18wac2suZVdS71u9Hn4iGbOK5yjtValmLbZN7eT1unYZPucwgWj63Hdo8dpvWwkJ05JJvOS\nc/HEn5Wf6MRuy6fHcgRrnOHz9JSbOGzijYvO1bp+7QZvpx1zVA/pp/X97V7XeqzhERT9HHq3sN00\nuobXvXCSPrZ+BEdSH1L8rPf3G0ZoXXMr7/7IW8br07ivItng/n1q5Pk85s28Q6THneHvIva1XJ15\nnLUgCIIgCAAkk5MgCIIg2EJE5yIOB6oKat03cpKw2/cEGNFCrWJHUINFwa6u0w5qHfYhpX4S35bd\nZQff4+jKNce9Ymjlft0Yc982zb9tzsVpFVq/XMDjJq1t2TahW4HWdZtK3Z47GjHa4Ip/faH1r7WH\ntF7/d340lYjioMwrlNCxvQAA60dMMH395J/5yWHBZcsNr+z0apy0lbw7o/tFnNinyquzhAa5gxUE\nQRAEG5ALrCAIgiDYgLiIPSQuI0Pr3Zdzibr/nvWKWXPNcT/+UeseDy/T2tw5Gp1semKw4afFpm1O\n++UqrdsuW2fzjEJD3PGc3GHbOY4o3uuv+0YfuyVrvc/nLiq+0vT4f4/nEmC9EpNM21z07Hdab3ks\nu8XrQzL49TeGn6V1JGz0DxRrb+bI9usyd2nd56c/a9312+h3CxvZ/Ns2LY79du1FWne7gd25/rzf\n5X3DmXqfveUTrW/pNVrr+nUbEY54Uk0nn4i+J6JVRFRCRLc5j2cT0QwiWu/8bpkuURAEQRBiDU9c\nxHUA7lJK9QMwCMA4IuoH4F4As5RSPQHMcv4sCIIgCAI82wdbDmeGBqVUJRGtBpAL4BI4ElAAwNsA\nZgO4x5ZZhopB/bXcdAcfXn7ai2673rz1DABAz78d0MfqjhwJ3NzCHErmyL92x+9y0dJBmye4rJqq\nPeqiZWRBJ3Bk6fUffqn1pWmeF6gylpz76785Q0mXj7Zq3WEz58eOP6a31nunGhNTmJdItHJNN457\n87Tr9LG+h0o9mXJUsP9afrSx9HJO9nHhuku17joqdsv+JR9oeWzrjK5a5x0uC/iYBQn8v3CkOz/O\nSI5UF7ERIioAcAKABQA6Oi++ALADQEeLboIgCIIQc3gc5ERE6QA+AXC7UuogEVfvUEopIjLdskhE\nYwCMAYAUpJo1CQnGO4vdAzO1vnjcD1qfnTFJ66Jk9wXSj/2BK2j0GOv4RNVQWerHLN0Trvbd9A8u\nRL2iv/GOnz/T/WYJBzZ1KOY7sHALAPPWxnEpnAKy7Sv8Kd6bu9bj53EAR5cn+HhOMaeWq4M5VXkc\nkNcn8bDhFfPUlOsMHoPfLeC9i4VPO/4SPYsXuB3TH8JpDdeezWn/bv3bZK2/OJyrdc3/ccBTnB8p\nV4OJHTbOmbbdIe7jY1WdAvPfayzWXvoP3tf90SEu3J66fo/W7t+dQ4NHd7BElAjHxfU9pdSnzsM7\nG5P+O7+b+gGVUhOVUkVKqaJEtCznJviH2Nd+xMb2Iva1H7FxaPAkipjgyD28Win1nOGlKQAaP2aP\nBvBF876CIAiCEKt44iI+FcA1AFYQUWOJiPsBPAFgMhHdCGAzgFH2TLEl8e0cboKaD7j6yJW5C03b\nPlvCFS7uOmam1n2SuZC30f1rrDhilcpvmSEG5/Z7b9G6x7QS7ltZaTn/WKCuk/tApX172JXZrrra\nzukEl14FWr7V9T2XTUuOstP1+mc4kq7rG7w+G7y0TdJ03o952R9v07r6dk6PePBHDpnInc1u5IJ5\nvFc72tNUauLYBbn1Bn4vGJiyReubxvPfJmWO+XtNrFG/ZRsA4LhXx+tjr9/A1bKenHyN1p5UF6q8\nbJDWox7g/eHXt+ZKaQOm8HrutSH8/w6eRBH/BIAsXj7L4rggCIIgxDSSKlEQBEEQbCAiUyU25Dui\n+Kb1neS27dWD33LbxoobN5+j9a//6qN1m5/ZdZRRNp/n5fNI0UFCbo7WDw1y/0i+8H0rx0hkE7eX\nqwFNPtRB6+21nFpuwmzHo4s+/+LYwA4bOEI4UGvJ6C5Oms7HMxGe+wZDwe4xJ2u9fhhXhjn5l+u0\nzpoa/u7IYKPqHI83Cv7NOwDG9LlW6zfeeVvrscVXm57jyj68Pm/N5hCfrHiOdO6/8Aate90cWX8H\nuYMVBEEQBBuQC6wgCIIg2EBEuojjdzmiIft+wtFrD5z7mdb1ij83/HPGJW7P12EBuyqzv+KItYYq\nLumbXsOuYDs220cDdbm8CfyyDPMN+Oev5jRzST9wmrloilitK9uu9Tu9803b9IQjeUO4bpCPduLb\ntNb6nLHztJ54gB9ztB/LqU3lf96a+r2cQKX3nRyRfceoP2ld+PvNWn/Ve6rWZ6/i94NJSzg1ZZdP\n+Tw5X3NEfaS9T8gdrCAIgiDYgD/l6h4iojIiWur8Gm7/dAVBEAQhMiClXN90O9MgdlZKLSGiDDgq\nZo+AI7HEIaXUM54OlkoZigAoKOSiEAXUx22fRn5S0xCPBBAIhDicQuZbcEtUMfagHElIxmA6FwBQ\nq45iBeajCkfQCqk4DoOQSC2LT5v13ahKsB2/ojG9WA8ci3aODJGaanUEJViEo6gGQMhFIbpQT7fj\nWvVzNeYCNQsH1T7T8Ntg2NfKTrXqKBYeW4LaPfuR2K4NNkyuQVab+Cb9xt6xBx98BiQmpaNoyJ1I\n+maRrfb11cYz1ceLlVJFgbRxIOzr7nf1df26slM0ruESVYxdrfYgoVU6el91N365/1Xsq6jH5X/a\ngZ82pCC5Y2t0v28E2l62zbRvMN8jfLGxK/sCsobtXsNG/ClX5xVEFE+Iw2CcixSkYiFmoZ3KQTpl\nuu/s5CQMRRK5zqOZg67IR3eUYJE+Voo1yEYHFFAflKo1KMUa9ER/j/oCQBf0RFfq3aK9/t1A6In+\nyKQs1KlaLMQsZKuOKEepy3Gt+nkyZos5BMm+gLWNWx3bDZ0vGYr9X/yAZ19egEf/ntWk3zWXpWPl\n1suwdvmHTY7bZV9XfT0Zt8U8/LSxv/Z197v6un6B2FrDOeiKtIt/h60z/qePPflyBc46LRUH77sJ\n5ZPnoXzyPLRFy+fnwX6PcNXXk3FbzEPWsK1ruDn+lKsDgPFEtJyI3iSiLIs+Y4ioGMBKApBK6Yij\nOHREPnZju1kXv8ii9khE009Fu7EdneGoU9gZXS3HNevrCcnUCpnOXz+BEpGKDNSgyu24Vv28Idj2\nBaxtnH66o4JO+uknYuo3LWvfnjYoBYmJrVocd4ev9nXV1xsiZQ37un6B2FrDWdQeCSlNK8pMmX4Y\n145ypO5se/Zx2D/PvEZusN8jXPX1BlnD9q5hK/wpVzcBwCNwBHY9AuBZADc076eUmghgIhH9IR4J\nHzUeT0ErHIDn5bsA4BfMARSQi27Io24e9zuKGiST4409CSk4ihqvxt2KjShXW5CBLPRCf1P3ciNV\n6jAqsR+tke3VuMZ++7HH4zHDwb6Aw8Z9H6gAUAGlFGbXEO7YPgQA8HzOHN0u8YflIFQh6Rv+hBoM\n+zbvGwob+2tfX9ewN/YFYmMNV2cDKgGoau/4eefuenTumACUA4lZaajdf9iyrxmyht0TzWvYCo8u\nsGbl6pRSOw2vvw7gS69G9pIinIEUaoWjqhpLMAdpKgNZ1N7r8xCRV7HeeeiObugHANiIEqzDchwD\n08dzqFN1WI556I0BSKDEJuO4Grd5vzzl+ZiBIlD2BZy/K3mWpSkY9jXrG2wbB9y+Hq5hb+wLyBoG\nGtev5+1lDXtPrKxhn8vVNdaCdXIpgJVuTlVmrE5TjSokw3N3YUrjJxBKQXvk4KAXn7qSkIwa5bjl\nr1FVSPKiHmIypYCIQOR4+G01boNqwHLMQyd0QQfK9Xhcs36ejtmMkNkXcP6uDQ63cE3DEbTK8szG\ndtvXqm+wbRwQ+/qwhr35PWN5DXdsH4/ynY7drkf3HUJi6zQ3PRhZw54RI2u4CZ48g20sV3dmsy05\nTxHRCiJaDuAMAHe4PAuwqAENqFKH0aAasBNb0R4tI8HMqFd1qFO1Wu/DTqShtZteTHvkoByOjc7l\n2Iz2yHHTg2n8wwDALpQhHS2DAZRSWIVipCEDXamXx+Na9fNkTBNCZl/A8btu27cU9Xv3Ydu+pWiz\npTM2DqzGxoHVuDh3oP5qjp32ddU3mDYOlH19WcOe/p6xtoZzn1+MxF1H0PXBuTgvZwBqNhZi2IB0\ntB6+AYevmo2OZe08GheQNewpMbKGm+B2m04gMYaH56AAhdTXo35H1CEshyPbioJCJ+Rb9l2hFqAC\nu1GLGiQhBd3QD+2RgxWYj2pUudymY9a3ArtRif0gEFKQir44UfvzG9mv9qAYs5FuWHA9cCwyke1y\nXKt+O7DVckxPtzjYZV8rO3li42Db11VfVzb2dJuOpzYOd/u6slM0ruFIeo9w1dfKxt5s05E1HPg1\nbCSoF9hMylau9k4J