Tutorial keras mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = (7,7) # Make the figures a bit bigger\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train original shape (60000, 28, 28)\n",
      "y_train original shape (60000,)\n"
     ]
    }
   ],
   "source": [
    "nb_classes = 10\n",
    "\n",
    "# the data, shuffled and split between tran and test sets\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "print(\"X_train original shape\", X_train.shape)\n",
    "print(\"y_train original shape\", y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(9):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.imshow(X_train[i], cmap='gray', interpolation='none')\n",
    "    plt.title(\"Class {}\".format(y_train[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training matrix shape (60000, 784)\n",
      "Testing matrix shape (10000, 784)\n"
     ]
    }
   ],
   "source": [
    "X_train = X_train.reshape(60000, 784)\n",
    "X_test = X_test.reshape(10000, 784)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "print(\"Training matrix shape\", X_train.shape)\n",
    "print(\"Testing matrix shape\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
    "Y_test = np_utils.to_categorical(y_test, nb_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Microtc\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\Microtc\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(512, input_shape=(784,)))\n",
    "model.add(Activation('relu')) # An \"activation\" is just a non-linear function applied to the output\n",
    "                              # of the layer above. Here, with a \"rectified linear unit\",\n",
    "                              # we clamp all values below 0 to 0.\n",
    "                           \n",
    "model.add(Dropout(0.2))   # Dropout helps protect the model from memorizing or \"overfitting\" the training data\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax')) # This special \"softmax\" activation among other things,\n",
    "                                 # ensures the output is a valid probaility distribution, that is\n",
    "                                 # that its values are all non-negative and sum to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 6s 96us/step - loss: 0.0217 - acc: 0.9933 - val_loss: 0.0734 - val_acc: 0.9838\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 5s 89us/step - loss: 0.0185 - acc: 0.9939 - val_loss: 0.0757 - val_acc: 0.9833\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 5s 87us/step - loss: 0.0163 - acc: 0.9947 - val_loss: 0.0681 - val_acc: 0.9843\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 5s 87us/step - loss: 0.0149 - acc: 0.9950 - val_loss: 0.0883 - val_acc: 0.9820\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 5s 90us/step - loss: 0.0141 - acc: 0.9953 - val_loss: 0.0749 - val_acc: 0.9857\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 5s 88us/step - loss: 0.0160 - acc: 0.9949 - val_loss: 0.0862 - val_acc: 0.9832\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 5s 87us/step - loss: 0.0153 - acc: 0.9957 - val_loss: 0.0749 - val_acc: 0.9841\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 5s 90us/step - loss: 0.0157 - acc: 0.9950 - val_loss: 0.0774 - val_acc: 0.9830\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 5s 88us/step - loss: 0.0132 - acc: 0.9954 - val_loss: 0.0815 - val_acc: 0.9844\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 5s 87us/step - loss: 0.0140 - acc: 0.9952 - val_loss: 0.0775 - val_acc: 0.9843\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x213821b52b0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, Y_train,batch_size=128, epochs=10, verbose=1, validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.0775322222161035\n",
      "Test accuracy: 0.9843\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(X_test, Y_test, verbose=0)\n",
    "print('Test score:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The predict_classes function outputs the highest probability class\n",
    "# according to the trained classifier for each input example.\n",
    "predicted_classes = model.predict_classes(X_test)\n",
    "\n",
    "# Check which items we got right / wrong\n",
    "correct_indices = np.nonzero(predicted_classes == y_test)[0]\n",
