TensorFlow_mnist_model1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, models\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get MNIST Data. \n",
    "### MNIST data loacted in tensorflow > keras > datasets > mnist \n",
    "### Split data to (train images, train labels) and (test images, test labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = keras.datasets.mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are Total 60000 Train images and Train labels. (6000 images for single class)\n",
    "### Shape of single image is 28 x 28 (pixel)\n",
    "### "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Train images : (60000, 28, 28)\n",
      "Shape of Train labels :  (60000,)\n",
      "\n",
      "Shape of Test images :  (10000, 28, 28)\n",
      "Shape of Test labels :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Shape of Train images :',train_images.shape)\n",
    "print('Shape of Train labels : ', train_labels.shape)\n",
    "print('\\nShape of Test images : ', test_images.shape)\n",
    "print(\"Shape of Test labels : \",test_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train labels :  [5 0 4 ... 5 6 8]\n"
     ]
    }
   ],
   "source": [
    "print('Train labels : ',train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot first train image. \n",
    "### when value is close to 0 : dark \n",
    "### when value is close to 255 : white"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  51 159 253\n",
      "  159  50   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  48 238 252 252\n",
      "  252 237   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0  54 227 253 252 239\n",
      "  233 252  57   6   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0  10  60 224 252 253 252 202\n",
      "   84 252 253 122   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0 163 252 252 252 253 252 252\n",
      "   96 189 253 167   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0  51 238 253 253 190 114 253 228\n",
      "   47  79 255 168   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0  48 238 252 252 179  12  75 121  21\n",
      "    0   0 253 243  50   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0  38 165 253 233 208  84   0   0   0   0\n",
      "    0   0 253 252 165   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   7 178 252 240  71  19  28   0   0   0   0\n",
      "    0   0 253 252 195   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  57 252 252  63   0   0   0   0   0   0   0\n",
      "    0   0 253 252 195   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0 198 253 190   0   0   0   0   0   0   0   0\n",
      "    0   0 255 253 196   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  76 246 252 112   0   0   0   0   0   0   0   0\n",
      "    0   0 253 252 148   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 230  25   0   0   0   0   0   0   0   0\n",
      "    7 135 253 186  12   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 223   0   0   0   0   0   0   0   0   7\n",
      "  131 252 225  71   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 145   0   0   0   0   0   0   0  48 165\n",
      "  252 173   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  86 253 225   0   0   0   0   0   0 114 238 253\n",