7nH1hxX7BgZXF1ixsf/IGrYXd2/+YmP/CcsLLBHtBnAYwB4fT9HOx76+9gvH\nMbsqpUwjC5z23ezH2NFkJ3/6ubOxrGH/+soatndMS/sCsoYD1NeljTVKqaB+ASgOdt9YGTMS5xxp\nY0binCNpzEicc6SNGYlzjqQxjV+S7F8QBEEQbEAusIIgCIJgA6G4wE4MQd9YGdPf/rFip1DZ15++\nsTKmv/1jxU6yhsN3TI3cwQqCIAiCHfj5oPx8AGsBbABwb7g+dI6VMSNxzpE2ZiTOOZLGjMQ5R9qY\nkTjnSBrT+OXzHSwRxQN4BcAFAPoBuIKI+vl6PkEQBEGIJnzeB0tEgwE8pJQ6z/nzfQCglHrcqk8S\nJat4JOgCtt5Sixqf+vraLxzHrMZhHFU1phuckyhZpSAt7OYcaWNWomKPstjjJmvY/76yhu0d05V9\nAVnDgejrzsaNeFyuzoRcAFsNP28DcErzRkQ0BsAYAEhBKk6j4X4MKSxQs5r83Ny+kqHFf2aqjzcb\nf5Y1HFhkDdtLc/sCsoYDjZmNzbA9yEkpNVEpVaSUKvL1k4RgjdjXfsTG9iL2tR+xcWjw5wJbBiDf\n8HOe85ggCIIgxDz+XGAXAehJRIVElATgcgBTAjMtQRAEQYhsfH4Gq5SqI6LxAKYDiAfwplKqJGAz\nEwRBEIQIxp8gJyilpgGYFqC5CIIgCELUIJmcBEEQBMEG/LqDFQQ7SOjKsXO7zszT+mB3brP2hgkA\ngHrVoI+dVfI7rdOuP6p1Xdl2O6YpCILgErmDFQRBEAQbkAusIAiCINiAuIiFsKP+LXb7zu39smmb\nWpMMn6mJ7BZGoizt5hy5lBOtbT+ds7ydOmiV1j/P79fi+Dtdf3R77ms3n671zsEH/ZqnIEQLfr0L\nEVEpgEoA9QDqlFJFgZiUIAiCIEQ6gfiYf4ZSak8AziMIgiAIUYP40YSgQgm85DY+NlDrT0Y9r3Xv\nxHhDD/MwgU8OtQMAPL3uHH2sw5XlWtdXVv5/e+cdXlWV/f3vSichgQRCIJQESEJRkCowgA0FRRGs\n2LG9qIA/rCPqOPaRURlsiGIZcEYdFHF0FFHELlJC7z10CL2FhOTe/f5xb/Y6kHNL7r3n1vV5njx8\nc+7eZ29Wds7JWWfttfydakThyv27cehbhlZLPJ/IC3ewGUY38gB08ukcghBt+BvkpAB8R0QLndUa\nBEEQBEGA/0+wfZRSO4ioEYBZRLRGKXXKn8Cnl0kSAovY13rExtYi9rUesXFo8DdV4g7nv6VE9DmA\nswH8clqbSQAmAUAGZflW3d0Ljl7XU+vUPRxNGv/zUm5kt1k1fMgIln39oeJSdgVvuYqnuG6AMUI4\n0eN52k4dqXXezCoAQMPvivUxe40egSFcbZzzR4bWH+S9HcKZAH1H3qV1KubVqm+42jeaiBYbJzRr\nqrWtcabW+zumaz1wNN+CeqetAwCMu+E6Psn85RbO8FR8dhETURoRpVdrAP0BrAjUxARBEAQhkvHn\nCTYHwOdEVH2ej5RSMwMyKy+xnddF68l/H6d164Q6Wl926U1a25fwfr9gkdCksdYbXmmkdcux/DSt\nFkdfEaK4TryfcusA/jtu3YA3PfbtPO8WrZs/xXYqXL1Ia1V5ErHIqU+tvgUk+UPrqXdrXXD/XK1r\n+9QaEoiDv0r/20brxd3/o/Uv5dx8W2UDrW9M36/1Xdt7cfstBVpX7GLXa/3VvOaz32I7QUXsw2PA\nsJ/bWesjeSla19tQpvWG4Xxr6tRqq9bX5szR+pq6/DPxROVHn2r9xtWcUtXqe4I/5eo2ATgrgHMR\nBEEQhKhBUiUKgiAIggVE9D7YXb3YvWB0CxvZcUF9rZt4sQ0w0GwY1VLrVX0maN3ruaFaZ14a1ClZ\nRvllZ2v96CtTtO5Xp8ysOV45WKT1xNm8n7XoIXYF22PUFWzEuMfVimAmY5pDY6pEowtYH0PNY2GJ\n0x28bzgHPx4+94TWa7u9r7XN4LXtnWw4R/Je0zZvNv2dvzFqF3RoMErr5n8zuNKjMOjyFOJ4P3vF\nAH6d1/15Dkwcm7NQ65Iqvk7kJ3iOdLbD3N0eB6px7GLDNejBK+tpnWfxPUGeYAVBEATBAuQGKwiC\nIAgWEJEu4n13OaL4vrnnRcNRdikYC283fYt9AFbtkzSjsr+j7sFPN79kOMpzvDl/vtZfIRORSkJ+\nC623GlzdrtzCdsNPYeKic7UuHM2uR4mzPJVfJ/juFvbO/cvVbyLGBeyB0nsc14jix82rMVWB3bOr\nT5pfGe5bz69xXimcqvXHh9hln5VwXOt7M3k3QDLxpXX5PTyHThXsLm72pmOPvv2EIXQ5itzGOx9i\nOy0Zbf5zMOLKLbyuku0z+eCftP7xVY7mthu20e/vyjbcMMiYKtRB8oGaLmSrkCdYQRAEQbAAjzdY\nInqfiEqJaIXhWBYRzSKi9c5/I/cRTBAEQRAswBsX8WQAbwD4wHBsDIDZSqmxRDTG+f0jgZ8eE5/D\nSRquGPkjAKBpvLlLIfVe/m/ZysxdlVZz4v6DAIBGLuY4+bWBWjfEH0GZU6Awpj58+nV2wXRNNmsN\n/ONAW63fmsdu4aI7i82aCwA2jO9p+K52oY6npC38nKNWo8X96xJD1OrxvsdrfFyhqrQ+40t21RaN\nmF+jLQAko0TrR9DDtA2QptXbb9yr9forJpq2Hn7r11ovuao5AGDRv/ncjd6YU6NPJFHyLLttp988\nzvCJi4uDgWOqQusuP47QuuB1/rkZ0xymDajUOq6SXyyVZ9Ucq+AbrkVT9FrwkqJ4fIJ1Ju8/cNrh\nwQCq92FMATAkwPMSBEEQhIjG1yCnHKVUdfHN3XCkTTRFqjhYi9jXesTG1iL2tR6xcWjwO4pYKaWI\nyGXgpz9VHOLSuUJC0Qx+iH60gSN/pHGjcbuf7uS2B7bXZpiAkdAyT+t7Wv5c43Nj5GLjLzYZjvtO\nsKpkGAulGyviuHILG6OFI90tHGmVSIxu4UggUPaNz+QEAqvP+WeNz8+czXmUXbmF/aHtxENaz76Y\nfzH61WHX54j6m7mDU7969059aNb7nLvcHsDXW1au4U0fddL6xz68a6KJi9djxutgl7m3ap36FefZ\nLvin+WuzK1Zx8o8r01/V+pV97Jpe3IfvG4OmXw0AaLOVXcsqiJHavkYR7yGiJgDg/Lc0cFMSBEEQ\nhMjH1xvslwCGOfUwAF8EZjqCIAiCEB14dBET0ccAzgPQkIi2A3gSwFgAnxDRHQC2ALjWislRM3aX\njGs8tcbn5y2/RuvWNy7WOlRbtTfcnqv1jek1H+rbfnOP1kW7I8tVuvFvHDl8aqF0c3oW36x1JLqF\nQ03uLwYv3lDX7cwwRiCb5ROOVnbe0Nbw3fcAgDWV7J5tM54TFliRdMa2cq3WI+Zxmcy1573ntt/o\nzA1af5/Mr5kQoh0QtWVst+lau3ILH7Gz7Qc88aDWzSazKziheTOtywZ003p3ryStr8/gyOS6xPnn\nn23EkfaD4s/T2raeX8WFAo83WKXU9S4+6hfguQiCIAhC1CCZnARBEATBAsI6F3H/aQvcfn7sW3Yh\nZ6Tt0dp+vOYmc6ugZI4WbN1nS43PjSWY2r3EUYaRlnH0s2vHG75LNG1jdNnn3s3/V3+ipGMVYyRw\n63M4+nXj0Jq5VU/H2OaWnpyLeE+vI2bNo4bDZ9RcaccVX+Lidu/XOlAu4vgGWVqXXtlG6xl9Xja0\nMi+lWU2HP27RusWxdQGaWXjxt729ta4ymGPjy/w647lB/BowP3Gf1t2TjbmDPSesCCfkCVYQBEEQ\nLEBusIIgCIJgAWHtIv50Wxet761fMxps0UMczXrnUE5msP14c60rbZyfVL3G+YxdkbKPo92qUhMN\nms9jM7gscu/nCMCPW35V43w3rRqmdcbaDTU+jyZ2rWX71t1lHr1a1a+r1iey2b65I9k2jVOOar1o\nXGetkw+xYz1ppvvXB9GEMRLY6PL9IO8Xj31PacP5DFzmK45kkvfUvJx1TeLf28zpHFF84JaWWtMx\nfo1TtZtfNbnC3pfXZJvxugYK/tdkgqGVe7ewkZb3sju0qvKk1/0iibE5C/mbvyx03VBTu5Jyxijl\ncMLXajpPEdEOIlri/Bro7hyCIAiCEGt44yKeDOBik+PjlVKdnF8zAjstQRAEQYhsvNkH+wsR5Vs/\nlZqkP8GloPq/wAV7nnC6YvumcNTgu81r5v6twduem6w0uGiy4/j8rsrOCcw157Arc9q/Opu2Gd/z\nY60vST1q2uYUxnH5rjG7OdnFipk+TDAKMEYC972C3by/TvBicRswtr/lz+x23vxiO60jzXXc6rU1\n/M2dNT+fkveD1v/6incgbKloqPW8A/kex+lYj5ManOL6jFHmHC3QekhaaJLK9JzDkfb5R5aFZA5m\n+BPkNIqIljldyFJwXRAEQRAM+HqDnQigNYBOAHYBGOeqIRENJ6JiIiquRIWrZoKPiH2tR2xsLWJf\n6xEbhwZSynPlIqeL+Cul1Jm1+ex0MihL9SD/MyzGF7UGANjqs9t23e0ctVenoXkOz7QUdv/O7fwf\nPh/x3xk2Zfd4/Ll9/F/9Zkd7rX8/65MaY/ZZxskXMi7ZaDqv2jBPzcYRdcA0xC5Q9jXjpRJ2/7ZL\nNE80YTXH7Hxh6PL1fQCAtg+v1sfsR71wOXvB92raQqVUN7PPrLRxoMj5g8t+eRNp7IoBuZ08N/IB\nq9YwJXLO2rUTOwIANlwyyadzucP4GunqucO1zv6Mr0EPPM+vQoakcdIVI/1XO157JV7Kkcuqwv+b\nnzv7AoFfw8YynasfYtf7+iETAzaGJ9pNGal1y8fMS90FEk82rsanJ9jqUnVOrgCwwlVbQRAEQYhF\nfK2mcx4RdQKgAJQAuMvlCQRBEAQhBvG1mo77+ksWY1tX09VaNN9zP0rg/+7lzYa4aekedZhdkVmN\nDV6C72u2TZjU0PCd/y7iUDH4R3bBrOsfeLdbj4U3aF1Wzq6+5b0na103jvOQrhvkcD9dOuV2fYz+\nWBrweUUixkjj1uNrl8c4klEG122bkcsBAL2vHqGPNbs7MIletr/NUbMt/82vTk4MPlvrHimGrB4w\n34Gw/Y+mAID8iq0BmVeoqNrMOdiLRu/QetAzvLuTDK+V1t7HiYCMtHlzt9alr/E1wPg6zxW2FhGa\naEIQBEEQhNojN1hBEARBsICwzkUcaFQVJ46oKgmMW2b9+Famxz891gAAkL68VB+LtBJ1Rpr/l3O6\nor/v59lnO6H1sKHsdm68gV1qO24s5A5c5SqqKLuiBwBg5zn8iqF3z1Va+xP923rq3Z4bRTnV0bj1\nPmQX7tEPA3PuethnevxEA/4daeJFYpqC9x1rPprKORqvsbY9paZtWj+0w/S4rfMZWj/e5guPY22s\n4mtJ9jfhWcZOnmAFQRAEwQLkBisIgiAIFhBTLmJLiDNP1PHWFkf5vOQNm4M5G8tI28yRqX8p5ZJz\nzzWqXS7WFEPyji0DjW40jsycf5sxMVgSzLh7m8O+ibt5E3+4u9qq3cJA7XMH14ZojxYWopMNN6Vr\nPSj1iJuWDoZMeljr5h/OcdMydHhTrq45Ef1IRKuIaCURjXYezyKiWUS03vmv5CMWBEEQBCfeuIir\nADyolGoPoCeAkUTUHsAYALOVUoUAZju/FwRBEAQB3iWa2AVHQn8opY4S0WoATQEMhiPDEwBMAfAT\ngEcsmWWYYUxY0SDrmGmbvT/mAgBanJmij9lXrDFtGwnYl/HcP/2lp9a3DOa8n0WJ5u5cI8ZkEStu\nf8NFK/PzTDjUWutdwxw5T22bA5M8IBhY6Rb2h1u2uChXh8gqVydEHva+XNby/D7La9U3Zb/nPPqh\nplbvYJ2J/TsDmAcgx3nzBYDdAHJc9BkOYDgApLjIaCL4jtjXesTG1iL2tR6xcWjw+gZLRHUBfAbg\nPqXUESLev6eUUkRk+ueEUmoSgEmAo4qDf9MNEwyBOvXrnDBtsmyU4+mszznGajqBn0oo7Fs4mvcW\nPjzhJq07TV2v9dONFgd83Nd+HMBzWBu8p6tA2bjvSN8LpAca417Zgvv55xmKp9aovEa4oNeS6AZw\nVwAAHP9JREFUoVpnbd/ipmVgCVcb77qf01vObPar1q4qmVXnFwCAtFI+Hq54tU2HiBLhuLl+qJSa\n7jy8p7qqjvNf813FgiAIghCDeBNFTHAk91+tlPqH4aMvAQxz6mEAPKfeEARBEIQYwRsXcW8ANwNY\nTkRLnMceAzAWwCdEdAeALQCutWaK4Yexasf2nw2VIdqyXFDh8MIkTWCXRiRX03GFsbLRojs7aN3t\nnD5aFz/0eq3O+fReLvT93Xg+T5tpy7QOf+dQTVI/Z/drX2eFx5Z/5oLx/qRHdIUxgOn3ue21NrqF\nheBx6CgXZc80XEdiiYRmTbV+9IyZpm2MbmEj48Zep3XWdOsLq/uLN1HEvwFwVbm9X2CnIwiCIAjR\ngaRKFARBEAQLkFSJfpL3d04VeEb8KK0bz68EAKR87UUl+ChBLVypdWNDBsXLxnc1ae0dmWA3UCS6\nhV1R7S7e8zkfGwB2jW8Y3/P0Ll5zqvuXU84VQNzCoebC1uu0LsnIAADYjnhOCxhNbLo9T+vr6v7P\nY/snSvn3ouHUpVpHwvVAnmAFQRAEwQLkBisIgiAIFiAuYj+pLuwMAHlPhmdFByHykCjf6OT1XL5G\nXN7gCoeIMRexPZHzXKyp5Otn20ROo7quslzrn/7+J63TyyLr98KfajpPEdEOIlri/Bpo/XQFQRAE\nITLw5gm2uprOIiJKB7CQiGY5PxuvlHrZuukJgiAIQmTiTzWdWlOFSsxRM6Gg0BQtkU9tPXdy8pua\ngXgkgEAgxKEHmW/BXamKsQ+7kIRk9KL+AIBKdRLLMRcnUIY6SEUH9EQi1azYYtZ3o1qJndiMRDjc\nFwU4Ew0dGSI15aoMK7EAJ1EOgNAULdGCCj2O66qfN2OaEQz7urKTNzYOtn3d9Q22jcPZvu7sFI1r\n2KprRNznDvdvbreazrxtO6pw++hS7NlrAxFw500Z2H1RLqrKy7Bz079iaw3/Zab+v15lf1D36TVg\nOX5/9jfs+H0b7In10eL/HAXVbR+/G1Fr2Ig/1XR6AxhFRLcAKIbjKfegm77xhDj0Qn+kIBXzMRsN\nVS7qUobX43fFuUiiZLdtcpGH5miNlVigj5VgDbLQCPnUFiVqDUqwBoXo6FVfAGiBQuRRG5djEgiF\n6IgMykSVqsR8zEaWysEulLgd11U/b8asMYcg2Rfw3cbBtq+7vt6MW2Mefto4XO0LxNYatuoakX7F\nrYYjP5zyeUIC8OJfG6Bzx2QcPWZHj4t3IKHbbhxYuyCm13CSrnjqoOCyArS9pi1mPRyZa/h0vI4i\nPr2aDoCJAFoD6ATHE+44F/2GE1ExgBUEIJXqIo7ikIPm2IudPk/cFZmUjcTT6onuxU40gWPvVRPk\nuRzXrK83JFMdZFAmACCBEpGKdFTghMdxXfWrDcG2L+C7jYNtX3d9a0OkrGFf7QvE1hoOxTWiSU4C\nOnd03JjS68ahbUEiKo8fxuGSlbKGDeR0bozkDM83cDNCuYZd4dUTrFk1HaXUHsPn7wD4yqxvdZkk\nIro6HgmfVh9PQR0cxoFaTXYxfgUU0BSt0Ixaed3vJCqQTI4coElIwUlUeOhxKtuwEbvUVqQjE0Xo\naOo6quaEOo6jOIR6yKrVuMZ+h7DP6zHDwb6AfzYOhn1P7xsKG0eCfYHYXMOBuEYkfPwi0tNyUZR/\nMc5dfrX+7OcO07R+Zl8HHNpRhl+W7UL3kyex5cjhmF7Dxp0XO5/k/2MiypD/hCPJzEaE/xp2hccb\nrKtqOkTUxFBw/QoAK2o1ci3phvORQnVwUpVjEX5FmkpHJmXX+jxEBNSiGmIztEYrOJKkb8RKrMMy\nnIFupm2rVBWW4Q+0QSckUOIp47gb9/R+zZT3YwaKQNkXqJ2Ng2Ffs77BtnEk2BeQNQz4cY3o2AEb\nt/2AdSUzkY+LTNueLKvCtAfmo/+fz8TRv516sZY1bE4kr2FvXMTV1XQuOG1LzotEtJyIlgE4H8D9\nHs6zw25IblWOE0hGHTfNTyWl+i8QSkE2cnGkFn91JSEZFcrxyF+hTiAJ3rsgkikFRAQix8tvV+Pa\nlR3L8AcaowUaUVOvxzXr5+2YpxEy+wK+29hq+7rqG2wbh7t9gdhew4G5RsShaaOuOHJsh2m7ykqF\naQ/Mx5mXNkPbC3Md48bVkTXsgQhbw6fg8QarlPpNKUVKqY5KqU7OrxlKqZuVUh2cxy83PM26YoEd\ndpxQx2FXduzBNmTDu4gsm6pClarU+gD2IA31vOoLANnIxS5sAQDswhZkI9frvtU/GAAoxQ7URc1g\nAKUUVqEYaUhHHhV5Pa6rft6MaULI7Av4bmMr7euubzBtHO72BWQNB+QaMXcZShfPRt0TSUi7eJP+\nGti0Cy7J7YzWeTZUzqkHjGuHuWclwrb/ALJtObKGPRBha/gUSKla+EL8JJXSFQFQUMhFPlpSO6/6\nlaljWOZM+q6g0BjNXfZdrubhIPaiEhVIQgpaoT2ykYvlmItynHAbgm/W9yD24igOgUBIQSraoYv2\n51dzSO1DMX5CXcOCK8CZyECW23Fd9duNbS7HnKdm44g6YFo+MBj2dWUnb2wcbPu66+vOxt+raQuV\nUqa+IF9sHO72dWenaFzDkXSNcNfXlY3d2ReQNWz1GjYS1BtsBmUpd3unBM+4+8GKfQODuxus2Nh/\nZA1bi6eLv9jYf8LyBktEewEcB7DPx1M09LGvr/3Cccw8pZRpZIHTvlv8GDua7ORPP082ljXsX19Z\nw9aO6dK+gKzhAPV1a2ONUiqoXwCKg903VsaMxDlH2piROOdIGjMS5xxpY0binCNpTOOXlKsTBEEQ\nBAuQG6wgCIIgWEAobrCrQ9A3Vsb0t3+s2ClU9vWnb6yM6W//WLGTrOHwHVPj1w2WiC4morVEtIGI\nxnjZzbu4+8D2jZUx/e0fK3YKlX396RsrY/rbP1bsJGs4fMfU+HyDJaJ4ABMAXAKgPYDriai9vxMS\nBEEQhGjA5206RNQLwFNKqQHO7x8FAKXUC676JFGyikeCrq9XWypR4VNfX/uF45jlOI6TqsJ0/1US\nJasUpIXdnCNtzKM4uE+5CMGXNex/X1nD1o7pzr6ArOFA9PVk42pqVQ/2NJoC2Gb4fjuAHu46pCDN\nbZFewTPz1GyXn4l9A8P3atoWV5+Jjf1H1rC1uLMvIDYOBJ5sXI0/N1ivIKLhAIYDQApSrR4u5hD7\nWo/Y2FrEvtYjNg4N/gQ57QDQ3PB9M+exU1BKTVJKdVNKdfP1UV1wjdjXesTG1iL2tR6xcWjw5wa7\nAEAhEbUkoiQA1wH4MjDTEgRBEITIxmcXsVKqiohGAfgWQDyA95VSKwM2M0EQBEGIYPx6B6uUmgFg\nRoDmIgiCIAhRg6RKFARBEAQLkBusIAiCIFiA3GAFQRAEwQLkBisIgiAIFiA3WEEQBEGwALnBCoIg\nCIIF+LVNh4hKABwFYANQpZTqFohJCYIgCEKkE4hcxOcrpfYF4DwRT3z7Iq3P/2QhAOC+zHX6WNcF\nN2ndZIjftXwFIeDE5zTSevUzeQCAlp/a9bHE7xcGfU5C7JHQpLHWx7q10HrbNVVan1u0no+PKdQ6\n/qdFFs/Oe8RFLAiCIAgW4O8NVgH4jogWOqs11ICIhhNRMREVV6LCz+GE0xH7Wo/Y2FrEvtYjNg4N\n/rqI+yildhBRIwCziGiNUuoXYwOl1CQAkwAgg7J8q+4exsS3KdC6y0fs9v2/zDUAALuh7YgiNs0X\nZ52jtX2p7+7icLUvJSZpPWjpLq1H1ucSwq8fzNP6qzMygzMxHwhXG/tDXKf2Wq8blqH1xEHvad0h\n6SAAYOg3D+hjiRbMJRrtG25Ego33/79eWg9/4Autb8v4Wmv7KVdUpvt9zbRu8lPg5+Yrfj3BKqV2\nOP8tBfA5gLMDMSlBEARBiHR8foIlojQAcUqpo07dH8AzAZtZhLDhtmytP8/+2G3bfz9xmdZpS+dZ\nNqdwgOL5b7fh9Uq0rjT87dw+ZbvW3xR00dq2YbOlc4sljJ6Eg9d31fqjZ1/SukVCHa3jQFr3efgh\nAEDWgq36mM1wPlV5MrCT9ZN9d/ET0Ny/vgEASKR4faxS2bQ+u/hGrQ+Vpmvd7uWDpuemY2VaV+3Y\n6f9kYxhjIN3lP3IBtivTX9a6Xhyvs0mHW2n97puDtD7Smp9mk5sf05rCaI364yLOAfA5EVWf5yOl\n1MyAzEoQBEEQIhx/6sFuAnBWAOciCIIgCFFDIPbBxhxxaWlaF/Uscdt2WMkArdNnrtDa/FV99GCv\n4EjFs/4YpvXSXlO0fnTNlVpnbuA9bULg2DCFg5lWn/uG4ZM6NRufxi8vTahx7LYt/bTe/jz/fZ38\n9QLfJhhA6uzj36qD9nIAQGZcij5mDJCZ2+1fpueIG8ivNoztvynjILw3h12lNc1Z6seMo5v4Qnbt\nHniV7Tqu7Sdab6tsoPX0o5xHYMLkwVrnvjhH60ZgfeyjTlov7vVPrS/vegdPYu4yX6YeMGQfrCAI\ngiBYgNxgBUEQBMECYspFTN07aF2Vxjv6vEmtZUzddfifqVrPLpxq2n6PzeEi3fDPNvpYg+N/eD/Z\nSEdxuLDNZv53XNkvHIGdCXER+0PFpd21/umdd7S2KePaJnii/W+3ap36a10AwEkOssUnd43T+vnH\n+Hz7eatiyEj7jCPz+7V6GACw6L7XA3LuS1I5ujj1g4+0HldwRkDOH41suYavmUvO4p+D0fU++vnr\ntM6ZwbsHcnexK9jIgds4Unxmb46Ef2T3eVrHLdtgGCu0yBOsIAiCIFiAxxssEb1PRKVEtMJwLIuI\nZhHReue/4ZuGRxAEQRBCgDcu4skA3gDwgeHYGACzlVJjiWiM8/tHAj8934hLZ59WVRdOZXjdW99o\n3TjxkNav3XQtd3YRdXasK1d0mN2hZnTl6Vz0L4eLKv/dGHIL1xJbiuc2gmviUvlVRfdni7W2KXaM\nlVRxgoSLfx+ldaP/svHr/1qidf6umut/+2N/0rqZ4Yoxb1O+1gUwT9AQKnJfcrgYB39xrennG2/h\nZAcns7lCi9GL/kifGVrfZkiWcm4dtunfL+DkHQk/SKUhI8pgS2PCj4KPRmrd+j2+Php+CqdQPogT\nBM55jiPhKxQvxvkvcqXU9LK5vkzXEjw+wTpzCx847fBgANX7LaYAGBLgeQmCIAhCROPrO9gcpVR1\nBvfdcGR1EgRBEATBid9RxEopRUQuqzM4y9gNB4AUpLpqFlA2vN1a6xXnTjJt8+xezn0bf+C41jaz\nxgC2DqpdAYrMVcEpWBEK+waKF27itw5P779F60ZvmEcQhopwtbG9jF2V//2eoyv39OVXJHvvytW6\n1bIlpudx5Zqr5oGbp2udSpzntdGMZG+n6hYr7Wtbt9H0eP5fzI8b+Rwc5f7O8Mu1nvskuykfmPSh\n1q8VtPVlikEhFGvYeFcw5oF+/8q3tH56BieFMLrYjdWeHh3PyWmMEcgX/fk+rTOmho9b2IivT7B7\niKgJADj/LXXVUCk1SSnVTSnVLRGB+YUUGLGv9YiNrUXsaz1i49Dg6w32SwDV+e+GAfjCTVtBEARB\niDk8uoiJ6GMA5wFoSETbATwJYCyAT4joDgBbAJiH6llMXApHQm79kN3Ci3u8ZWgVDzM+m95X6xbr\nzF2S1JU3kY+/wH0pOgBo/8m9Whd+4tjgH5aVjcOE3ASOPCWbWKrWxPHaLnxhjdZ7/nzE0Mioa8fJ\nix3JKwamjdfH1lXymBkbj9foE63U2V8zzzEAnJV0QutqewFA0szQ52YONS1eX671HYPO1/qdFrO1\nfv5dfoU3ZuTdWu88h29NfVIOa91x8v1a5/9nfuAmaxEeb7BKqetdfNTPxXFBEARBiHkkk5MgCIIg\nWEBE5yJeN5bLFa3qacw5ym6sacc4H+ZfZ1yjdeELvDHflXPyhg+/1dqYi9RIl7m3al30OG/Sp6KW\nAICq+lwWLG4el6tTVZ5iN6OfOWWFWmdPlIQcrjj4NdupYSq7ZVMTTmp9Z5NftX7ukVu1Nubn9YbK\nCzlxwvR3XgUA7LVzxoAR947WOmV++LvoAoXRjj0uYjflukETtT7anC+nXIQtdrEfPar1vkFske7v\n8o6BH7q+q/W3775pep7rNw7SOv/xyLpOyBOsIAiCIFiA3GAFQRAEwQIi0kVcvQn5kQFfmn7+0n4u\nSzdnKOuC1bwZ2ZuY1evT92jtquzR4FYcKTf1bXavvdDDsTn/1U0cC5YxhM0tLuLYJr5+PQDAwYHt\n9LEj+fz37spR7C47teScZy5+/W2tW11wl9aFI9nNaYzAP3Qlv2ppOXKt1hlxjjYXLLpBH2v0v9hx\nC7sieU9EXjZDim3ffq2bDGE96nd2/07J/xZmLN3aTOsC7LVgdtYhT7CCIAiCYAG+lqt7ioh2ENES\n59dAa6cpCIIgCJGFr+XqAGC8UurlgM+oFsQbHLdxhr8VHmmwUus2Y7jUVp01rL+/50WtG8ZzpK8R\nY4mlShc+5acbLdb6s629tX7vZkcUcV1s0sdcuZkFICGvudZVW7aFcCbBoWSUI4nJ0ns4+n1hBX/e\nZxlHvCe+whGYqUs92+bLYi6ztnjwK1r/afuDWicZ8k/Mf4znEGeo11Y4bQQAoOhBzxH3sUTTn/kH\nFXcn26vncHblr38nqFOKWFZ+wfmb40bPMm3zeR+O1B49gEsuJn1bbNY8rPC1XJ0gCIIgCG7w5x3s\nKCJa5nQhZ7pqRETDiaiYiIorUeGqmeAjYl/rERtbi9jXesTGocHXcLiJAJ6Fw2P0LIBxAG43a6iU\nmgRgEgBkUFZAPEz2JasAAH+byXXeb7r2ddO2qy/kiEpcaPyEK0rYXThvjW5hYxtjqbv5I1i3XMgu\nomC50qywb0iwh+/UA2XjjS/31HrBUMfbla/LGulj71x0gdZpJfxqAYbXDN7Env99P0cmP9xglda/\njeA3OuXKuOb5Fck594/Qumi6wwVndcR7pK1hY1k1u+E3PTf5kNabGjTV2rY/9A7AcLXxW3dz6b8/\nKviV3N2LbtJ6aS8uV/f0RE5MMbZXf61te1wWdAspPj3BKqX2KKVsSik7gHcAnB3YaQmCIAhCZOPT\nDba6FqyTKwCscNVWEARBEGIRX8vVnUdEneDwhJYAuMvlCSyk6NElWndqxh7qqd3NQ/iy49jVlRVf\nu6LDkw4VaL3oBnbB0aqlWoeN3yVCmPw27+7K2WZeMjDSMZY8fHnwv7W+Y/NgAED53Ry+YCtZV7uT\nG8rVJTTJ0frCdGN5Zv4bum4cr/lfy9K1fmUkJ5Ko+13tkrEIzIMN+DljVvdztE6aGXoXcTgRl5am\ndSLZtB75D44QzpvMCXw6jeIyoItGvar14Sl1tc648pjW9rKywE3WT3wtV/eeBXMRBEEQhKhBMjkJ\ngiAIggVEdFJNe3m51i2uYZfCw+hp1hzrJ/TQevWQN0zbuOK1r9id2WpVZJVMCgWUmKT1293YNfrW\noVZaN36T89pGqzsybyJHADeOP6z1kb86EmvEr/KcZzihGUek7hySp/XhduxeWzuEcxfHGco1GqNc\nN1fx78vEqzlKM3Fp+G/YD1eeLO3MuhFHF5d2SdS62cygTinsKb2xo9Zp9L3Wud9y7nebodRd3juc\nH7t7D/NSd30ffljrFk+Hz+smeYIVBEEQBAuQG6wgCIIgWEBEu4it5szJHNXW+pngJ5GIZCie/3Y7\nM4ndPelx67X+mnIQ7Ryt5LJw3ZM5b21pV8fxtFzz1xnHr2V3stEVlhlnnjf7sVIulfjlF5xz+9c7\nXtI6hXjlqkR2Iwu+M/VntvXT13Be8tx+nDOaXuLXJaryZHAmFiHst/N6tq3baNrGVam77u+P1vqH\n2zm3/EV12V3c+uHQvs7zpppOcyL6kYhWEdFKIhrtPJ5FRLOIaL3zX5fpEgVBEAQh1vDGRVwF4EGl\nVHsAPQGMJKL2AMYAmK2UKgQw2/m9IAiCIAjwbh/sLgC7nPooEa0G0BTAYDgSUADAFAA/AXjEkln6\ngTGaFemVteqb/zi7F8QtXDv2Xc/RlQOXcSm6hg9xG1W5HtFO8c9cjsuezxGTCx8wz51dzTqDK/GZ\nPedpvb2sPrf5ulDrZq9wBGurRlu1PnAbn5PjWoVA0Xb8Dq0XD+b8zl+1na71JeferXXi9/xzEoAM\n4sID8Q25LKPRLeyK9o9v17r8An5WXH0D7xC57OGuCCW1CnIionwAnQHMA5DjvPkCwG4A0f9CTRAE\nQRC8xOsgJyKqC+AzAPcppY4QccCGUkoRkelDHhENBzAcAFKQ6t9sfSCugPcNru73tpuWDi5YPlRr\nY7H0cCXU9nXFsea8PhJ/yNbatjp89qh5iz82LniRq9lc/snN3o957ITWtg2bDZ/s1aqpQRt/+ZRh\nD2Gl4r+hs+O5la0OP8+GeitBuK5hb6jawsFMo1ZxyslfO32kNY3hnxPYiRFUwsnGjb9hm8U9xmty\nw0NFWrcc4zk4yX6E1/mEvedrPS73N3+nGDC8+t0iokQ4bq4fKqWqfR97qpP+O/81rReklJqklOqm\nlOqWiNrl/xU8I/a1HrGxtYh9rUdsHBq8iSImOHIPr1ZK/cPw0ZcAhjn1MABfnN5XEARBEGIVb1zE\nvQHcDGA5EVWXr3kMwFgAnxDRHQC2ALjWmin6x4FxnsOTVp/k4AR6N9vwSfi7iMOV8lyuXJS4Pna3\nW9sO8X5WLDnsumEgyeZgkcu++z+tqZz/nm67giv3cMJFwR8OLWvI33RiOaPdZ1pfju5BnFF4UrWN\ng5MGz+D1ueDGl7Ue1XcQH5/PruPU/CNan5WzU+txuT9pfceWiwyjHfJ3un7hTRTxbwDIxcf9Ajsd\nQRAEQYgOQh3fIAiCIAhRSdT77vYdquuxzZjNV2qdNm2eldOJGR7o+63W766+NIQziT1s6/nVRtFw\n89cc4hYOPAXv7db6q6vYTX952kGt4xtkaW3bL4XYiz7gCk89yh/UetVQ3icelz9Lazv4dZ6R1w7y\nfvMDl4dPGlB5ghUEQRAEC5AbrCAIgiBYQNS7iIvu5bRxV312udafFX6pdfm4XK2TwanPhNoRn80R\n2Klx7C5r/GrkJZcQhNpiTAjyj00Xan1Zh6laZ/BlBwd7B2Va4c3cZVq2WVNP6/Zx92qtMjlt6Ou9\nOYHH/cW8caXRp1yVJ21f+LzmkydYQRAEQbAAf8rVPUVEO4hoifNroPXTFQRBEITIwBsXcXW5ukVE\nlA5gIRFVh3WNV0q97KZvyDFG6tnO4+PGDd/JWBDEGUUva17mqjnbT4qrXYhdyqcbap90YPlGi/9p\nPeSqBwAAaZ+Fj0szlBiTshTcN9e0zWvgaOGWWGbaJpzwp1ydIAiCIAgu8KdcHQCMIqJlRPQ+EWW6\n6DOciIqJqLgSFWZNBD8Q+1qP2NhaxL7WIzYODf6Uq5sI4Fk4KmU9C2AcgNtP76eUmgRgEgBkUJbU\nLQ8w4WTfwmGLtJ6DJDctI4twsnE0Eo32zfnPSq0vG8qJbN4q+FjrOqUnESyi0caRgM/l6pRSe5RS\nNqWUHcA7AM62bpqCIAiCEFn4XK6uuhaskysArAj89ARBEAQhMvGnXN31RNQJDhdxCYC7LJmhIAhC\nhGE7wmXV4vqxHoE+fByLgzonIfj4U65uRuCnIwiCIAjRgWRyEgRBEAQLIKWCF1BGRHsBHAewz8dT\nNPSxr6/9wnHMPKVUttkHTvtu8WPsaLKTP/082VjWsH99ZQ1bO6ZL+wKyhgPU162NNUqpoH4BKA52\n31gZMxLnHGljRuKcI2nMSJxzpI0ZiXOOpDGNX+IiFgRBEAQLkBusIAiCIFhAKG6wk0LQN1bG9Ld/\nrNgpVPb1p2+sjOlv/1ixk6zh8B1TE9QgJ0EQBEGIFcRFLAiCIAgWIDdYQRAEQbAAucEKgiAIggXI\nDVYQBEEQLEBusIIgCIJgAf8fuvlJT1HSoskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40a556a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6.  5.  1.  4.  1.]\n",
      " [ 8.  9.  4.  8.  6.]\n",
      " [ 3.  7.  2.  7.  8.]\n",
      " [ 4.  6.  5.  9.  9.]\n",
      " [ 4.  1.  2.  7.  8.]]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "from PIL import Image\n",
    "import pprint\n",
    "import numpy \n",
    "\n",
    "num_of_samples = 5\n",
    "\n",
    "fig = plt.figure(1,(8., 8.))\n",
    "grid = ImageGrid(fig, 111,  # similar to subplot(111)\n",
    "                 nrows_ncols=(num_of_samples, num_of_samples),  # creates 2x2 grid of axes\n",
    "                 axes_pad=0.1,  # pad between axes in inch.\n",
    "                 )\n",
    "\n",
    "output = numpy.zeros(num_of_samples ** 2)\n",
    "for i, (data, target) in enumerate(test_loader):\n",
    "    if i < 1:\n",
    "        for j in range(num_of_samples ** 2):\n",
    "            grid[j].matshow(Image.fromarray(data[j][0].numpy()))\n",
    "            output[j] = target[j]\n",
    "            \n",
    "    else:\n",
    "        break\n",
    "            \n",
    "output = output.reshape(num_of_samples,num_of_samples)\n",
    "plt.show()\n",
    "print(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model0(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model0, self).__init__()\n",
    "        self.output_layer = nn.Linear(28*28, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model1(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model1, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 5)\n",
    "        self.output_layer = nn.Linear(5, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model2(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model2, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 6)\n",