    "incorrect_indices = np.nonzero(predicted_classes != y_test)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AVFXdBpjqfxvxwXRNJqZrAinEwhLVQLD69ioRmQdsCRwFDPTRJgDTgQvrOUVZCFefBF0Le/XqBdScLS/c8FlJxE3Xt956q0HxH3jgAQCqq6sB6Ny5MwAnnHBCk+xYunRpanvMmDFNOlcxiJuuAwYMAGoOCDLqUtA6chHpCfQDXgU6+5sGVa0WkU4ZjhkODC+kHUZhMV2TiemaHApWkItIK+Ah4GxV/VpE8jpOVccB4/w5tFD25KJ3796p7d13371GWLhRq9Lnso6SrkHDM8BRRx3VpHMNGTIkZ5x169YBsGHDhjphkyZNAmDWrFk19j///PNNsqtUREnXbBx99NFAzS/oYEWgGTNmFDv52FCQ7oci0gx3U9ytqg/73Z+JSBcf3gVYVoi0jNJhuiYT0zV5NNkjF/cqvw2Yp6rXh4ImAcNwq3EPAx5ralqFIBhYMGXKlDphwdD8yZMnl9SmKBJFXY855pjU9gUXXADUnTQrzI477ghkr/e+/fbbAVi4cGGdsGD1ofnz5zfY1qgSRV3rY7PNNgPS6wGECeYfD7p8GoWpWvkpcCrwtogEU89djLsh7heR04FPgNzfskaUMF2TiemaQArRa+UFIFMF26Cmnt8oD6ZrMjFdk0nFjewcPtw1uG+11VZ1wp577jkgvYSUEV2uvfbavOOedNJJRbTEKAbBiNtgPpWgcRngxhtvLItNUcbmWjEMw4g5FeORBwMLzjrrrDJbYhhGLgKPfJ999imzJfHAPHLDMIyYUzEeebDOX6tWreqEBYN+bC5rwzDiiHnkhmEYMadiPPLazJkzJ7U9aJDrdbV8+fJymWMYhtFozCM3DMOIOVaQG4ZhxBwp5+AXP43mx0AzVV1XytkPI8psVd2j3EY0FdO1DonQFWpqC+S3rl5yiYyuOT1yEVkoImtFZLWIfCYid/gpMI0YICKDRGS+iHzjl/jq4febrglARC4VERWR/UP7TNsYIyLjROQ9EdkgIqflc0y+jZ1HqOqzIrIl8DRwCbWWgvKzqomq1p28OX++ANb4/9nYGVgIrGpkOl2BFjjPojYbAzsBi4CVuNVTWgG5psDrgLO7D85bWQR8A1QB7QD14X2BL6k/jz0amI+siEgH4GHg18DjwJXAfbglvqCydA1oAfTG6fxxHmlFTtcAEekNHIdf8acWpdA2X12huNoK0BGny9Y0TFeA7sAWOG1X+/O1AZoDS4GewPfAp7XOURRdgTm45/R/8j5CVbP+4S7+/qHf1wGT/fZ0YAzwIrAWd7NvgZsmsxr4D3AVUOXjVwF/xl3Aj4AzcQ/CxqHzLQyldQYwDyfKu8BuwERgg09vNXCBj9sfeAlX+M4BBobO0wt4zp/nGeDvwF0Z8jsceCn0u6VPa7sc12kWsL+P2z1LvOnAr/12b+D/SBcAdwNtQnEv9NdwFfAeMMjv38un9zXwGXB9Q/NSabqGjnkSOLR2/uOka668lFjbVUG+y62tP25J+DzZdPX/twHWA3tliTseuMpvtwUmA58DK/x2t1Dc0/x1WoV7oZzs9/fx+fnKX8v78rDxBeC0XPFUtWEFOe7NNRe4MnRTfALsiPNwmgGPArfiCo1OwGvACB9/JM6z7Y7zZqaR4YHHTaP5H2BP3BuyD9Ajw426Je6hORRXXXSA/93Rh78MXI97o+/rL3KmgvxG4OZa+94Bjs3jgb8GeC5HvOmkH/g+3tYWOI9iBvBXH9YXWAx09b97Ar1D+TnVb7cC+jc0L5Wmayjtx+pLK0665spLibVNFeTl1DZ03oYW5COBRTnijiddkLfHPT+bAa2BB4BHfVhL3Eu4r//dBdjRb98D/MHndRNgQB425l2Q51u18qiIrMO9TZ4A/hQKG6+qcwFEpDNwCM77WAusEZEbcJ7hrcDxuBt6sY9/NekFX2vza+BaVZ3pfy/IYt8pwL9VNVgL7BkRmQUcKiLTcDfW/qr6HTBDRB7Pcq5WuLdtmK9wouWiPfV/5taLqi4gna/PReR64FL/ez3uJt5BRD5X1YWhQ38A+ohIB1X9AnglQxK58lIxuvo64j8BB2ZJLxOR0jXPvFSMtk2kodp+iVtdCQARGYN7uQVsAHYSkU80tNA1TtseuBf4ElwhXTDy7X44WFXbqGoPVf2NFzxgcWi7B+4NXy0iK0VkJe5mCBZy7Vor/qJ60goW4usO5LtgZg9gSJCmT3cA7o3YFVihqmtypBuwGti81r7NyV3nNg7nUXTJ02ZEpJOI3Csi/xGRr4G7cHV3QWFwNnAZsMzH6+oPPR3YFpgvIjNF5PBG5qWSdL0cmKiqH+eZdkAUdc0nL6XSNlxvXC5tG8M4/7+h2m4mIreKyCKv7QygjYhUeXtPwHn51SLyhIhs5w+9APeV8pqIzBWRXxUuK4XpR66h7cXAd0AHfxO1UdXNVXVHH16NEzug7qTg6Qd+Ma6uMVeaQdyJoTTbqGpLVb3Gp9lWRFrmSDdgLvDj4Ic/rrffnxF1i9I+C+wlIt2yxQ1xtc/LLqq6Oc5LSU36r6r/UtUBuJte8Y0fqvqBqp6Ie9j+B3iwVv6alJcg+dB2EnQdBPxWRJaKyFJv7/0icmGWY6Kqa6PyEs5WaLup2oa92XJp22C8rgBTgW4ikm83wnNx1WN7e2339fvFn/dpVT0A93KYD/zD71+qqmeoaldgBDBWRPoUJjcFHhDkPyWmAH8Rkc1FZCMR6S0i+/ko9+NuwG4i0pZarei1+CdwnojsLo4+4rvO4RqCtg7FvQs4QkQOEpEqEdlERAaKSDdVXYSr57xcRJqLyADgiCzpPoL7NDpWRDYB/ht4S1XnA4jIZSIyPUP+n8U1zDzi7d5YRFqLyMgMb+DWOK95pe9dcH4QICJ9ReQXItIC+BbXULTeh50iIh3V9TZY6Q+pbwHDrHnJl4ToOgjXG2lX//cp7oG6CWKna9a8NISEaIuItPD3OEBzfz7xYaeJyMIM+f8AGAvc49MPjh0qIvXltTVOs5Ui0o50lRki0llEjvQvoO9w90Cg7ZCQI7AC92Krd9HRwAbcy6GZtyd7WZ1HhftCMjQKEWrgCe3bArgZ1+jwFfAGMNSHbQzcgPuc+Zj6ezeEW8BH4lr1V+Ma6fr5/UfhGmxWAuf5fXvjWoWX4+qFnwC28mFbA8/78+RsAcf1UpjvBZsO9AyF3QaMyXJsc9yn7wJc16xFuBt8q9p5xDU4zfZ2vYl72y/xYbvgGp1W+TxNJt1AdhdulfPVOO96cEPzUom6Zruv46ZrjrxUnLY+z1rrL7jX/wjcneVYAUb7a/4NrsH2PtINleNJN3Z29XleDbyPe4Gqv05dSPdMWenj7eCPu9afdzWu+ml4Fnum15OXgdnugbKO7KyNiByM62lRBfxT3WdWpBCRubgbrwOuYWOcqt7o38734XohLASOV9UV5bIzSpiuySQOugKIyHO4niKbk1BtI1OQi0gV7g13AM4zmAmcqKrvltWwWohIF6CLqr4uIq1xntdgXP/R5ap6jf8ka6uq+dZZJhbTNZnERVeoDG2jNGnWXsACVf1IVb8H7sV9jkUKVa1W1df99irc4IctcbZO8NEm4G4Uw3RNKrHQFSpD26IW5CJysLg5AxZkaDgIsyU1uzkt8fsii7gJhPoBrwKd1TUcBQ1InTIfGW9M1+TSAG1jpyskV9uiFeT+0+sm3GCDHYATRWSHbIfUsy8a9T71IG5QxkPA2ar6dbntKRWma3JpoLax0hUSrm0+reKN+QN+Ajwd+v174Pc54tduqa20v8+LpYfparoWUlvTNVq6FnOpt/o+vfauHUlEhuOGAxuFH71WDEzXhhMHXSEPbU3XGkRG12IW5Hl9eqkbYTUOQGwBgjhguiaXnNqartGkmI2dS6g5tLcbdefzNeKH6ZpcTNuYUsyCfCawjYj0EpHmwFBgUhHTM0qD6ZpcTNuYUrSqFXVrNY7CrU5SBdyufupMI76YrsnFtI0vkRnZCVbnRoQWcy0kpqvpmlAio2uURnYahmEYjcAKcsMwjJhTzO6HkaZly/Sc9ddddx0AI0aMAGD27NmpsCFDhgCwaFFkuowahmHUwDxywzCMmFOxHnmXLull+s444wwANmzYAMDuu++eCjv8cLds4k03NXjxFaME7LbbbgA8/PDDAPTs2bNJ5zvwwPR6xvPmzQNg8eLFmaIbEeOII9xCQpMmuV6To0aNAuCWW25JxVm/vt6FeWKNeeSGYRgxxwpywzCMmFNxVSsdO3YEYMKECTliGnHgoIMOAqBFixYFOV/waQ7wq1+5dZWHDh1akHMbxaF9+/ap7bFjx9YI+/vf/w7A7bffntq3du3a0hhWQswjNwzDiDkV45H/9re/BWDwYLea01577ZXXcfvuuy8AG23k3nlz5swBYMaMGYU20WgAG2/sbt1DDz20oOcNdz0955xzgHRX1TVr1hQ0LaMwBM8oQLdu3WqE3XPPPQB8++23JbWp1JhHbhiGEXMqxiO/4YYbgHQXw3w55phjavwPBgadcMIJqThhL84oDT//+c8B+MlPfgLAtddeW5Dztm3bNrW9ww5ulbPNNtsMMI88agTtIn/4wx8yxpk4cSIAUZpTqhiYR24YhhFzrCA3DMOIOU2uWhGR24HDgWWqupPf1w64D+gJLASOV9UVTU2rMfz73/8G0o2V+fDll1+mtlevXg1Ajx49AOjVqxcAr732WipOVVVVk+2MIlHTdqeddkptB41YH374IQB/+tOfCpLGUUcdVZDzRJmo6dpYdt55Z6DmSOyAdevWAfDkk0+W1KZyUQiPfDxwcK19FwFTVXUbYKr/bcSP8Zi2SWQ8pmuiaLJHrqozRKRnrd1HAQP99gRgOnBhU9PKl/322y+13bdvXyDdyJmtsTOYj2HKlCmpfV999RUAv/jFL4D6G1b+67/+C4Cbb765KWZHjqhpe8kll6S2gy6BBx/syqPgy6mxtGvXDqh57zS0YTwuRE3XxnLsscdmDAs/w5VAsXqtdFbVagBVrRaRTpkiishwYHiR7DAKT17amq6xw3SNMWXvfqiq44Bx0PSlo4KZ7+69997Uvg4dOtQbNzy/+EMPPQTA5ZdfDsA333yTMf7w4e4eDob6Q7rr2yabbAKkhwUD/PDDDw3LREIopK7HHXccUHPwz4IFCwCYNWtWU06dIvjSCnvh06dPB2DlypUFSSMJFFLXphIeCBTw/fffA9m7JCaRYvVa+UxEugD4/8uKlI5RekzbZGK6xphieeSTgGHANf7/Y0VKpwbBsO1MXjjAc889B9ScCOmLL77Iee7AI7/66qsBuP7661NhwYCRwDMP5kKGdK+KBFFybYNVmoLrDHUnR2oswVfcySefDNScq/qqq64CKuarqizPbGPYZ599avwPEwzaevPNN0tqU7lpskcuIvcALwN9RWSJiJyOuxkOEJEPgAP8byNmmLbJxHRNHoXotXJihqBBTT23UV5M22RiuiaPsjd2loqgUSyYYzqf6pT6CKpNgk9xgD333LOJ1hn1scUWWwDQv3//OmGF6uoZNF4H1XHB8m4A06ZNK0gaRmHJ9rwlrQtwvtgQfcMwjJiTSI+8vuH4e++9d0HOLSJ10qid3mWXXZbaPvXUUwuSbiUSzG635ZZbAulh+YWkd+/eNX6/8847BU/DKCx77LFHjd/h7qHmkRuGYRixJFEe+ciRI4HiDq0O1nTs169fal/t4f9hj9xoPKtWrQLSXcl22WWXVFgwpH758uWNOnenTm7gYjDYKOCFF15o1PmM4jJgwIDU9kknnVQjLJhGA2DJkiUlsylKmEduGIYRc6wgNwzDiDmJqloJqj0KSTCnSrDs18UXX5wx7ueffw5UzEjAorN27VogPTo2PNvdE088AdQcYZuJYB7zrbfeOrUvGNFZewmwpM54GHfat2+f2q7dueCZZ54ptTmRwzxywzCMmJMoj7wYBLOonXnmmRnjLFy4EIBhw4YB8MknnxTdrkri0ksvBdJdPwEOO+wwIL8uicHgr7D3nWk+nvHjxzfWTKOI1G6UhnS3w1tvvbXU5kQO88gNwzBijnnk9RCs8wnpFYay8e677wLWda1YzJ8/H4Djjz8+tW/XXXcFoE+fPjmPf/DBB+vsmzBhAlBzqgVI18vkgWZgAAAZp0lEQVQb0aBbt25A3S6HkO5qWKg56eOMeeSGYRgxJ1EeeX3D5wMOOeSQGr/HjRuX2u7atWuNsPDx+fRiKEZvGSM7wSChxs47/dFHH9W7P+jhAjZcPwoEc47X90w/+uijpTYnshRiPvLuIjJNROaJyFwRGe33txORZ0TkA/+/bdPNNUqF6ZpMTNdkUoiqlXXAuaq6PdAfOFNEdgAuAqaq6jbAVP/biA+mazIxXRNIIRaWqAaC1bdXicg8YEvgKGCgjzYBmA5c2NT0shHMfBYsuRZm8uTJQP1VJdmqTzKF3XLLLY0xMTZESddiEFTDhbs0QvKrU+Kma3ggUEDQnfTGG28stTmRpaB15CLSE+gHvAp09jcNqlotIp0yHDMcGF5IO4zCYromE9M1ORSsIBeRVsBDwNmq+nVtTycTqjoOGOfPoTmiZ+Xhhx8G4Pzzz0/tC4bYN5Zg2H2wckywokx1dXWTzhsXoqBrMQgGB9Ueol8pxEXXgw46qM6+YMBdeNbDSqcg3Q9FpBnuprhbVR/2uz8TkS4+vAuwrBBpGaXDdE0mpmvyaLJHLu5VfhswT1XDMxhNAobhVuMeBjzW1LRysWjRIgCGDh2a2jd48GAARo8e3ahzjhkzBoCbbrqpidbFiyjpWgw22WSTGr8rZSBQXHRt1qwZUHcFJ4Bvv/0WsMnpwhSiauWnwKnA2yISdOq9GHdD3C8ipwOfAEMKkJZROkzXZGK6JpBC9Fp5AchUwTaoqec3yoPpmkxM12SSqJGdATNmzKizPWXKFCDdWBkejTlp0iQgPdoz3PATzKNiJItf/vKXQHoGvSuvvLKc5hi1CLr9BvOohEfcLliwoCw2RRmba8UwDCPmJNIjr4+nnnqqxn+jspk5cyaQXmFo2rRp5TTHqMX69euB9HoA4W6is2fPLotNUcY8csMwjJgjURoQEcWBIyVmtqruUW4jCo3paromlMjoah65YRhGzLGC3DAMI+ZYQW4YhhFzrCA3DMOIOWUtyEWkp4ioiFRMN8hKwHRNLqZtNMlZkIvIQhFZKyKrReQzEbnDT4FpxAgRudQ/gPv736ZrjBGRI0TkHa/fS36VnyDMtI0poRfl6tDfH3Mdl+9b9QhVfVZEtgSeBi6h1lJQflY1UdXcqxVn5gtgjf+fjZ2BhcCqRqbTFWgBfJwlTgugN+4afZxHWh1wdvcBmgGLgG+AKqAdoD68L/Al9eexR945aAAi0hs4Dr8yTIhK0rUVTs/3gO+A7sCm/nc2Al27A1vgdF2Nm6+kDdAcWAr0BL4HPq3nHAXVVUS2Ae4GDgVeAc4HJonIdqq6zkcrhbb56grF1ba5P39DRgoFukLjn9miPK+eNiEtc6OqWf9wF3//0O/rgMl+ezowBngRWIu7IFvgpsmsBv4DXAVU+fhVwJ9xF+Qj4Ezcxdo4dL6FobTOAObhxH8X2A2YCGzw6a0GLvBx+wMvASuBOcDA0Hl6Ac/58zwD/B24K0e+n8Q9KDXynyX+LGB/b1f3LPGmA7/2272B/yN9k9ztBQziXuiv4SpcgTPI79/Lp/c18BlwfUPzUmm6evtuCv3u6m3snYeu2wDrgb2yxBsPXOW32wKTgc+BFX67Wyjuaf46rcIVTCf7/X18fr7y1/K+DGmNAp4I/d7IX7fg/iiltqvw93MZte0ZtimfP2CW/x+pZ7YxeVHVhhXkOK9kLnBlKIOfADviPNdmwKPArUBLoBPwGjDCxx8JzPfnaQdMI8MDj5tG8z/Anjjvpw/QI8ONuqW/sIfibuoD/O+OPvxl4HrcG31ff5EzFuQ+7cfqSyvHA38N8FyOeOGboo+3tQXQEZgB/NWH9QUWA11DAvcO5edUv90K6N/QvFSarsBfgLG1zq3AUXnoOhJYlCPeeNIFeXvgWGAzoDXwAPCoD2uJe5j7+t9dgB399j3AH3xeNwEGZEjrLODfod9VwLfA6DJomyrIy6htT2/Tf4AlwB1Ah1y6+v+RemYbkxfV/Avy1bi35iJgLLBpKINXhOJ2xn22bhradyIwzW//HzAyFHYgmR/4p/E3ZgabwjfFhcDEWnGexk2QvxVu5fCWobB/ZbkpWgEfAL3qSyvHA/8P4N58b4p6wgYDb4RumGU4j6FZrXgzgMvzuFkz5qUCdR2E86B2wVWp3IrzEk/MQ9c/AK/kiDceX5DXE7YrsMJvt/TX/Njw9fRhd+KWUeuWI63tcFUaA3HVCn/0efl9GbQNF+TlfGb3wL2YOgMPAk/n0tX/j+Iz26C8qGrevVYGq2obVe2hqr9R1fByKotD2z1wb/hqEVkpIitxD0ywkGvXWvEX1ZNWMAdtd+DDPO3rAQwJ0vTpDsB5O11xD9GaHOkGXI67wT7OM+2AcTiPoku+B4hIJxG5V0T+IyJfA3fh6u5Q1QXA2cBlwDIfr6s/9HRgW2C+iMwUkcMbmZeK0VVVpwKX4pY4W0S6vnZJDhsao+tmInKriCzyus4A2ohIlbf3BJynWy0iT4jIdv7QC3Ce7GsiMldEfpUhL/NxBd7fcdUhHXDVGOG8lErbcJtAubRdraqzVHWdqn6Gq3o6UEQ2z5L+OP8/Us9sI/NSkO6HGtpejHu7d/A3URtV3VxVd/Th1TixA7aq53zBA78YVx+VK80g7sRQmm1UtaWqXuPTbCsiLXOkGzAI+K2ILBWRpd7e+0XkwizHoG5R2meBvUSkW7a4Ia72edlFVTcHTiE06b+q/ktVB+BuegX+x+//QFVPxD1s/wM8WCt/TcpLkHxoOwm6oqo3qeo2qtoJV6BvDLyT45hxwFSgm4jkO6/GubjP7L29rvv6/eLP+bSqHoArQObjvEJUdamqnqGqXYERwFgR6ZPBrgdVdSdVbY97QfUAZuZpXyG1DTegl03bDGllXFHa6wrRe2YbnBcocD9yVa0GpgB/EZHNRWQjEektIvv5KPfjCpZuItKWWq3otfgncJ6I7C6OPiLSw4d9BmwdinsXcISIHCQiVSKyiYgMFJFuqroI93l8uYg0F5EBwBFkZhCwE+5zeFecxzECuAlARC4TkekZ8v8srmHmEW/3xiLSWkRGZvCuWuM/gX3vgvODABHpKyK/EJEWuPrPtbgGN0TkFBHpqK63wUp/yPqG5iVfkqCrP3Ynn+ZWOI/sRlVd4cNPE5GFGfL/Aa564h6ffnN/vqEiUl9eW+P