      "  162   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 249 146  48  29  85 178 225 253 223 167\n",
      "   56   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  85 252 252 252 229 215 252 252 252 196 130   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0  28 199 252 252 253 252 252 233 145   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0  25 128 252 253 252 141  37   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]\n",
      " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
      "    0   0   0   0   0   0   0   0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(train_images[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot First 10 Train images and Corresponding labels  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Train images in MNIST dataset\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train labels match with Train label sequentialy\n",
      " [5 0 4 1 9 2 1 3 1 4]\n"
     ]
    }
   ],
   "source": [
    "print('First 10 Train images in MNIST dataset\\n')\n",
    "for i in range(10):\n",
    "    plt.subplot(1, 10, i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.imshow(train_images[i])\n",
    "plt.show()\n",
    "print('\\nTrain labels match with Train label sequentialy\\n',train_labels[:10])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Important\n",
    "### Change data shape (60000 x 28 x 28) to (60000 x 28 x 28 x 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = tf.reshape(train_images, [60000, 28, 28, 1])\n",
    "test_images = tf.reshape(test_images, [10000, 28, 28, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MODEL 1 : 3 Layers with 1 Convolution layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "            keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (28, 28,1)),  # layer 1 \n",
    "            keras.layers.MaxPool2D((2,2)),                                                  # layer 2 \n",
    "            keras.layers.Flatten(),\n",
    "            keras.layers.Dense(10, activation = 'softmax')])                                # layer 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If you want to see information of model, model.summary() will help\n",
    "### summary() is also built in function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 5408)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                54090     \n",
      "=================================================================\n",
      "Total params: 54,410\n",
      "Trainable params: 54,410\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Components in training step \n",
    "### Optimizer, Loss function, accuracy metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer = 'adam',\n",
    "    loss = 'sparse_categorical_crossentropy',\n",
    "    metrics = ['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Step \n",