    "        self.output_layer = nn.Linear(6, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model3(nn.Module):    \n",
    "    def __init__(self):\n",
    "        super(Model3, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 7)\n",
    "        self.output_layer = nn.Linear(7, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model4(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model4, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 8)\n",
    "        self.output_layer = nn.Linear(8, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "\n",
    "class Model5(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model5, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 9)\n",
    "        self.output_layer = nn.Linear(9, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model6(nn.Module):    \n",
    "    def __init__(self):\n",
    "        super(Model6, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 10)\n",
    "        self.output_layer = nn.Linear(10, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model7(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model7, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "\n",
    "class Model8(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model8, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "        self.hidden_layer = nn.Linear(100, 100)\n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "\n",
    "class Model9(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model9, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "        self.hidden_layer = nn.Linear(100, 100)\n",
    "        self.hidden_layer1 = nn.Linear(100, 100)\n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.hidden_layer1(x)\n",
    "        x = self.output_layer(x)        \n",
    "        return F.log_softmax(x)\n",
    "    \n",
    "class Model10(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model10, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "        self.hidden_layer = nn.Linear(100, 100)\n",
    "        self.hidden_layer1 = nn.Linear(100, 100)\n",
    "        self.hidden_layer2 = nn.Linear(100, 100)\n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.hidden_layer1(x)\n",
    "        x = self.hidden_layer2(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)\n",
    "    \n",
    "\n",
    "class Model11(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Model11, self).__init__()\n",
    "        self.input_layer = nn.Linear(28*28, 100)\n",
    "        self.hidden_layer = nn.Linear(100, 100)\n",
    "        self.hidden_layer1 = nn.Linear(100, 100)\n",
    "        self.hidden_layer2 = nn.Linear(100, 100)\n",
    "        self.hidden_layer3 = nn.Linear(100, 100)\n",
    "        self.output_layer = nn.Linear(100, 10)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.input_layer(x)\n",
    "        x = self.hidden_layer(x)\n",
    "        x = self.hidden_layer1(x)\n",
    "        x = self.hidden_layer2(x)\n",
    "        x = self.hidden_layer3(x)\n",
    "        x = self.output_layer(x)\n",
    "        return F.log_softmax(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models = Model0(), Model1(), Model2(), Model3(), Model4(), Model5(), Model6(), Model7(), Model8(), Model9(), Model10(), Model11()\n",
    "if args.cuda:\n",
    "    for model in models:\n",
    "        model.cuda()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train(epoch, model, print_every=100, file_path='ckpt.pth'):\n",
    "    optimizer = optim.SGD(model.parameters(),\n",
    "           lr=args.lr, momentum=args.momentum)\n",
    "    losses = []\n",
    "    for i in range(epoch + 1):\n",
    "        model.train()\n",
    "        for batch_idx, (data, target) in enumerate(train_loader):\n",
    "            if args.cuda:\n",
    "                data, target = data.cuda(), target.cuda()\n",
    "            \n",
    "            data = data.view(args.batch_size , -1)\n",
    "            data, target = Variable(data), Variable(target)\n",
    "            optimizer.zero_grad()\n",
    "            output = model(data)\n",
    "            \n",
    "            loss = F.nll_loss(output, target)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "        losses.append(loss.data[0])\n",
    "        torch.save(model.state_dict(), file_path)        \n",
    "        if i % print_every == 0:\n",
    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