0Wiki7XAFbWBHZxE50j/E3+H0D3QdEipQVuAe4vp0xV+/KhHpiPOgH/eeeoNIiLZ7++dlIxFpD/wvMF1Vv/LhsXlmc+UlI7nqXshSR0w9dUe4FvCbcZ95XwFvAEN92MbADbjPmY+pv3dDuAV8JK7ldzXOc+rn9x+Fa7BZCZzn9+2Na+Vejust8ASwlQ/bGnjenyevXiuZ8o9r3R+TJX5zXJXGAlw95iLcDb5V7TziGpxme7vexHlyS3zYLrhGp1U+T5NJN6LchauLW41ryBrc0LxUmq64roJveU2W4jyrqlD4H3Gryme6dgKM9tf7G1xj1H2kGyrHk27s7OrzvBp4H/fyVH+dupDumbLSx9vBH3etP+9qXBXF8Cz2vBC6N26lZn1ypWl7ordtDc6bvxP4URyf2Vx5yfQXtWlsDwZuxLXC/1PdZ1akEJG5OJE64BqYxqnqjd7zug/X6rwQOF69t1fpxETX53A9RTbHdM2LOOgKlfHMRqYgF5EqnPdyAM4zmInrURCpRTNFpAvQRVVfF5HWuLfzYFzf4OWqeo3/3G6rqvnURSca0zWZxEVXqAxtozRp1l7AAlX9SFW/B+7FfY5FClWtVtXX/fYq3OCHLXG2TvDRJuBuFMN0TSqx0BUqQ9uiFuQicrCIvCciCzI0CoXZkprdnJb4fZFFRHoC/YBXgc7qGo6CBqROmY+MN6ZrcmmAtrHTFZKrbdEKcv/pdRNwCLADcKKEJvap75B69kWj3qcexE1C9BBwtqp+XW57SoXpmlwaqG2sdIWEa5urNbSxf8BPCI1IAn6PH3mWJb5W+N/nxdLDdDVdC6mt6RotXYs5p3B9n157144kIsOB4UW0I05kG3EaFUzXhhMHXSEPbU3XGkRG12IW5Hl9eqkbYTUObFXumGC6Jpec2pqu0aSYjZ1LqDm0txv1z9VsxAvTNbmYtjGlmAX5TGAbEeklIs2BocCkIqZnlAbTNbmYtjGlaFUrqrpOREbhpqasAm5X1bnFSs8oDaZrcjFt40tkRnaC1bkBs1U13xn2YoPparomlMjoGqWRnYZhGEYjsILcMAwj5hSz+6FhGEZZaNu2LQBbbZV5PYpFi1w38N/97nepfe+849YZef/99wGYM2dOsUwsKOaRG4ZhxJzEe+SdOrl5cO6//34AXnrpJQDGjRuXirNw4cKCpLXFFlsAsO+++6b2PfXUUwD88MMPBUnDMIyaHHbYYantI488EoCBAwcC0KdPvSvlAWmvu0ePHql9LVq0qBGnqqqqUGYWFfPIDcMwYo4V5IZhGDEnkVUrQUMHwNy5bjxDUO3x2WefAYWrTgmfe/bs2QB07NgxFbb77rsDsGDBgoKlZ9Rl8803B+Dqq69O7dtpp50A2H///QGr3oozvXv3Tm2feeaZAJxxxhkAbLrppqkwkayLzddg2223LZB15cc8csMwjJiTKI+8Q4cOANx3332pfe3atQNg7NixAJx11lkFT/eSSy4BoFevXgCMGDEiFWaeeHE5+eSTARgzZgwA3bt3rxMn8Na//PLL0hlmFJRu3bqltkePHt2kc82fPx9If60nAfPIDcMwYk6iPPLddtsNSHc9CnPFFVcUNK0dd9wxtX3uuecC8MgjjwA1vwiM4hB4aH/9618BaN++PQD1zR30t7/9DYBRo0al9i1fvrzYJhp5EnxJQ9rbfvHFF4F0993vvvsuFeerr74CYM2aNQC0bNkyFTZlyhQgPbDn1VdfBeCNN95IxVm7dm2N45OAeeSGYRgxxwpywzCMmNPkqhURuR04HFimqjv5fe2A+4CewELgeFVd0dS0MhGM3jz22GPrhJ1++ukAfP755wVJK6hSefbZZ+uEBVUrq1atKkha5SYK2mbivPPOA9KN2dk44YQTADj44INT+4LG0aDa5fvvvy+0iZElKroGVSJBdQjAj3/8YwCOPvroGnFfeeWV1HZQhRp0IQ7Pp7JkyRIANmzYUHiDI0whPPLxwMG19l0ETFXVbYCp/rcRP8Zj2iaR8ZiuiaIgC0uISE9gcujt/h4wUFWrRaQLMF1V++ZxnkYZM3HiRABOOeUUID0wB2C//fYDCtewMXLkSCDdnRFg/PjxAPzqV79q6ukjM1F9QCG0LdQCBOE5Md566y0AWrVqBcDbb78NpAd8QXogUH0sW7YMgH79+gGwdOnSQpiYCdM1RPPmzQF44IEHADj88MNTYX/605+A9MCub775pjFJlIrI6FqsXiudVbUawN8YnTJFFJHhwPAi2WEUnry0NV1jh+kaY8re/VBVxwHjoPFv+OCrIqgX+/TT9MLfTa37DIb/XnzxxQD85je/qZEmFMQTTxyF0LU2u+66a2q7devWADz//PNA+strk002ScU58cQTgbR24WHeP/rRjwB47LHHADjkkEMA65aYi8bqGnw5Afz+978H0p74F198kQr785//DETeE48cxeq18pn/PMP/X1akdIzSY9omE9M1xhTLI58EDAOu8f8fK1I69RKenzhoEV+5ciUAN998c87jA+8O0oOL+vfvXyPOgw8+2FQz40rZtA3PFR18Ed1www014nz77bep7TvuuAOAIUOGALD11lvXOWfg+VVSr5UMFFXXwYMHp7Yvusi1o37yyScA/OxnP0uFBYN9jIbRZI9cRO4BXgb6isgSETkddzMcICIfAAf430bMMG2TiemaPJrskavqiRmCBjX13EZ5MW2TiemaPMre2FkIbrzxRgB+/vOfA9C1a9dUWLDsWjBPcbAUVDbCcxrX7p750UcfAekGNKN0BI2XYYJqtEcffTTjcXvskbmHWDDQZPXq1U20zsjGPvvsU2dfMP9JMIjHaDw2RN8wDCPmJMIjDwYA7bLLLkDNbmrBsOzzzz8fSA/VnzBhQsbzBQOMAObMmVMjLFi8+cMPP2yq2UYDueeee1LbwZfVnnvuCcB2220HwM4775yKEwzzDlaMChq8w/uCVWYCzd99992i2F7pHHfccXX2Bc/mpZdemtoXdAd98803S2NYQjCP3DAMI+YUZIh+oSjUwJGmEu6mFqzwE3gIBx10EFC4SbhqEZkhv4WkULqGJ8gKdAnWSw3aNeq7n4MJzoK1HgEmT54MwDbbbAPAP/7xDyA9BUOBqXhdw7pkm9AqCLvllluAdBtGeGKsQPv6VvgJJrV7+eWXgaLXv0dGV/PIDcMwYo4V5IZhGDHHqlbqIZjNEODUU08F0g0zzzzzTDGTjsynWiEphq7BzIbBCNugiiV8PwdzjV944YVAzVGfwSx7wSjDRYsW1TgvFLRBu+J1ve6661Lb55xzTlHsCRNUfU6fPh2AoUOHFiOZyOhqHrlhGEbMMY88RDAnR3jx5GC1n2Cw0euvv15MEyLzhi8kxdQ18KBPOukkoGYXw//+7/8G6h/sE8xq+a9//QtId2e86667UnGGDRtWKDMrXteqqqrUdjAHfHDtN9443Qu6e/fuAGy0UWF8zKB8u+yyy1L7rrrqqoKcmwjpah65YRhGzEnEgKBCEcxJHSboplZkT9xoJEHXwvrWUM3G2rVrgfTXV+CRB19ekO7uaHOUN53169entmfNmgXAtttuWyfeoEFuupdmzZoBaU86GPjVUIJuqbvvvnujjo8L5pEbhmHEHPPIQwQeeXh9z7/85S/lMscoAffffz+Q9shPOOGEVNioUaMAuOKKK0pvWIUyderUGr+D6TbCHvm6deuA9HzzwWAugLPPPhtIt5lUCoWYj7y7iEwTkXkiMldERvv97UTkGRH5wP9v23RzjVJhuiYT0zWZFKJqZR1wrqpuD/QHzhSRHYCLgKmqug0w1f824oPpmkxM1wRS8O6HIvIY8Hf/N9CvyN0FmK6qfXMcW5buh8H8GmPHjgVg2bL0coXBIr0lIjLdmWoTR10bQvAJ/+KLL6b2BQs5b7/99gC8//77jT296dpIdtttNwBmzpyZMc60adNS28HSjOE1BSD9bAOcddZZhTIvMroWtI5cRHoC/YBXgc6qWg3gb45OGY4ZDgwvpB1GYTFdk4npmhwK5pGLSCvgOWCMqj4sIitVtU0ofIWqZq13K5fnFsxsGMxlHR6if/rppwPQunVrID2PdbBwbIGJzBs+IM66NoZzzz03tR0MK3/44YeB9HQNkO6+mCemayMJBm7dfvvtqX3HH398zuOC7o5PPPEEAKecckoqLNyZoYlERteCdD8UkWbAQ8Ddqvqw3/2Z/0TD/1+W6XgjmpiuycR0TR5NrloRVxl1GzBPVa8PBU0ChuFW4x4GPNbUtEpFePDCySefDMDvfvc7ID0HcgGHb0eSJOqaD3feeWdqe8SIEQAcc8wxQM1uiG+99VZpDSsQcdM1+PIJuhUCtGrVCkivxdqpU7oWaOHChUB6xafw0PwkU4g68p8CpwJvi0iwPtPFuBvifhE5HfgEGFKAtIzSYbomE9M1gTS5IFfVFwDJEDyoqec3yoPpmkxM12Risx9St7Ez3HUpuD633XYbAFdeeSUAixcvLoYpkWk8KSRxauwMEywvFnyuhxd/Dqrc8sR0LQJB43P//v1T+y6//HKgZhfiIhIZXW2uFcMwjJhjHjkwYMAAIN2YNWPGjFTYzTffDMCKFSsA+P7774tpSmTe8IWk3J5bU5kyZQoAP/nJT1L79t57bwDefffdfE5huiaTyOhqHrlhGEbMsdkPgRdeeAGAX/ziF2W2xIgixx13HABz5sxJ7evTpw+Qt0duGEXFPHLDMIyYYx65YeTg66+/BqBXr15ltsQw6sc8csMwjJhjBblhGEbMsYLcMAwj5lhBbhiGEXOi1tj5BbDG/48bHWi63T0KYUgEMV2TiekaESI1shNARGZFZbRUQ4ir3aUirtcnrnaXirhen7janQmrWjEMw4g5VpAbhmHEnCgW5OPKbUAjiavdpSKu1yeudpeKuF6fuNpdL5GrIzcMwzAaRhQ9csMwDKMBWEFuGIYRcyJVkIvIwSLynogsEJGLym1PfYhIdxGZJiLzRGSuiIz2+9uJyDMi8oH/37bctkYF0zWZxEFXqAxtI1NHLiJVwPvAAcASYCZwoqpGasJnEekCdFHV10WkNTAbGAycBixX1Wv8Td1WVS8so6mRwHRNJnHRFSpD2yh55HsBC1T1I1X9HrgXOKrMNtVBVatV9XW/vQqYB2yJs3WCjzYBd6MYpmtSiYWuUBnaRqkg3xIIL02/xO+LLCLSE+gHvAp0VtVqcDcO0Kl8lkUK0zWZxE5XSK62USrIpZ590aj3qQcRaQU8BJytql+X254IY7omk1jpCsnWNkoF+RKge+h3N+DTMtmSFRFphrsh7lbVh/3uz3xdXFAnt6xc9kUM0zWZxEZXSL62USrIZwLbiEgvEWkODAUmldmmOoiIALcB81T1+lDQJGCY3x4GPFZq2yKK6ZpMYqErVIa2kem1AiAihwJ/BaqA21V1TJlNqoOIDACeB94GNvjdF+Pq3O4HtgI+AYao6vKyGBkxTNdkEgddoTK0jVRBbhiGYTScKFWtGIZhGI3ACnLDMIyYYwW5YRhGzLGC3DAMI+ZYQW4YhhFzrCA3DMOIOVaQG4ZhxJz/D4nlLDPmSWxzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5miYsCTZ4sBs2HTrCiiX499h+++2BejWtGHUkDDfM1qSyYsUKID07N+pDJ+wXlnoLz3mYIZpUzCI3DMOIOYm0yKMEXynhjRze3tEOkvBmz8azzz4L2ESRShCGGAYr+7jjjsuZ9vzzzwfSqz1B2pr7wx/+sFr60Mn/5ptvAjBp0iQgPXEM0p73gmVvNBzRyVuZBM+Gwef4CSeckIp74okngPRizSNGjADS3k+TilnkhmEYMSfxFnkg+J2+//77gfQanABbbrklANOmTQPSbXAAQ4cObaASGpmESTv5LPHgFz6b64XDDz8cgLZt2wK111kNllt0LUgjHmT6Gl+8OO1x99FHHwXSFnmw1i+44IKc+ycBs8gNwzBiTqOxyAPffPPNamHRUSpQuz183rx5ZS+TkZ2RI0fmjPvwww+B9GiiMLooykYbbQSkLfFx48al4oIzNSN+/Pvf/84ZF50MCPDiiy8CyV+LtRT+yLuKyGQRmSkiM0RkhA9vKyJPich7/n+b+hfXaChM12RiuiaTUjStfAecoapbALsAvxWRPsBZwCRV7Q1M8r+N+GC6JhPTNYGUYmGJGiCsvr1cRGYCnYGBQD+fbCwwBRhV3/zKwe9///tav0NHWGOmUrpuuOGGqe3opC2o3Sx25JFHAvmbvoInxOC9MHR4Q+Od2JWE5zWT6PJ+wRtm8Et+ww031PqdVEraRi4iPYDtgJeADv6mQVVrRKR9jn2GAauvBmBUDaZrMjFdk0PJKnIRaQHcB5ymqstEpKj9VHU0MNofo8FcMW677bap7f79+zdUtrGjoXWNLnydOT07OlTwpZdeKnisMPlr7NixALz++uvFFiPxxO15jdKlSxcgrWtwwQDpYcW33norAPfee28Dl64ylGT4oYg0w90UE1T1fh+8SEQ6+viOwOJc+xvViemaTEzX5FFvi1zcq/wfwExVvSYS9TAwBLca9xCgqha7bN68eWo7WIFhItCECRMqUqZqolK6Rod+dujQoV7HCutwhv9GfJ7X8PX02WefAbX7ToYMGVIrbfRr4rrrrgPS6wc0FkrRtLIbcDTwloiEb9dzcDfEv0TkOOAjIPf6aUY1YromE9M1gZRi1MpzQK4Gtr1yhBtVjumaTEzXZNLoZnYGPvnkk9T2l19+CcD06dOB9GwwwzAqw+TJkwHo27cvAKNHj07FhcXVg0fTsPwiwPPPPw80vgWzzdeKYRhGzEnk4ssxpmoWcy0lpqvpmlCqRlezyA3DMGKOVeSGYRgxxypywzCMmGMVuWEYRsyxitwwDCPmWEVuGIYRcypakYtIDxFREWm0E5OSiOmaXEzb6qRgRS4ic0VklYisEJFFInK7d4FpVDki0k5E/isin4nIUhF5QUR283Gma4wRkfVF5EYR+VREvhCRZyJxpm0CEJEh/qV5fKG0xb5VD1LVp0WkMzAR+AMZS0F5r2qiqvVZeuVTYKX/n4+tgLnAmq6o2glYB/iwiLQbAh2Btwuka4crd1dgA2AesALn16I1sDawEOgBfAP8L8sxuhdRnrqwAjgWeA9Q3Cow/44sGtCYdG0K9MXpshS3Kk4LYFaBYwZdewHN/P5fAk2Atrjr+imwGfAZ2c+x1LqC8wneFNgC+BzYNiO+IbQtVlcor7YtgJ7AbOBr3DO4nv+di6ArrPkzWw5dAfBrpp4NzChqB1XN+4e7+HtHfl8JPOK3pwCXAv8FVuFu9g1wbjJrgI+BS4AmPn0T4CrcBZwD/Bb3IDSNHG9uJK8TgJk48d8BtgfGAT/4/FYAI33aXYDncQ/pG0C/yHE2Bqb64zwFXA+ML3Tuft/JwAVFpHsF6A18D+yUJ90Y4BK/3QZ4BPgEWOK3u0TSDvXXaTnuBv61D+/lz+cLfy3vLqJ8awEH+evdvrHpilvV5vnI7+Y+r82L0HVvn7ZrnnRTgOP9dk/gP6Qr9glA60jaUf4aLsdVNnv58J18fsuARcA1OfLazKdpVQXP7PJw3hXU9irghsjvTr6MPfPp6v9X5TML3AycHL2v8qYvogJI3RS4N9cM4OLITfERsCXOOmgGPAjcgntQ2gMvAyf69MNxFlBXnDUzmRwPPM6N5sfAjrg3ZC+ge44btTPuodkfV2H197838vEvANfg3ug/8xe5YEWOe+N+D2xcRNpX/PnNK5AuelNsCBwKrA+0BO4BHoxUNMuAzfzvjsCWfvtO4Fx/rusCuxfI802cRaHArY1RV+A64KaMsLeBQ4vQ9XJgaoF0U0hX5L18WdcBNgKeAa71cZsB84FO/ncPfIXjz+dov90C2CVHXscAbwF/wVUKb0XPo4G1TVXkFdT2auDGjGMrMDCfrpHzq6pnlvQLfS1KXJGvwL015wE3AutFboo/RtJ2wH3arBcJGwxM9tv/AYZH4vYh9wM/ERiRp0zRm2IUMC4jzUScg/xuuJXDm0fi7sh1U2Qc4zxgSqF0kQf+XODFYm+KLHHbAksiN8VSf9Osl5Hun7hP6y7FlM3vs67XYkhj1BVncV6eEfZfYGgRut4K3FUg3RRyPHDAwcBrfrsXbvWdvYFmGemeAS4C2hXI6xx/fS/Eff7/3Gu5RQW0jVbkldJ2L9wLbWtck8otuC+Awfl09f+r6pnFfQG9Avyk0H0V/St21MrBqtpaVbur6smquioSNz+y3R33hq/xnWtL/UUNbbKdMtJnWwI9dNp0BT4osnzdgUEhT5/v7rg3YifchV5ZIN9sHINbUbwYRuMsio5Fpg8dVreIyDwRWYY799Yi0sSX93CcxVAjIo+KyOZ+15E4i+dlEZkhIscWyktVv1LVO4GzRGQbH9yYdF0BtMoIa0XhNts10bW9iNwlIh97Xcfj2mRR1feB03CV8GKfrpPf9ThgU2CWiEwTkQNzZLEK+BZXuXyjqlNxlvI+kTQNpW203bgi2qrqJOAC3PJ180i3xS/Ik3/wi1ttz+zJwJuq+kKxZYLSDD/UyPZ83Nu9nb+JWqtqK1Xd0sfX4MQOdMtyvPDAz8e1NRbKM6QdF8mztao2V9XLfZ5tRKR5JH22fGvhR3d0AopavVXdorSTgC4iUqxHtDNwn9o7q2or3CckeMf/qjpRVfvjbrRZOMsQVV2oqieoaifgROBGEelVZJ7NgE2KOaXIdhJ0nQGEFxh+v54U6Ezyuj4N7CQiXfKljfAnfy5be12PIrKYg6reoaq74yozBf7sw99T1cG4SvTPwL0Z5xd4s8hy5KKU2kbX0avYM6uqN6hqb1Vtj6vQm5JngILXFarvmd0LOEREForIQmBX4GoRuT5foUo6jlxVa4AnfcatRGQtEekpIj/3Sf4FnCoiXXyv7Fk5DwZ/B84UkR+Lo5eIdPdxi6hdGY0HDhKRfUWkiYisKyL9RKSLqs7DfapcJCJri8juuE6/QgwB7lPVWhabiAwVkbk5zv893GfsnT7/tX1ZjhCRbOfaEmddLRWRtjirIuTTQUR+4W/mr3EW5fc+blCkUlmCe0i+zzy4iOwiIrv7cqwnIqNwn9KFl6CvfV5