    "## Training for 5 epochs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 19s 316us/sample - loss: 0.5727 - accuracy: 0.9422\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 19s 315us/sample - loss: 0.0830 - accuracy: 0.9754\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 18s 295us/sample - loss: 0.0701 - accuracy: 0.9788\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 18s 296us/sample - loss: 0.0598 - accuracy: 0.9819\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 19s 320us/sample - loss: 0.0507 - accuracy: 0.9848\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fb031dcc438>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels,  epochs = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Step \n",
    "## Perform Test with Test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/1 - 1s - loss: 0.0778 - accuracy: 0.9666\n",
      "\n",
      "Test loss :  0.15512693734746572\n",
      "Test accuracy : 0.9666\n"
     ]
    }
   ],
   "source": [
    "test_loss, accuracy = model.evaluate(test_images, test_labels, verbose = 2)\n",
    "print('\\nTest loss : ', test_loss)\n",
    "print('Test accuracy :', accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Before prediction, change test image's type to float 32. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images = tf.cast(test_images, tf.float32)\n",
    "pred = model.predict(test_images)\n",
    "Number = [0,1,2,3,4,5,6,7,8,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction :  (10000, 10)\n",
      "Test labels :  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print('Prediction : ', pred.shape)\n",
    "print('Test labels : ', test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions for plot images, probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(Number[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                Number[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  plt.xticks(Number)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 1\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, pred, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, pred,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+onPaWdLXZkr/tLVE0o+K3vDS2EnSfDN718y+U2q0+eB4BTPLY1TAFptZNKJnGUf0LOXLaLVJtCRuaOyc6yTpQEkja/oDOedaSxog6a7ouN+Z2f9ybBJdGbBMN8BuLKm5c66xUqMUH6WVbyfpdTP72sxWS3pR0uCatr3cnFMrSf+SdI6ZPq/LYxUxL7eXdJ2k36YVhZqX1eakVH1eFisnpYrLywaVk6VUrJzMIdSclDhXShV2rix7R1apTzVTzfRJgdt9KGnz2Hqn6LUPozj9dc859ZbkJM2TdLiZjpC0pXPaKu0Y30haZ1hc2UeQm0XbhCjbp/Yac85VKcuIXnQpZLpSf9CeNrP0OjdJGqp1L5PFmaSnnHNTnHMnZyjfQqk/rvdEl11GOuda5thfxvfVzD6UdIOkDxSNUJjZU2nVZkva3Tm3sXOuhaQDlPz9DIZzaqJUJ/afZnqkwM3LkpfOqZNSowPHmmlBWv1Q87LSclKqkLxsaDlZS+U8V2YTak5KlZeX+eSklDsvgz5XVkJH9igVPq1AkiZK2s85tXVObSXtJ2mimRZL+tw57RLN8TlW0r/Ttr1S0sVKfbppFL32vVKfHuLekVQVf8E5rSfpCCXnxyo61maSFtbgZ6l3nHOxET1bZ0TPzNZElyg6SdoputywdtuDJC0xsynVHGY3M+uj1Ieh051zA9LKG0vqI+k2M+st6StJ2eYZZr0y4Jxrq9Qn7i2UGkVo6Zz7VdrPM1ep0cCnlJonNl3SmmraX3Gi3+O7JM0104012EXJ89I5tZH0mKQ/mun/Mvw85KVql5PR9hWTlw0pJ4ugLOfKbMjJpBKdK6XceRn0ubKsHVnn1FLSvlL2UR/ntKNzWiTpcEl3OKc5kmSmz5RKsjej5YroNUk6Talh9vmSFkh6Ira/QyRNNtNHZvqfpOnOaZakZmaaET+2mb6StMA5xedvDJD0XzO9m9bUvpJeM9Pqgv4TKke2T+0Fc87FRvQs54hedPnieSXnE/1E0iDn3EKlPjDs5ZwblWHbD6N/lyg1GrdTWpVFkhbFPsGOUSpZM4muDFimKwP7SHrPzD41s1VK/b7umqE9d5lZXzMbIGm5Un/cQ/MTScdI2ss5TY+WA9IrVVheniGpq1KXP9e2eZOoLOS8rKSclCorLxtSTso5PSDpVUnbOKdFzumEDHUqKSflnBZKulHSr6M2r53/GXJOSpWVl3nlZLR9rrwM+1xpFXDrhEpeJDtUsqvyqHezZHuXu701/znVWNK7Sn2SaipphqTtM9SrkpTjFjBySn379aYcddpLahPFzSVNknRQlrp7SJqQ4fWWkjaIxa9IGpih3iRJ20TxZZKuz3Kc0ZKOz1K2s1Lfhm8R/Xz3SjozQ71