    "                    i, batch_idx * len(data), len(train_loader.dataset),\n",
    "                    100. * batch_idx / len(train_loader), loss.data[0]))\n",
    "    return losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Epoch: 0 [59800/60000 (100%)]\tLoss: 0.902932\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Process Process-4:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/multiprocessing/process.py\", line 249, in _bootstrap\n",
      "    self.run()\n",
      "  File \"/usr/lib/python3.5/multiprocessing/process.py\", line 93, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/home/paarulakan/environments/python/pytorch-py35/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 34, in _worker_loop\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/paarulakan/environments/python/pytorch-py35/lib/python3.5/site-packages/torch/utils/data/dataloader.py\", line 34, in <listcomp>\n",
      "    samples = collate_fn([dataset[i] for i in batch_indices])\n",
      "  File \"/home/paarulakan/environments/python/pytorch-py35/lib/python3.5/site-packages/torchvision/datasets/mnist.py\", line 55, in __getitem__\n",
      "    img = self.transform(img)\n",
      "  File \"/home/paarulakan/environments/python/pytorch-py35/lib/python3.5/site-packages/torchvision/transforms.py\", line 29, in __call__\n",
      "    img = t(img)\n",
      "  File \"/home/paarulakan/environments/python/pytorch-py35/lib/python3.5/site-packages/torchvision/transforms.py\", line 63, in __call__\n",
      "    return img.float().div(255)\n",
      "  File \"/home/paarulakan/environments/python/pytorch-py35/lib/python3.5/site-packages/torch/tensor.py\", line 39, in float\n",
      "    return self.type(type(self).__module__ + '.FloatTensor')\n",
      "  File \"/home/paarulakan/environments/python/pytorch-py35/lib/python3.5/site-packages/torch/_utils.py\", line 34, in _type\n",
      "    return new_type(self.size()).copy_(self, async)\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-be0cedd4ae7a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel_losses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbasedir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'model{}.pth'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mmodel_losses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-fa98aa62fa81>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch, model, print_every, file_path)\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/paarulakan/environments/python/pytorch-py35/lib/python3.5/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m             \u001b[0;32massert\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshutdown\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m             \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_queue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatches_outstanding\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0midx\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcvd_idx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/queue.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    162\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m                 \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_qsize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnot_empty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'timeout' must be a non-negative number\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.5/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    291\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    294\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model_losses = []\n",
    "for i, model in enumerate(models): \n",
    "    losses = train(1000, model, file_path=os.path.join(basedir,'model{}.pth'.format(i)))\n",
    "    model_losses.append(losses)\n",
    "    \n",
    "for losses in model_losses:\n",
    "    plt.plot(range(len(losses)), losses)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i, model in enumerate(models):\n",
    "    torch.save(model.state_dict(), \n",
    "               os.path.join(basedir, 'model{}.pth'.format(i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = Model0(), Model1(), Model2(), Model3(), Model4(), Model5(), Model6(), Model7(), Model8(), Model9(), Model10()\n",
    "if args.cuda:\n",
    "    for model in models:\n",
    "        model.cuda()\n",
    "        \n",
    "for i, model in enumerate(models):\n",
    "    model.load_state_dict(\n",