J0PUBoK+IHCoi6wLn4yyfWf5aXSgiU3Kc/9O4DrcHfLmbikhLERmew7JqiW/aEDdq5PchQkQ2E5E9RWQd4Cuc/kHXo0RkI3WjSJb6XVbTFfdS/Ag425dlN6AfrmmiTiRBW79vX59nN5y1fZ2qLvHxsXlmcZ2lW+CabLYN1wHXBJSbQm0vZLRtZcRNIaP9BtcDfhPus+YL4DXgCB/XFNdB8xmuRzfb6IZoD/hwXK/+CtzbdTsfPhB3Iy8FzvRhO+N6hT/H9Sg/CnTzcZsAz/rjFBy1gmtPXoofTZARdx5u9fFc+wowAmfpfYnr/LmbdKfHGNIdJ538Oa8A3sW9qdVfp46ke7mX+nR9/H5X+OOuwH3KDstRlp/jRgMs99dlKvCzRqzr3jgraZUvU49I3D+AS/PsuzbugXofN+RuHq7i6pZ5jriOxOm+XK/jrLgFPm5rXGdi0OQR0h2f43Ht5ytw98/BecqzJa5DcCVudMghjfWZxQ0VfNNfi4W4L6ImcXxmi9Er21+1LSwxADe6oAnwd3WfWVWFiEzFVfStcB0qo1X1Ov92vhs3CmEucJh6i6CxExNdZ+AqlHaYrkURB12hcTyzVVORi0gT3BuuP84ymIbrdX6nogXLQEQ6Ah1V9VURaYmzvA7GfRJ9rqqX+0+yNqo6qoJFrQpM12QSF12hcWhbTU6zdgLeV9U5qvoNcBfuc6yqUNUaVX3Vby/HTX7ojCtrGOEyFnejGKZrUomFrtA4tC1rRS4iA0Rktoi8n6PjIEpnag9zWuDDqhYR6QFsh+s87KCu4yh0ILXPvWf8qYO2pmuMSLKukFxty1aR+0+vG4D9gD7AYBHpk2+XLGHV0e6TBXFOiO4DTlPVZZUuT0NSR21N15iQZF0h4doW03O6Jn/AT4CJkd9nA2cXSK+N/O+TculRKW1NV9M1wX9Vo2s5fQpn+/TaOTORiAzDOTQyip9xWmkKamu61sJ0TSZVo2s5K/KiPr3UzbAaDSAiq8UbVUlBbU3XWGK6xpRydnYuoPbU3i5k98FtxA/TNpmYrjGlnBX5NKC3iGwsImsDRwAPlzE/o+EwbZOJ6RpTyta0oqrficgpOP8PTYDbVLW41S6Mqsa0TSama3ypmpmdYG1uwHRVLdYLW2wwXU3XhFI1ulbTzE7DMAxjDbCK3DAMI+ZYRW4YhhFzrCI3DMOIOVaRG4ng1FNP5dRTT610MQyjIlhFbhiGEXOsIjcMw4g55fS1Yhhlo3lzt8D65Ze71cV69OgBwF//+tdKFcmoIjbbbDMAzjzzTAC6dOmSittnn30AuP322wE4/vjjG7h0pccscsMwjJgTW4t8o402Sm1feumlAOyyyy4AzJw5MxUXLDQR59htzpw5APzvf+YLKM5svPHGAJx88skA7Lzzah6SjUbIddddB8Cxxx4LwHrrrbdamjCbvX///g1XsDJjFrlhGEbMqXqL/IADDgDSbaHdu3cHoGnTdNHXXXddABYtWgRA3759U3GHHHIIAGut5d5Z33zzDQDfffddKs39998PwB133LFa/tOmTQNgyZIl9T0Vo4Rce+21ALz99tsArFq1qpLFMSpAeM5POumkVFho727SpEnB/V977bXyFKwCmEVuGIYRc6wiNwzDiDn1bloRkduAA4HFqtrXh7UF7gZ6AHOBw1R1jdomzj33XAC23HJLAGbMcO6Ro52Vb7zxBgBPPfUUULvZJTSJrLPOOkC6U2zfffdNpena1S2Kcs899wDQsmXLVNxnn30GwBVXXAHAv//9bwBmzZq1JqcTK8qtbV2Jdk4FjbfZZps1OlbPnj0BaN26NQDTp08HYI899kil2W233XLuH+65cD/EiWrTta6EumDSpEkAbLjhhnXaf/ny5QD85S9/KW3BKkgpLPIxwICMsLOASaraG5jkfxvxYwymbRIZg+maKEqysISI9AAeibzdZwP9VLVGRDoCU1R1syKOs1ph+vTpA8CYMWMAeOeddwAYOnRovcudSZs2bYD0Gx/gV7/6FQBDhgwBYMGCBQCccsopqTRTp04tVRGqxlF9oBTalmoBgquuuiq1vf322wOw55575kzfqVMnAB588MHV4lq1agWkv9SCrtFhrb1798557E8//RSAefPcQuo77bRTvqKbrmtIeBZPOOGEVNihhx4KpPWtax0WhidvtdVW9S1e1eharlErHVS1BsDfGO1zJRSRYcCwMpXDKD1FaWu6xg7TNcZUfPihqo4GRkP2N3ywwEOb9tdff122soT29JdffjkVtnjxYiA97HHgwIEAnHVW+sszWIf//Oc/gXS7emOmkK51IVhe0fbwMMxshx2cQfTRRx8Bab0AbrvtNiBtfYdJYQC9evWqlcfYsWOB2sPWzjnnnJxlateuHVD7XmkMlFLXfISJPGHY8X777ZczbRh2DHD++ecDcMkllwC1v7ACTz75ZMnKWS2Ua9TKIv95hv+/uEB6Iz6YtsnEdI0x5bLIHwaGAJf7/w/V94ANMSEnTD66+eabU2FhVENw0hSItomG0RRvvvkmkO5NTygl17YQ48aNA6Bfv36psFtuuQWAbt26AfDrX/8aqG2Rr1ixAkj3c4RJYQAdOnRAvyoRAAAem0lEQVSolcczzzwDpEcwRfcL7gCaNWuWips4cSKQngqeABpc12yEL6Ubb7wRyN8HEli5cmVq+8QTTwSyW+KBoNmLL74IpEerxZl6W+QicifwArCZiCwQkeNwN0N/EXkP6O9/GzHDtE0mpmvyqLdFrqqDc0TtVd9jG5XFtE0mpmvyqHhnZzUROtU6d+68Wtxbb71VK+68885LxS1duhRIfJNKgxMmb+24445Abd8YobP59NNPB+Dzzz9fbf/QNFIX3n///dR2aD674YYbgHTzDaQnpH3yySd1zsOoTfv26QEy//d//wcU16QS2GSTTeqUX5jwF4azWtOKYRiGUXHMIo8QJh397Gc/S4UFqy5YgMEqXLhwYcMWrhESOq5CR/OECRNScWFKfdRKLjWhQ7SceTRmwnDSxx9/PBUWtc5LQegIvfXWW1NhH3zwAQBffvllSfOqJGaRG4ZhxByzyCN8++23AJx22mmpsGXLlgFw0003AfDqq68CcMwxx6TShGFuRv0JEzoAjjrqKACee+45AP72t7+VPf8LL7wwtT1q1CggvepMdILQ999/X/ayJJUwge7ss88GSm+FQ9q53oABzqVMTU1NyfOoJswiNwzDiDlWkRuGYcQca1rJQtRXyogRIwB45JFHALj33nsBeOih9MS3M844A4DXX3+9oYqYWKJNG8Gr3Q8//ADUXp6v1Fx22WVAbZ/nf/7znwF44oknAPjqq6/Kln/S+fGPf5zaDsN0o37/14QwUzfcH5Be+u/KK68Ekt+kEjCL3DAMI+aYRV6AYAWGIVJhckp0ZZjQQReGy9kkkTUn6qEwWOTBcvvRj36Uiqvv8M/gNXH48OFAuvM6asEFb5Zz5sypV14G7LLLLqntFi1aAMX5EX/66acB+PDDD1NhwTd5sMSjxwk+WsaPH1/PEscLs8gNwzBijlnkRRImpQRLrm3btqm4Qw45BEhb61EPekbdyGalbbfddkDaZzjA4MHOXUi2qfmZbL311gAMGjQoFTZy5EgAHnvsMQAuuOACIO0FEcwSbyiCBpDuZwoeSIP7i+g6vNHVgiC94g/A3XffXbZyVjNmkRuGYcQcs8izEJxnARx55JFAuv07rL4eJYxmiFoWxpoRpk9D2qd0aCPfe++9U3F33XUXACeffDKQHqWQufIPpFcI+utf/5oKC5NSQpt4MZa9sebccccdqe3Q7h0I657C6iODgpuEKVOmrHbMd999F4D9998/FRYs+MZGKfyRdxWRySIyU0RmiMgIH95WRJ4Skff8/zb1L67RUJiuycR0TSalaFr5DjhDVbcAdgF+KyJ9gLOASaraG5jkfxvxwXRNJqZrAinFwhI1QFh9e7mIzAQ6AwOBfj7ZWGAKMKq++ZWDMDTq8MMPB2ov35Vr0sIXX3yR2g4+sW+//fZyFbHBqZSuvXv3Tm2HxZODL5voELa99nJrIMyePTvnscLQ0eArJeovPvjiaGxUStfoUo11WbbxlFNOAWrfF4HgT3z+/Pn1LF38KWkbuYj0ALYDXgI6+JsGVa0RkayecURkGDCslOUwSovpmkxM1+QgxQzKL+pAIi2AqcClqnq/iCxV1daR+CWqmrfdTURKU5g8BN/Se+yxRyrssMMOA9ITFaIEyy0s1BqGpEX9G3/66aelKt50Vd2hVAcrBZXUddNNNwXS1zwstAxpFwnRSUKZhIlawXNlBTFd60jQ+r///S9Q20Ni8EW/6667liv7YqkaXUsy/FBEmgH3ARNU9X4fvEhEOvr4jsDiXPsb1YnpmkxM1+RR76YVcXOq/wHMVNVrIlEPA0Nwq3EPAR7KsnuD8/DDDwPp9RgBpk2bBsCsWbOAtIMsSA+NeueddxqqiFVBNegahpcFohN0ttpqq3Jlm2iqQdd8rLfeekC636ljx45A7WGpVWCJVx2laCPfDTgaeEtEgvu/c3A3xL9E5DjgI2BQjv2N6sR