Non87S3p77c/IUhe/r/U/L/PNyahu1rwsdk5Gdcqal+Rk5S0NISdT7Q/rXBmVVZuXIZ8ry/5LEcIi2Yl51Dmp3O2s/c+pA5T6ZLRA0jr3+VNqCshipW7HskjSCRnq7KbUXJyZSl0ymC7pgLQ6PZT6RuxMpebLXJKjTdlOmD+O/oDMiBIn430Jlfqm5+ToWGMlrXMj7iixl0lqnaMdl0cJN1vSPyStn6HOJElvRW0K9g91KEtDyMvqcjKqkzMvi52TUf2y5yU5WXlLQ8jJ1M8QzrkyKqs2L0M+V0ZP8QAAAADCUglf9gIAAAAKRkcWAAAAQaIjCwAAgCA1LqRyu3btrKqqqo6agkwWLlyopUuXumLuM/19nDFDWp3lRiiNG0s9exbz6A3blClTlppZ+2Ltj5wsj4b2Ptb3vxEN7f2sryrhfazvuVIqhbyXBXVkq6qqNHny5Jq1CjXSr1+2R5XXXPr76HJ0k1evlnjLi8c5l+mRfjVGTpZHQ3sf6/vfiIb2ftZXlfA+1vdcKZVC3kumFgAAACBIdGQBAAAQJDqyAAAACBIdWQAAAASJjiwAAACCREcWAAAAQaIjCwAAgCDRkQUAAECQ6MgCAAAgSHRkAQAAEKSCHlFb6b766qvE+vnnn+/j22+/3cfpj319+OGHfdylS5c6ah0AAACKiRFZAAAABImOLAAAAIJERxYAAABBqldzZD/66KPE+p133unjRo0a+Xjy5MmJeuPHj/fxGWecUUetA+qvqVOn+njw4ME+XrhwYZ0e96mnnvLxdttt5+PNN9+8To8LNETxc+WgQYN8fOuttybqnXrqqT6On3uBusCILAAAAIJERxYAAABBCn5qwaeffurj4447rowtARquiRMn+njlypUlO+64ceN8fPfdd/t49OjRJWsDUF8tW7YssR6fMhB35plnJtZPOOEEHzdv3rz4DQNiGJEFAABAkOjIAgAAIEhBTi245ZZbfDx27Fgfv/nmmzXa36RJk3xsZj7u2bNnot6AAQNqtH+gvlm9enVi/fHHHy9LO+JP6bvxxht9nP6Uv5YtW5asTUB98dJLLyXWP/zww4z1jjrqqMR6s2bN6qxNQDpGZAEAABAkOrIAAAAIEh1ZAAAABCnIObLnnHOOj4vx1JBHHnkkY9y5c+dEvYceesjHffv2rfVxgVA9//zzifVXXnnFx3/4wx9K1o7PPvvMx3PmzPHx119/najHHFkgP/Hb51111VV5bXPMMcck1p1zRW0TkAsjsgAAAAgSHVkAAAAEKYipBQcccEBiPX6LrDVr1hS8v3bt2iXW45cd33//fR+/9957iXo77rijj7///vuCjwuEbNasWT4+8sgjE2Vdu3b18YUXXliyNsWf7AWg9mbOnOnjqVOnZq3XuPEP3Yf999+/TtsE5MKILAAAAIJERxYAAABBoiMLAACAIFXsHNkXX3zRx2+//XaiLH5rj3xvv3XKKaf4eL/99kuUtW7d2sfPPfecj6+++uqs+7vtttt8fOqpp+bVBiBk8XxIv73VqFGjfNyqVas6a0P8dltS8u8Et/wBai9+C8pc9t133zpuCZAfRmQBAAAQJDqyAAAACFLFTC1YuHBhYj1+e5+lS5fmtY/0J3ENGTLEx5deeqmPW7RokXUfXbp08fEdd9yRKIu3Y+jQoT7+9ttvE/XOOOMMHzdp0qS6ZgMVacyYMYn1xx9/3Mfx221JyVvT1aX0Jw3FpxPssccePm7Tpk1J2gPUN/HpOumaNm3q42uuuaYUzQGqxYgsAAAAgkRHFgAAAEGqmKkFq1atSqznO51gwIABPn7wwQcTZelP8MpHfGpB+hOKzjvvPB9/9dVXPo5PM5CkQYMG+XjLLbcsuA1AJXj44YcT6/Hf+VLeqSM+7ej+++9PlMWfLnTRRRf5mCk9QP5eeeUVH7/66qtZ68Wn5fXq1atO2wTkixFZAAAABImOLAAAAIJERxYAAABBqpg5soWI3+rnnnvu8XFN5sTmEp/rKkn//Oc/ffzGG28U9VhAJVixYoWPX3vttaz1TjvttFI0R5I0YsQIH3/66aeJsm7duvl4r732KlmbgPrkzTffzKseT7FEJWJEFgAAAEGiIwsAAIAgVezUgjVr1mQte/3110vSBjNLrH///fcZy9LbGn+K2KhRo+qodUDxrVy50seLFi1KlB111FGlbo4kacGCBVnLunfvXsKWAPVTtqkF6U/IK+WUIiBfjMgCAAAgSHRkAQAAEKSKmVpw++23J9YbNWpUppb8YPz48Yn1adOm+dg55+P0tl5++eV12zCgjmywwQY+Tn9yz6xZs3z82WefJco22mijorZjyZIlPk5/wljcT37yk6IeF2gIXn755cR6+hPz1mrdunVivVOnTnXWJqCmGJEFAABAkOjIAgAAIEh0ZAEAABCkipkjO2HChLIdO/60oLfeesvH11xzTV7bpz9RrEmTJsVpGFBizZs393HXrl0TZWPGjPHxgQcemCg777zzCj7W7NmzfZx+i63333/fx/H56OnWW4/P4kChli1bllhPv9XkWvvuu28pmgPUCmcBAAAABImOLAAAAIJUMVMLyunqq6/28fDhw/Papqqqysf33ntvoqxz585FaRdQTpdddlliPX75MX0q0JFHHlnw/tu3b+/j9OkDS5cuzWsfxx9/fMHHBRq6XLe0iz/N6+STTy5Fc4BaYUQWAAAAQaIjCwAAgCA1yKkFBxxwQGL97bffLngf3bp18/Huu+9e6zYBlWa77bZLrD/00EM+jj/lTlr3rgP5GDJkSNay4447zsejRo3KWi9+lwUA2S1atMjH2Z7kJSWf3rXjjjvWaZuAYmBEFgAAAEGiIwsAAIAg0ZEFAABAkCpmjmz6k0XWrFmTte4TTzyR8fWTTjopsf7RRx/ldaxcTw7KppxPIgPKrXfv3jnXa+vHP/5xXvVmzZrl4x122KGobQDqk1deecXH2Z7kJUkHH3xwKZoDFA0jsgAAAAgSHVkAAAAEqWKmFpx66qmJ9aFDh2ate+CBB/q4UaNGWetlK0uftpBrH3GnnHJKXvUA1E780meuy6BMJwDys2zZsqxl7dq18/E555xTiuYARcOILAAAAIJERxYAAABBqpipBYMHD06sDxs2zMdLly6t02PHL6vEn2Z05513Jup16NChTtsBICV+J5Ga3FUEQNLEiROzlm2++eY+bt26dSmaAxQNI7IAAAAIEh1ZAAAABImOLAAAAIJUMXNku3Tpklh/8MEHfTx27NhE2U033VTUY//pT3/y8RlnnFHUfQMo3Lfffpvx9ebNm5e4JUC4Vq1a5eP58+dnrdesWTMfN2nSpE7bBBQbI7IAAAAIEh1ZAAAABKliphakGzBgQMZYkvbbbz8fjxgxwsfjx49P1Pv5z3/u49/+9rc+Tn9SULdu3WrXWABFdc899/i4TZs2Pr7kkkvK0RwgSOut98NY1Y477ujjOXPmJOpttdVWJWsTUGyMyAIAACBIdGQBAAAQJDqyAAAACFLFzpHNZeDAgRljAPVDfD7fueee6+O99tqrHM0BgtSoUSMfX3311T5Of+xznz59StYmoNgYkQUAAECQ6MgCAAAgSEFOLQBQv6XfSg9A7fzoRz/y8d13313GlgDFxYgsAAAAgkRHFgAAAEGiIwsAAIAg0ZEFAABAkOjIAgAAIEh0ZAEAABAkOrIAAAAIEh1ZAAAABImOLAAAAILkzCz/ys59Kun9umsOMuhiZu2LucNq3sd2kpZWs4uGWqcY+yjq+0lOlk2p38f6lgeVVoe8rB8qLS8r6Xe80uoULScL6sii/nPOTTazftSpu+MAhaqPeVBpdYBCNdRcKdXfkXwxtQAAAABBoiMLAACAINGRRboR1Knz4wCFqo95UGl1gEI11Fwp1d+R/JhZ2RbJBko2T7L5kv0xS53DJZsj2feS9UsruyDadp5kP6tuv5L9U7KZkl0Te+0iyQ7J0cbekt0VxU6yW6L9zpSsT/R6e8meLOf/JQtLMRfJGkk2TbIJWcorJi9jr+0o2WrJhkTr5CVLvVgku1uyJZLNzlFngGRT4zkQKztOsv9Ey3Gx1/tKNivKyVski743Y9dFOXlfrO6vJDsnx/E7rP17IdnGkj0v2ZeS/S2t3jOStS33/ylL/VnKNiLrnBpJGi5pf0ndJB3lnLplqDpb0mBJL6Vt303SkZK2lzRQ0v9zTo2y7dc59ZD0jZl6SNrRObV2Th0k7WymsTmaeqGkW6J4f0lbRcvJkm6TJDN9Kmmxc/pJof8PQIU6W9LcHOWVlJdr/55cJ+mpta+Rl6hH/q5UPuXygaRfS7o//qJz2kjSpZJ2lrSTpEudU9uo+DZJJ+mH89pA59RaUp8oJ79zTjs4p+aSjlcqh7M5T9KdUfytpIsl/T5DvX9IOq2anwXIWzmnFuwkab6Z3jXTd5JGSzo4vZKZ5pppXobtD5Y02kwrzfSepPnRPrPtd5Wk5s5pPUlNJK2RdIVSCZ6Rc9pAUg8zzYgd877oQ8BrktpEJ11JGivp6AL/D4CK45w6STpQ0shsdSosLyXpTEn/krQkrTp5ieCZ6SVJn1VTZ6GZZkr6Pq3oZ5KeNtNnZlou6WmlOqwdJG1optfMZJLuk3RItH0T5+QktVAqR38v6VYzrcrRhMMkPRm15SszvaxUhzbdOElH5f6JgfyVsyPbUdJ/Y+uLotdqu33G1800V9KnkqZKGi+pq6T1zDQ1xzH6KTVeP1aJAAAgAElEQVTylE+bJ0vavYD2Vxzn3EDn3Dzn3Hzn3B8zlN/tnFvinJudafuozubOueedc2855+Y4587OUKeZc+4N59yMqM7lWfbVyDk3zTk3IUv5QufcLOfcdOfc5Cx12jjnxjjn3nbOzXXO9U8r3ybafu3yuXPunAz7OTdq62zn3APOuWYZ6pwdlc/JtI+A3CRpqNY9Ieaj5HnpnDpKOlTRFZI0QedldTkZ1cmZl8XMyahuReRlA8vJ2siVk4vSXzfTF5IelzRN0mJJK1TNFRLntIWk5WZaWV1jos70+s5p40J/kEoR2rkyqpMzL0M+VzaoL3uZ6Rwz9TLTXyRdKeli5/Qn5/SQczopwyYdlDrJ5mOJpB8Vq62l5pzLcOnXpU/1+Luqv7y1WtLvzKybpF0knZ5hPysl7WVmPSX1kjTQObdLhn1Vd3lbkvY0s16W/X50N0t60sy2ldQzfX9mNi/avpekvpK+lvRovI5zrqOksyT1M7Pukhopdfk8Xqe7UpfodoqOc5Bzrms1ba84zukgSUvMNKVUxyxCXt4k6Q9mGTveweZlnjkpVZ+XxcxJqQLysiHlZDmYaViUk79TKicvcU4nRjl5UYZNCjlXSvU/L/+uyjtXSrnzMthzZTk7sh9K2jy23il6rbbbV7tf53SwpCmSWkna0kxHSBrinFqkHeMbSfFPE7n23SyqH6ro0q+9a2YZp3qYWR6Xt2yxmU2N4i+USoaOaXXMzL6MVptES+LJHM65ai9vV8c511rSAEl3Rcf9zsz+l2OTvSUtMLNMT3JpLKm5c66xUpfbPkor307S62b2tZmtlvSiUnNIQ/MTSYOc00Klfgf2ck6jCti+HHnZT9LoqM1DlJqXe0hUFnJeVpuTUvV5WayclCouLxtKTtZWrpzslOF1zzn1luQkzZN0eJSTWzqnrdKOkZ6T1anXecm5srR5Wc6O7JuStnJOWzinpkr12scVsP04SUc6p/WjyxpbSXqjuv06pyaSzpE0TFJz/fBL0UhS07RjzFXqUmf8mMc6J+ecdpG0wkyLo7KtlZyGEJraTvVYh3OuSlJvSa9nKGvknJuu1Cfzp80svU4+l7dN0lPOuSnOuZMzlG+h1CjBPdFll5HOuZY59nekpAfWOYjZh5JuUOrLFIslrTCzp9KqzZa0u3NuY+dcC0kHKHnyCIKZLjBTJzNVKfX/8ZyZflXALkqel2bawkxVUZvHSDotdhk05LystJyUKiQvG1JOFsFESfs5p7bRl7z2kzQxOnd97px2iebDHivp32nbXqnUl7aaKJWLUuq9T/9w+Y6kqnwaEx1rM0kLC/9RKkKl5WW+U8Fy5WXQ58qydWTNtFrSGUol2VxJD5lpTno953Soc1okqb+kx5zTxGj7OZIekvSWUhPMTzfTmjz2e7qke830taSZklo4p1mSppgp8QnETG9Lah19uURKzRt6V6kvsNyp5Dcv95T0WI3/Q+oZ51wrpb58c46ZfZ5ebmZroksUnSTtFF1uWLttdHnbqru8vZuZ9VHqEs/pzrkBaeWNJfWRdJuZ9Zb0laRs8wybShok6eEMZW2V+sS9hVKXw1o65xKdOzObqx++Nf+kpOlKfXGpXqqwvMyFvIzUJiej7SsmLxtaTjqnByS9Kmkb57TIOZ2Qoc6OUU4eLukO51L5ZabPlOqQvhktV0SvSalz2EilzmkLJD0R298hkiab6aMoB6dHOdks7YuWMtNXkhY498MHzOgKyY2Sfh21ee1l876SXov+JjR4JTpXSrnzMuxzZV3d16u+LJKdK9mJedR7KeR74ynVIZkYW79A0gUZ6lVJynovw6hOE6U6LOfleexLJP0+tv5npT7lLpT0sVJzcUZVs4/L4vuIXttM0sLY+u6SHsuy/cGSnspSdriku2Lrx0r6f9W05xpJp5X7fa2vS0PIy3xzMirLmZe1zcnotYrJS3Ky8hbJDpXsqjzq3SzZ3uVub81/zrDPldF2l6XtJ+hzZYP6slcN3Sbl/iamc2ov6UZLfRszVNGlX7dF9Imr0KkekiTnnFNqns1cM7sxS532zrk2Udxc0r6S3l5bbmYXmFknM6uSv7xtv0rbR0vn3AZrY6UulyUuIZvZx5L+65zbJnppb6VGCjM5ShkulUQ+kLSLc65F9PPtrQwT651zm0T/dlZqzs/96XVQNA0hLysmJ6WKy0tyssKY6VHlN11gtpmerePm1KWKyct8cjLaNmdeBn+uLPenG5bKWZSaq/KOUpeY/pSh/AGl5r2sUupT4AkZ6uym1FycmUpdMpgu6YC0Oj2UurXLTKWS6ZIcbdpD0jpPl5L0Y0kzomVOpvZG9XopdQummUrdU3Sd0TlJLSUtk9Q6RzsuV+oPyGylbui9foY6k5RK/hmSgh1xYKmcpbqcjOrkzMti52RUv+x5SU6ylGsJ6VwZlVWblyGfK6PH0QEAAABhYWoBAAAAgkRHFgAAAEFqXEjldu3aWVVVVR01BZksXLhQS5cudcXcZyW/jzNmSKuz3JSlcWOpZ8/StqfYpkyZstTM2hdrf5X8XtZnvI+VrdC/I7yf9QPvY/1RyHtZUEe2qqpKkydnfHQ26ki/ftme8Fhzlfw+uhxd9tWrpQptdt6cc5mehFJjlfxe1me8j5Wt0L8jvJ/1A+9j/VHIe8nUAgAAAASJjiwAAEA9s9lmqasTmZbNNit364qHjiwAAEA988knNSsLDR1ZAAAABImOLAAAAIJERxYAAABBoiMLAACAINGRBQAAQJDoyAIAACBIdGQBAAAQJDqyAAAACFLjcjcAAACEZfny5T7+4IMP8tqmS5cuifW//vWvPu7evXuibOutt/Zxz549a9JENBCMyAIAACBIdGQBAAAQJDqyAAAACFIQc2SXLFmSWD/iiCN8vOuuu/r45JNPTtSrqqqq03attWLFisT6Sy+95OOBAwcmypo0aVKSNgEAUBsTJkxIrI8fP97HL7zwgo//85//5LW/bbbZJrG+cOFCH69cuTLrdt9//31e+0fDxIgsAAAAgkRHFgAAAEGq2KkF8Vt7bL/99omy+KX8TTfd1MelmkqQ3oY+ffokypYuXerjyZMnJ8q22mqrum0YUGaff/65j//4xz8myubMmePjZ555xsdMuQFKZ8GCBYn14cOH+3jEiBE+/uabbxL1zKxWx503b16ttgcyYUQWAAAAQaIjCwAAgCBVzNSC+OV4KXlngmXLliXKTj/9dB/feuutdduwLK666iofv/fee4my+KUZphKgIRg1apSPL7roIh/neuJPfArCxhtvXDcNA7CORYsWJdZvuummOjvWtttu6+P0p3cBxcCILAAAAIJERxYAAABBoiMLAACAIFXMHNmpU6cm1uNPDUl3ySWX1HFrMps9e7aPb7jhBh8feuihiXq/+MUvStYmoBzS59ide+65Po7Pd3fOZd3HmWee6eO//e1vibKNNtqotk0EGoR4vqXPdd1tt918HH/KZNOmTRP1Wrdu7eNWrVr5+Msvv0zU+9nPfubj+HzXnXfeOVGvd+/ePm7evLmPW7ZsmeWnAGqOEVkAAAAEiY4sAAAAglTWqQVLlizx8b/+9a+s9e6+++7Eevv27eusTXHxqQSStO+++2asN3jw4MT6BhtsUGdtAipBfGqNtO4t8vIxevRoHz/xxBOJsvgtvOJTENIviQINzVdffZVYj5+XZsyYkSgbO3Zsxn30798/sT5t2jQfx5+QmX77vE6dOvl4vfUYB0Nl4DcRAAAAQaIjCwAAgCDRkQUAAECQyjpH9ne/+52P44+4lKQ+ffr4+PDDDy9Zm+JefvnlxPrHH3/s4+OPP97Hv/rVr0rWJqBc3n//fR/fc889Wev17NnTx5tuummi7Omnn864zYoVKxLr8Tm4Rx99tI8322yz/BoL1CPfffedj3/5y18myuLzYi+88MJE2T777JPX/uPzYuM6d+6cZwuB8mFEFgAAAEGiIwsAAIAglXVqQfypP+lPAOrYsaOP6/qWO998842Pr7nmGh8PHz48US/exvRbggH13fTp0338+eefJ8oGDBjg4xdffNHH3377baLe/fff7+M///nPPp4/f36iXnwaz8EHH+zj9Nt08QQw1Ffxp2rFz0vjx49P1IvfjvL8889PlLVo0aKOWgdUDkZkAQAAECQ6sgAAAAhSWacW5DJhwgQf77fffomyNm3a+PjUU08teN8vvPBC1vXXXnst63blunsCUAlWrlzp4/SpQOeee27GbZo1a5ZY/81vfuPjMWPG+HjBggWJembm4/jlUZ7shYYi/lSua6+91sddunRJ1Js0aZKPW7duXfcNAyoMI7IAAAAIEh1ZAAAABImOLAAAAIJU1jmyZ599to+fe+65RNlHH33k4/jtfKTk/Ll///vfBR83vr207ny/tbbccsvEevwWKEBD88ADD2Qte+yxx3x8yCGH5LW/yZMn51Vvl1128XGrVq3y2gYI3SuvvJLx9d69eyfWO3XqVIrmABWLEVkAAAAEiY4sAAAAglTWqQV9+/b18axZsxJl8acIPfnkk4myYcOG+XiTTTbx8XHHHZfXcY855pjEeo8ePTLW23XXXRPr6VMNgIbkqKOO8nH6lJ4333zTx2+//baP0/P60Ucf9fHy5ct9HL+lXnrZiBEjfJyeu926dcur7UBo4reni0t/ut3ll1/u40GDBiXK0qchAPURI7IAAAAIEh1ZAAAABKlinuzVtm3bxPqee+6ZMZak6667rlbHevfddxPr8bsY9OrVy8c33HBDrY4D1Cf77LOPj9OfIDRz5kwfb7fddj7OdkcQSdp33319PHz48ETZQQcd5ON33nnHx7fcckui3u23315ds4Egffrppz6O51H8CXtScmrBVVddlSg75ZRTfLzzzjv7+L///W+iXteuXX28/fbbZ23TnDlzfNy/f38fc+cElBMjsgAAAAgSHVkAAAAEiY4sAAAAglQxc2RL6Yorrkisx+cfxW/t1b59+5K1Cah0G220kY8ffvjhRNmQIUN8vGLFCh+nP0XvrLPO8