    "        torch.load(os.path.join(basedir, 'model{}.pth'.format(i))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_tuts(model):\n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    for data, target in test_loader:\n",
    "        if args.cuda:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        \n",
    "        data = data.view(data.size()[0], -1)\n",
    "        data, target = Variable(data, volatile=True), Variable(target)\n",
    "        output = model(data)\n",
    "        test_loss += F.nll_loss(output, target).data[0]\n",
    "        pred = output.data.max(1)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data).cpu().sum()\n",
    "\n",
    "    test_loss = test_loss\n",
    "    test_loss /= len(test_loader) # loss function already averages over batch size\n",
    "    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n",
    "        test_loss, correct, len(test_loader.dataset),\n",
    "        100. * correct / len(test_loader.dataset)))\n",
    "    \n",
    "    return 100. * correct / len(test_loader.dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test set: Average loss: 0.2663, Accuracy: 9253/10000 (93%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.3568, Accuracy: 9030/10000 (90%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.3244, Accuracy: 9119/10000 (91%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.2928, Accuracy: 9216/10000 (92%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.2816, Accuracy: 9236/10000 (92%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.2654, Accuracy: 9257/10000 (93%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.2658, Accuracy: 9257/10000 (93%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.2662, Accuracy: 9250/10000 (92%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.2677, Accuracy: 9259/10000 (93%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.2715, Accuracy: 9269/10000 (93%)\n",
      "\n",
      "\n",
      "Test set: Average loss: 0.2720, Accuracy: 9265/10000 (93%)\n",
      "\n",
      "[92.53, 90.3, 91.19, 92.16, 92.36, 92.57, 92.57, 92.5, 92.59, 92.69, 92.65]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "from PIL import Image\n",
    "import pprint\n",
    "import numpy \n",
    "\n",
    "\n",
    "accuracy = []\n",
    "for model in models:\n",
    "    accuracy.append(test_tuts(model))\n",
    "    \n",
    "pprint.pprint(accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD8CAYAAACb4nSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40a54c2198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40a54c2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(accuracy)), accuracy, linewidth=1.0)\n",
    "plt.axis([0, 10, 0, 100])\n",
    "plt.show()\n",
    "\n",
    "plt.plot(range(len(accuracy)), accuracy, linewidth=1.0)\n",
    "plt.axis([0, 10, 90, 93])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdgAAAHVCAYAAABSR+pHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwQL+H8zJSqVQFgckAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f40bc3ecb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8.  8.  3.  7.  8.]\n",
      " [ 2.  7.  3.  1.  4.]\n",
      " [ 9.  2.  9.  4.  1.]\n",
      " [ 8.  5.  5.  9.  5.]\n",
      " [ 1.  2.  1.  0.  7.]]\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "from PIL import Image\n",
    "import pprint\n",
    "import numpy \n",
    "\n",
    "num_of_samples = 5\n",
    "\n",
    "fig = plt.figure(1,(8., 8.))\n",
    "grid = ImageGrid(fig, 111,  # similar to subplot(111)\n",
    "                 nrows_ncols=(num_of_samples, num_of_samples),  # creates 2x2 grid of axes\n",
    "                 axes_pad=0.1,  # pad between axes in inch.\n",
    "                 )\n",
    "\n",
    "output = numpy.zeros(num_of_samples ** 2)\n",
    "for i, (data, target) in enumerate(test_loader):\n",
    "    if i < 1:\n",
    "        \n",
    "        data1 = data.cuda()\n",
    "        data1 = data1.view(data.size()[0], -1)\n",
    "        out = models[9](Variable(data1))\n",
    "        #data = data.view(data.size()[0], -1)\n",
    "        for j in range(num_of_samples ** 2):\n",
    "            \n",
    "            grid[j].matshow(Image.fromarray(data[j][0].numpy()))\n",
    "            output[j] = out.data.max(1)[1][j].cpu().numpy()[0]\n",
    "            \n",
    "    else:\n",
    "        break\n",
    "            \n",
    "output = output.reshape(num_of_samples,num_of_samples)\n",
    "plt.show()\n",
    "print(output)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}