0TSamawIpxaiV5wDJEb1XfY9vVAbTNZmYrsmkZJ2dpaAhOjurnKrpPCklpqvpWiyhyfP5558H0kMVd99991SafENOG5iq0dV8rRiGYcQc87ViGEbV8uWXXwJVZYVXJWaRG4ZhxByzyA3DqBoWLFgApIf7brDBBpUsTmwwi9wwDCPmmEVuGEbV8L///Q9Ir+pkFIdZ5IZhGDHHKnLDMIyYU9GKXER6iIiKiDXxJAjTNbmYttVJwYpcROaKyCoRWSEii0Tkdu8C04gBIrKriLwsIstF5E0R2d2Hm64xRkQO88u1LReRd0Tk4EicaRtjRGRPEXlVRJaJyBzvAz4vxb5VD1LVp0WkMzAR+AMZS0F5r2qiqj/UueRpPgVW+v/52AqYCyxfw3w6AesAH2aJa+aP/z6wDNgA2AR4C7dMVi7a4crdAfgRMM/vr0AroCWwoEDe3et+KrkRkbY4r3YnAfcDg4F/i8gmPklj0rUp0Beny1LcqjgtgFkFjhl07YW7N+YBXwJNgLY4fT8FNgM+I/s5llrXzsB43Ko+TwD7A/eISA9VDe5nG0LbYnWF8mrbCqdHWLarG06r9/IcL+gK0BX3nM8DVuB80bQG1gYWAj2Ab4D/ZRyjpLpCysXwA8BIYDSwAzBZRF5S1Tdy7qiqef9wF3/vyO8rgUf89hTgUuC/wCrczb4Bzk1mDfAxcAnQxKdvAlyFu4BzgN/iHoSmkePNjeR1AjATJ/47wPbAOOAHn98KYKRPuwvwPO4hfQPoFznOxjgn+suBp4DrgfE5zndnYHFG2CfATwpcp1f8ua8ABuVJd2E0b+Ae3M3yBfAMsGUkbn9/3sv9tTzTh7cDHvHn+jnwLLBWlrwOBGZkhL0LHNcIdR0GPB/53dzntXkRuu7t03bNk24KcLzf7gn8h3TFPgFoHUk7yl/D5cBsYC8fvpPPbxmwCLhmTe7RBtZ2eTjvSmmb5fpsDywvpKv/3xv4HtgpT9oxwCV+uw3u2fsEWOK3u0TSDvXXaTnupfNrH97Ln88X/lrenSOvDv76rh8JmwYMzns+RVyU1E2Be3PNAC6O3BQfAVviLJ5mwIPALbgHpT3wMnCiTz8cZwF1xVkzk8nxwOPcaH4M7Ih7Q/YCuue4UTvjHpr9cc1F/f3vjXz8C8A1uDf6z/xFzvXAN/EX/Bd++2CcJd28iAd+AM5qb5on3YXUrsiPxVnr6wDXAq9H4mqAn0ZuoO399p+Am/31bgb8FO8ALSOvg4B3MsLeA/7SCHW9DrgpI+xt4NAidL0cmFog3RTSFXkvX9Z1gI1wL+hrfdxmwHygk//dA+gZOZ+j/XYLYJc1uUcbWNtURV4pbbNcn9OAFwvpGjm/eQXSjiFdkW8IHAqsj3tu7wEe9HHNcS/hzfzvjnjDDLgTONef67rA7nnyuwP3wmwC/AS3yEdOI0K1+Ip8Be6tOQ+4EVgvclP8MZK2A/B1iPdhg4HJfvs/wPBI3D7kfuAnAiPylCl6U4wCxmWkmYhzkN8NV7k2j8Tdke+mwFmsK/x+XwIHFHGdXgF+DSwskO7CXHnjPucU2MD//gg4EWiVke6POMf/vQrktaHXbTDugR2Cs4xuaWy64izOyzPC/gsMLULXW4G7CqSbQsQyzYg7GHjNb/fCPZh7A80y0j0DXAS0K+J+y3mPNrC20Yq8Ys9sJN3WuK/UnxbS1f8/l8KV/hh8RZ4lbltgid9u7q/5odHr6eP+iWsq6VLEORyE+yL7zv+dUGifYketHKyqrVW1u6qerKqrInHRJay74yqMGhFZKiJLcZVGWHCvU0b6eVnyesb/7wp8kCU+G92BQSFPn+/uuDdiJ9yFXlkgXwBEZG/gCtyK4msDPwf+LiLbFijDaJxF0a7YHn0RaSIil4vIByKyDHezg2s6AXdD7A/ME5GpIvITH34lrg3/Sd8ZUqvtM6Cqn+HaUU/H3RgDgKdx1hs0Il1xFVurjLBWFG6zDbp2LLLMiEh7EblLRD72uo7Ha6qq7+MsxguBxT5dJ7/rccCmwCwRmSYiB+Y4fjH3aENpG203rpS2AIhIL+Bx3Mvk2QLJR/v/ddV2fRG5RUTmeW2fAVqLSBNf3sNxVn6NiDwqIpv7XUfivlJeFpEZInJsjuNvDtwNHIPTdktgpIgckK9cpRh+qJHt+bi3ezt/E7VW1VaquqWPr8GJHejG6oQHfj6urbFQniHtuEierVW1uape7vNsIyLNC+Qb2BZ4RlVfUdUfVHUabpXxvfPsg6qOxn0OfoWzwIrhSFxFuzeunbKHDxd/zGmqOhD3UD0I/MuHL1fVM1R1E9zb+3QRyboogKpOVdUdVbUtbmWYzXCfzoVImq4zgG3CD79fTx+eE6/r08BOItIlX9oIf/LnsrWqtgKOIrKYg6reoaq74yozBf7sw99T1cE4vf8M3JtxfoE1ukejpxXZrq+2NRnHqoS2iEh3nE4Xq+q4fGkhpSvAJKCLiBTrV/wM3DO0s9f2Z6EI/rgTVbU/7uUwC/c1h6ouVNUTVLUT7iv7Rv/iyaQvMNsf5wdVnQ08CuyXr1AlHUeuqjXAk8DVItJKRNYSkZ4i8nOf5F/AqSLSRUTakNGLnsHfgTNF5Mfi6OXFAmddbhJJOx44SET29VbuuiLST0S6qOo83OfxRSKyth9+d1CefKcBPw3WjYhsh2uDftP/7ic5HOqr6hfA+cANInKwf3s3E5H9ROSKLLu0xD1En+Ha3C4LEb6svxaRDVT1W1zb2/c+7kB/PSQS/n22MonIdr4MrXCdVgtUdWKe8892XknQ9QGgr4gcKiLr4nR6U1Vn+et0oYhMyXH+T+M63B7w5W4qIi1FZHgOy6olvmlD3KiR34cIEdlM3PCydXAv/VWkdT1KRDZSN4pkqd8lm65579G6kARt/TX+D3CDqt6cJX6oiMzNcf7v4Zqe7vT5r+3LcoRk/9JtidNsqbhRYRdE8ukgIr/wL6CvcfdA0HZQxBBYgnuxZdP2NaC3v0dERHriBi3kHrHiT6RQe81cIm1bGXFTyGgXxFmWN+E+37/wBTvCxzXFdbR9huvRzTa6IdoDPhzXq78C1zG1nQ8fiGs/Xkp6JMfOuA6gz3E9yo8C3XzcJriRHSsoogccOAXXdLEc1wN9RiTuaCKjH3Ls/2vcjbgSNyLlUWBXH3dhyBvXofWQz2ce7nNKce2oa+OGli3BVdbT8B0kwO+8Liv9dT4vT1nu9Dp8gftka9+Idd0bZyWt8mXqEYn7B3Bpnn3XxrVfv++v+zxcxdUt8xxxn8PTfblex1lxCzTdhvuy1/xz3KiH0PE5Htd+vgL3pXDwGt6jjUpbXGWqPm3qLxJ/HjAhz7UUYIS/5l/iOmzvJt1ROYZ0Z2cnf84rcCPATgzXA2eFh5EpS326Pn6/K/xxV+Can4blKc9h/tot95r8mSyj0qJ/1bbU2wDc6IImwN/VfWZVFSJyJ9AH92D/AIxW1ev82/luXPPIXOAwVV1SqXJWEzHRdQauQmmH6VoUcdAVQESm4kaKtCKh2lZNRS4iTXBvuP64t1AYO/lORQuWgYh0BDqq6qsi0hJneR2MGz/6uape7j/J2qjqqAoWtSowXZNJXHSFxqFtNTnN2gl4X1XnqOo3wF24z7GqQlVrVPVVv70cN/mhM66sY32ysRTf4Zl0TNdkEgtdoXFoW9aKXEQGiMhsEXk/R8dBlM7UHua0wIdVLSLSA9gON2Kgg7qOo9CB1D73nvGnDtqarjEiybpCcrUtW0XuP71uwA2b6QMMFpE++XbJElYd7T5ZEOeE6D7gNFVdVunyNCR11NZ0jQlJ1hUSrm2+ntD6/OGmlk6M/D4bOLtAem3kf5+US49KaWu6mq4J/qsaXcvpUzjbp9fOmYnEuWgs6KaxkVBw9lqVUFBb07UWpmsyqRpdy1mRF/XppW6G1WgAyTHRxqg6CmprusYS0zWmlLOzcwG1p/Z2YXV/vkY8MW2TiekaU8pZkU/DTTXdWETWBo7ALXJgxB/TNpmYrjGlbE0rqvqdiJyCc03ZBLhNVfM6KDLigWmbTEzX+FI1MzvB2tyA6aparBe22GC6mq4JpWp0tZWwjUbDj3/849T2U089BcDSpc7J4IABAwB49913G75ghlFPqmmKvmEYhrEGmEVuJJb1118fgFtuuQWAAw5IL7LSqlWrWv/vueceALbZZhuMePDDDz8AcP/996fCRNwIynfecb67zjvvvIYvWAUwi9wwDCPmmEWehT/84Q+p7T/+8Y9A+k3/ySefALDnnnum0rz99tsNWDojH3379k1t33777QBst912QFpDgMxO/ilTppS/cEZJCRoefHDaaWHQeOBA54jx1VdfBeCBBx5o4NI1LGaRG4ZhxByryA3DMGJOox1H3qtXegHrkSNHAnDMMccA0LRpusUp+jke5YMPPkhtb7rppqUqVtWMSy0lDaFrp06dgHRTGMDQoUMzy5Hazrzvv/zySwBOOumkVNiECRNKVTzTtQwMG7a6765LLrkEgA033BBIN63suOOO5ShC1ehqFrlhGEbMaXSdncF6/v3vf58KO/bYY3OmX7RoEQArV64EYJNNNqn1H+CGG24A4Le//W1pC2sUzVlnucVsMq3wYglDFceOHZsK23777QF44403APjnP/9ZjxIapWb06NGrhQXNjj/++IYuTkUxi9wwDCPmNDqL/K677gKyT/wIQ5SmTZuWCrv55puBtKUWLPFoe+svfvELwCzyShCm3R911FFA7j4NgLXWStstYTJJPk4//XQA7r77bsAs8jgR7oNnn322wiVpGMwiNwzDiDlWkRuGYcScejetiMhtwIHAYlXt68PaAncDPYC5wGGquqS+edWHMNzwRz/60WpxkydPBuDII48E4JtvvqnTsadPn17P0lUncdD2lFNOAdI+U7INp3399deB9Gw/gK233hqA3/3ud0DtmbqB0PwSfLT85je/ScWFWaNxJA66rimHHHIIkL4Pon5YkkwpLPIxwICMsLOASaraG5jkfxvxYwymbRIZg+maKOptkavqMyLSIyN4INDPb48FpgCj6ptXfQiTBzp06ADAggULUnFnnnkmUDdLfMWKFanta665phRFrDrioG2YxJXNEn/55ZcBOPTQQwGoqalJxX388ccALFnijM5sFnkgTBZauHBhCUpceeKg65oSBiyccMIJADz33HOVLE6DUa5RKx1UtQZAVWtEpH2uhCIyDFh9ipZRrRSlrekaO0zXGFPx4YeqOhoYDeWZ8vurX/0KgBEjRtQKD1N3Id2GGmjXrl1qOwxry7TYot7ynnnmmZKUNUmUW9di+Mc//gHAqlWrAGjZsmUqrnXr1kBxE0f+85//APD444+Xuoixoxp0DWy00UYAnH322amw0EYe/JE3Fso1amWRiHQE8P8Xlykfo+ExbZOJ6RpjymWRPwwMAS73/x8qUz4F2WCDDYDajrCg9uiV6GgGgC222CK1femll9aKC23jSW0XL4Kq0bYQV111FQDDhw8Havdr/PSnPy36OA8//HBpC1adVLWu3bt3T22HNQHC13L0azv0ZwwaNKgBS1d56m2Ri8idwAvAZiKyQESOw90M/UXkPaC//23EDNM2mZiuyaMUo1YG54jaq77HNiqLaZtMTNfkUfHOznIThhmGT67g5W6nnXZKpanLpIEwbG3q1KmlKqKxBlx99dVA2n9KNt8pYZJQ8IhXV18rv/zlLwF46KGqamVolIShpJD2gRM8XkaHnl522WUAzJo1qwFLV3lsir5hGEbMSbxFPnHiRABeeuklAPbYY4+caefMmQPU9jWeya233lrC0hl14aabbkptDx7sWgeCZV3MSldRK7yY9GaJV54wnDAMNQQ455xzaoXNnDkzFfenP/2pAUtXPZhFbhiGEXMSb5EHwlClYNWFdlOAefPmAek3/XXXXZeK23bbbWsdZ/78+WUtp7E6ffv2BdJT7QFatGiRNe3y5ctT26NGuRnmYehaaFMtlmDdXXDBBUDdnakZdWfzzTcH0lpnawe/7777AOjTpw8ABx98cCru3HPPBVYfNpx0zCI3DMOIOVaRG4ZhxJxG07QSPNeFzpPgnxzg/fffB9LD1b7//vvV9v/www+BxuNNrZo4+eSTAWjbtm3ONC+88AIA559/fios+JkPRH2tnHjiiQA0adIk5zFHjhwJwLJly4DG25FWbqKzNkOTSHhOgx+jHj16pNLccccdADRv3hyo3dl58cUXAzB37lwAJkyYUJ5CVxlmkRuGYcScRmORZxKs8ChhJZiwoG+29EnxSR0HQkfzQQcdVDDtjTfeCKxuhUc59dRTU9vBv07nzp0LHjusJmSUh+ii1rvtthuQ9qcSJv989NFHqTSffvopkJ7cF7XIgz/yMHAhTAQM4UnFLHLDMIyYk0iLvE2bNqnt4AEv+Bz/29/+lnO/0BabjWDxGQ3HpptuCkCnTp1ypnnttdcAeOyxx1aLC23iwed41GNlMVP0w5R+8zdfHsKEnqgnynCt+/XrV3D/YG1HCesMhHUIFi923njDEONomiRhFrlhGEbMSaRFfsQRR6S2hw4dCuTvvQ5tbeuuu25Zy2XUjTAJJN90+t69ewPp0QpR3/LBou7YsSNQ9yn6Xbp0AaxfpFxkrngPpW/LPvroo4H05CEwizwrItJVRCaLyEwRmSEiI3x4WxF5SkTe8//bFDqWUT2YrsnEdE0mpWha+Q44Q1W3AHYBfisifYCzgEmq2huY5H8b8cF0TSamawIpxcISNUBYfXu5iMwEOgMDgX4+2VhgCjCqvvmtKbvssguQXuLtiy++SMVdf/31QG3/K4Fvv/0WgK+++qrcRawq4qJr8LmSr6O6GL7++msALrroolRY8D2fJKpJ1zDE8LPPPkuFDRs2DEgPN1zTppbQbBPWGog2q40fP36NjlnNlLSNXER6ANsBLwEd/E2DqtaISPsc+wwDhpWyHEZpMV2TiemaHEpWkYtIC+A+4DRVXSYiRe2nqqOB0f4YhXugiiBqPYc3cc+ePYH0yjLRIYr77rtvrf2/++671HZYCejJJ58sRdFiRyV1DdPu33vvPSDdsbmmhNWiYPXhh6NHjwbgiiuuqFcecaEantdgbUen6B933HFAepJQWPGnWPcIwfth8JoYdE66N8SSDD8UkWa4m2KCqoZ10xaJSEcf3xFYXIq8jIbDdE0mpmvykGKGYeU9gHuVjwU+V9XTIuFXAp+p6uUichbQVlVHFjhWSSzyKGHVn+hbvxDRad577713qYuUj+mqukNDZpiLatI1+BXPZlUFSzLbfTx27FgA3nrrLQCuvfba+hSjPpiuRRL8kT/xxBMA7LCDu2xhWn42xo0bt9r+7dq1A9JT/Ms0Rb9qdC1F08puwNHAWyLyug87B7gc+JeIHAd8BAwqQV5Gw2G6JhPTNYGUYtTKc0CuBra96nt8ozKYrsnEdE0m9W5aKSXl+FQLnSehMysfs2fPBmDAgAGpsKjXtQagaj7VSkk5dI0Zpmsd2X333QGYNWsWkL1pJTSjTJs2LRUWOkfDIun5mmRKQNXoar5WDMMwYk7iLfLg0zr4Gh8xYgQAd955ZypN8JAWOseiw9QamKp5w5cSs8hN14RSNbqaRW4YhhFzEm+Rx4yqecOXEtPVdE0oVaOrWeSGYRgxxypywzCMmGMVuWEYRsyxitwwDCPmWEVuGIYRc6wiNwzDiDnVtvjyp8BK/z9utKP+5S7eRWO8MF2TielaJVTVOHIAEXmlWsZm1oW4lruhiOv1iWu5G4q4Xp+4ljsX1rRiGIYRc6wiNwzDiDnVWJEX9jdbncS13A1FXK9PXMvdUMT1+sS13FmpujZywzAMo25Uo0VuGIZh1AGryA3DMGJOVVXkIjJARGaLyPt+Je+qQ0S6ishkEZkpIjNEZIQPbysiT4nIe/5/m0qXtVowXZNJHHSFxqFt1bSRi0gT4F2gP7AAmAYMVtV3KlqwDESkI9BRVV8VkZbAdOBgYCjwuape7m/qNqo6qoJFrQpM12QSF12hcWhbTRb5TsD7qjpHVb8B7gIGVrhMq6GqNar6qt9eDswEOuPKOtYnG4u7UQzTNanEQldoHNpWU0XeGZgf+b3Ah1UtItID2A54CeigqjXgbhygfeVKVlWYrskkdrpCcrWtpopcsoRVR7tPFkSkBXAfcJqqLqt0eaoY0zWZxEpXSLa21VSRLwC6Rn53Af5XobLkRUSa4W6ICap6vw9e5NviQpvc4kqVr8owXZNJbHSF5GtbTRX5NKC3iGwsImsDRwAPV7hMqyEiAvwDmKmq10SiHgaG+O0hwEMNXbYqxXRNJrHQFRqHtlUzagVARPYHrgWaALep6qUVLtJqiMjuwLPAW8APPvgcXJvbv4BuwEfAIFX9vCKFrDJM12QSB12hcWhbVRW5YRiGUXeqqWnFMAzDWAOsIjcMw4g5VpEbhmHEHKvIDcMwYo5V5IZhGDHHKnLDMIyYYxW5YRhGzPl/p9Dz+7e9ic4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "for i, correct in enumerate(correct_indices[:9]):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.imshow(X_test[correct].reshape(28,28), cmap='gray', interpolation='none')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], y_test[correct]))\n",
    "    \n",
    "plt.figure()\n",
    "for i, incorrect in enumerate(incorrect_indices[:9]):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.imshow(X_test[incorrect].reshape(28,28), cmap='gray', interpolation='none')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], y_test[incorrect]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}