nF8rnuzZs0S9QYPHuzjP//5zz6eOHFiot6CBQt8zO3xUJ/8/ve/9/Ff/vKXvLZZs2ZNYj0+9zx9HnptxW99ucceeyTKRo8eXdRjAbkwIgsAAIAg0ZEFAABAkBrM1IL4pdB77703Ubbhhhv6eOONNy5Zm4BQxW/FJSWfQnT//ff7OP2JXfFpPenTCeIuvvhiH8+dO9fH6U8Ui+8vPa+BkF177bU+PuKII3x89NFHJ+qtWrXKx4sWLUqUpU81KKYlS5b4OH2qUffu3X180UUX1VkbAIkRWQAAAASKjiwAAACC1GCmFjzxxBNZyw488EAf9+nTpxTNAeqV+FSD9GkHNdG8eXMf/+IXv/Bx+tSC559/3sefffaZj+N3WABC1KhRIx/vuOOOPo4/6S7ds88+m1iPTzu47LLLfPzGG28UoYU/SL87yZQpU4q6fyAXRmQBAAAQJDqyAAAACBIdWQAAAASpQc6RbdmyZaIs/gQVAJUlfuuhcePGJcriTxD629/+5uNLLrmk7hsGVJi99947a9n06dN9nD5HtkmTJj4+/vjjfXzSSScl6v31r3/1cfw2e0A5MSILAACAINGRBQAAQJDq9dSC22+/3ccff/yxjzfddNNEPW65BVSu9db74fP20KFDE2Vjx471cfz2QkceeWSi3tZbb103jQMCsd9++/n4wgsvTJTFb9M1YsQIH//nP/9J1HvhhRfyOlbHjh1r0EKgZhiRBQAAQJDoyAIAACBIDWZqgXPOxwcccEDWbb744gsfL1++PFHWuXPnIrYOQKF69eqVWL/yyit9HL/7yAUXXJCoN2rUKB/HnxoGNBTbbbedj+NPy5OkBx98MOM28SfnpWvc+IfuQ/zpmJJ03XXX1aSJQI0wIgsAAIAg0ZEFAABAkOjIAgAAIEj1eo5sNvG5PVJy/lz8ySXdu3dP1Lv33nvrtmEACnLsscf6+I477vDxI488kqgXv41Qjx496r5hQIWJzw2/6aabEmXx74ZMmTLFx5988kmiXlVVlY/juRe/9R1QaozIAgAAIEh0ZAEAABCkBjm14M4770ysjxw50scnnniijy+++OKStQlA4dq3b+/jZ555xsddunRJ1Lv22mt9fP/999d9w4AKlv50ywkTJvj4H//4h49fffXVRL34FIJNNtmkbhoHFIgRWQAAAASJjiwAAACCREcWAAAAQarXc2RvvfVWH1966aU+HjBgQKLeqaee6uO2bdv6uGnTpnXYOgDFFH+E9L777psoGzdunI/feuutRFm3bt3qtmFAQI455piMMVCpGJEFAABAkOjIAgAAIEj1emrB7rvv7uPnnnuujC0BUEpjxoxJrPfs2dPH8+fPT5QxtQAAwsWILAAAAIJERxYAAABBqtdTCwA0TBtuuGFi/b333itTSwAAdYkRWQAAAASJjiwAAACCREcWAAAAQWKOLAAAQAP0xz/+MWvZtddeW8KW1BwjsgAAAAgSHVkAAAAEyZlZ/pWd+1TS+3XXHGTQxczaF3OH1byP7SQtrWYXDbVOMfZR1PeTnCybUr+P9S0PKq0OeVk/VFpeVtLveKXVKVpOFtSRRf3nnJtsZv2oU3fHAQpVH/Og0uoAhWqouVKqvyP5YmoBAAAAgkRHFgAAAEGiI4t0I6hT58cBClUf86DS6gCFaqi5Uqq/I/kxs7IukjWSbJpkE7KUHy7ZHMm+l6xfWtkFks2XbJ5kP4u9PjB6bb5kf4y9/k/JZkp2Tey1iyQ7JEf7ekt2V9prO0q2WrIh0Xp7yZ4s9/8lC0sxFsnOlmx2lHfnZKkzQLKp8TyIlR0n2X+i5bjY630lmxXl5S2SRXP07booL++L1f1VtmNH5R3W/s2QrKlk90T7niHZHrF6z0jWttz/pywsxVokWxj9rk+XbHKWOl0kezbKqxck6xS9vme03drl27Xnv9qeHyXbQ7IVsX1fEr3eVLKXJGtc7v87lvq5VMKI7NmS5uYony1psKSX4i86p26SjpS0vaSBkv6fc2rknBpJGi5pf0ndJB3lnLo5px6SvjFTD0k7OqfWzqmDpJ3NNDbH8S+UdEvsuI0kXSfpqbWvmelTSYud00/y/aGBSuScuks6SdJOknpKOsg5dc1Q9QNJv5Z0f9r2G0m6VNLO0T4udU5to+Lbon1vFS0DnVNrSX2ivPzOOe3gnJpLOl6pPM7mPEl3RvFJkmSmHSTtK+kvzvm/bf+QdFp+Pz0QjD3N1MtM2b4sc4Ok+6K8ukLSnyXJTM9H2/WStJekryU9Vazzo6RJa/dvpiuiY34n6VlJv6jFzwtkVdaOrHPqJOlASSOz1THTXDPNy1B0sKTRZlpppvckzVfqxLmTpPlmejdKoNFR3VWSmkcnuCaS1iiV4JfmaN8GknqYaUbs5TMl/UvSkrTqYyUdnevnrXTOuYHOuXnOufnOuXUe9+Gcu9s5t8Q5NzvHPjZ3zj3vnHvLOTfHOXd2hjrNnHNvOOdmRHUuz7KvRs65ac65CVnKFzrnZjnnpjvnJmep08Y5N8Y597Zzbq5zrn9a+TbR9muXz51z52TYz7lRW2c75x5wzjXLUOfsqHxOpn0EYjtJr5vpazOtlvSiUh8kE8y00EwzJX2fVvQzSU+b6TMzLZf0tFId1g6SNjTTa2YySfdJOiTavolzcpJaKJWnv5d0q5lW5WjnYZKejOJukp6L2rVE0v8kf4IfJ+mogv4HKkh1ORnVyZmXxczJqG5F5GUDysma8Dkh6XmlzoHphkh6wkxfq3jnx2yCPz/GhXaujOrkzMuQz5XlHpG9SdJQrXsyzEdHSf+NrS+KXsv4upnmSvpU0lRJ4yV1lbSemabmOEY/pUaEJUnOqaOkQ5UaWUo3WdLuhf8YlcE5l2Ek23VLq/Z3pUa/c1kt6Xdm1k3SLpJOz7CflZL2MrOeknpJGuic2yXDvqobrZekPc2sl2W/jcfNkp40s22VGmFM7M/M5kXb95LUV6kRikfjdZxzHSWdJamfmXWX1EipqwHxOhlGMl2mkcxKN1vS7s5pY+fUQtIBkjYvYPtcebko/XUzfSHpcUnTJC2WtELVjAI5py0kLTfTyuilGZIGOafGUVnftW2OOtPrO6eNC/gZKkKeOSlVn5fFzEmpAvKygeVkOlNqFHWKczo5S50Z+uED6KGSNsiQA0dKekBKDRiplufHSH/nNMM5PeGcto+9PlvSjtX8XEEI+Fwp5c7LYM+VZevIOqeDJC0x05RSHdNM50SXPP4i6UpJFzunPzmnh5xLXZ5M00Gp5F7rJkl/MMvY8V4i6UfFb3XJRCPZ9q6ZxUeyPTN7SdJnuXZiZovNbGoUf6FUMnRMq2Nm9mW02iRaEjc0ds5VO1pfHedca0kDJN0VHfc7M/tfjk32lrTAzDLdALuxpObOucZKjRx+lFYejWTa12aWdSSz0kUntLVTZ56UNF2p0Zm6POawKC9/p1ReXuKcTozy8qIMm6Tn5d1KdYwnK5Wjr6S1OdTcrDYnperzslg5KVVcXjaInMxgNzP1UaojdbpzGpChzu8l/dQ5TZP0U0kfKpYT0RWSHSRNXPtaEc6PUyV1MVNPSbdKP3wYNdMapaYObVCzH7micK6ssHNlOUdkf6LUKMpCpX4R9nJOowrY/kMlR4o6Ra9le91zTgdLmiKplaQtzXSEpCHRCFTcN5Liw+L9JI2O2jxEqXm5h0RlzaL6oco2klZjzrkqSb0lvZ6hrJFzbrpSnYynzSy9Tj6j9dHIhJvinMs0MrGFUn9o74kuu4x0zrXMsT8/QpE4iNmHSs05+0DRqKGZPZVWLRrJdBs752oyklkxzHSXmfqaaYCk5ZLeKWDzXHnZKcPrnnPqLclJmifp8Cgvt3ROW6UdI5GXZlptpnOjk/DBktqktTnU3Ky0nJQqJC8bWk7GmaXyJppG86hSHav0Oh+ZabCZekv6U/RavGNyhKRHM03fqen50Uyfm+nLKH5cqSlD7WL115f0baE/bwWqtLzM98p2rrwM+lxZto6smS4wUyczVSn1n/KcmX5VwC7GSTrSOa0fXU7cStIbkt6UtJVz2sI5NY32PW7tRs6piaRzJA2T1Fw/fLppJKlp2jHmSj980cVMW5ipKmrzGEmnxS6Bbq11L7M0WM65VkrNJT7HzD5PLzezNdElik6SdoouN6zdNhqtt+pG63czs9jIhEsfmWgsqY+k28yst6SvJGWbZ9hU0iBJD2coa6vUJ+4tlBrZa+mcS/yumlnJRzLrinPaJPq3s1KflO/PvUXCREn7Oae20Ze89pM00UyLJX3unHaJ5sMeK+nfadteKelipUYdGkWvfS+tcwJ9R1JVrL0tnFPLKN5X0mozvRWtO0mbSVpYwM9QL9UmJ6PtKyYvG1pOruWcWq4d1Yx+5/dThvOOc2oX+8LjBUpdtYg7Shk6IrU5PzqnzaJ8k3PaSan+xbJofWNJS6uZ994glehcKeXOy6DPleWeI1st53Soc1okqb+kx5xLXQox0xxJD0l6S6n/jNPNtCb6gsoZSp1Q50p6KKq71umS7o0muM+U1MI5zZI0Je0Tq8z0tqTWeV4O2VPSY7X5Wcus2pHsfDnnmiiVmP80s0dy1Y0uXzyv5HyiaLTeLZQfrXfrjNZHn/5kZtlGJhZJWhT7BDtGqWTNZH9JU83skwxl+0h6z8w+NbNVkh6RtGuG9txlZn3NrCYjmZXkX87pLaXmyp2enheS5Jx2jPLycEl3OJfKMTN9plSH9M1ouSJ6TUrdPWCkUl/MXCDpidj+DpE0ORpJ+p+k6VFeNkv/MomZvpK0IHY3hU0kTXVOcyX9QdIxsep9Jb0W/V0ITSXlpFRZednQcnKtTSW97JxmKDVw85iZ/9Jj3B6S5jmnd6Jtrl5b4JyqlPq9ejHDdrU5Pw6RNDtq2y2Sjoy+2CmFf36Mq6S8zCsno+1z5WXY50or0n286usi2bmSnZhHvZdCvl+lUp/I3lXqk1RTpb4ssH2GelWSZufYj1PqG+k35ajTXlKbKG4uaZKkg7LU3UPSOvcYltRS0gax+BVJAzPUmyRpmyi+TNL1WY4zWtLxWcp2ljRHqZFBJ+leSWdmqLdJ9G9nSW+v/RlZ6uL31Q6V7Ko86t0s2d7lbm/Nfsb8cjKqmzUvi52TUZ2y5iU5WRlLAefHRyTbutztLc7PHNa5MiqrNi9DPleW/Zei0hfJmkl2TDV12ue6aXQoi1JzVd5RarTsTxnKH1Bq3ssqpT7BnZChzm5KXY6aqdQlg+mSDkir00Opb6nPVOqy2CU52pTthPnj6A/IjChx1mlvVK+XUl8CmqnUlw/W+bARJfYySa1ztOPyKOFmK3Vv0vUz1Jmk1BWCGZKC7DyFtOR5Aj2p3O2s3c+YOyejOjnzstg5GdUve16Sk+Vf8jw/NpXs2HK3tbg/dzjnyqis2rwM+VwZPVkHAAAACEvFz5EFAAAAMqEjCwAAgCDRkQUAAECQGhdSuV27dlZVVVVHTUEmCxcu1NKlS10x98n7WFwzZkirs9zcqXFjqWfPH9anTJmy1MzaF+vYvJflUdfvY67fKWnd3yvUDnlZP/A+Fq6Q81cpFfJeFtSRraqq0uTJk2vWKtRIv37ZHlVec7yPxeVyfMxYvVqK/1c75zI90q/GeC/Lo67fx1y/U9K6v1eoHfKyfuB9LFwh569SKuS9ZGoBAAAAgkRHFgAAAEGiIwsAAIAg0ZEFAABAkOjIAgAAIEh0ZAEAABAkOrIAAAAIUkH3kQUAAGFYuXKlj3fddVcfT5s2LVFv0KBBPh47dmzdNwwoIkZkAQAAECQ6sgAAAAgSUwsAFNWkSZN8HL+cKUnz5s3z8YQJE3z82GOPJeodeOCBGffdv3//xPruu+9e43YC9U18KoEknXvuuT6ePn26j13ac0n79u1bt9ohPGAAACAASURBVA0D6hAjsgAAAAgSHVkAAAAEiY4sAAAAgsQcWQAF+/zzzxPrRx99tI+fffZZHzdv3jxRb9WqVT7+4osvsu7/pZdeyvh6+v5atmzp49tuu83HQ4YMybpvoL665ZZbEut33HGHj/fee28fX3HFFYl6u+yyS902DKhDjMgCAAAgSHRkAQAAECSmFgAo2B/+8IfEevxWWnHffPNNYn277bbz8SabbOLjDTfcMOuxvv/+ex+n36Yrvv8TTjjBx1tvvXWiXo8ePbLuH6gvFi9enLVsn3328TFTCVCfMCILAACAINGRBQAAQJDq9dSC+fPn+3jp0qU+fvTRRxP1XnjhBR+vt94PfftTTjklUS/+lKKtttqqWM0EgjB79mwfjxkzJmu9zTff3Mf33Xdfoqxr164+btOmjY9btWqVdX/xqQXp37a+8sorfRy/k8Jll12WqHfXXXf5uG3btlmPBYTsyy+/TKw3bdrUx/GpBUB9wogsAAAAgkRHFgAAAEGiIwsAAIAgBT9HdtasWT4ePnx4ouyRRx7x8aefflrwvl977bXEepMmTXy8zTbb+Hi33XZL1Lv55pt9HJ+jBIQsPv8uPudckpxzPh46dKiP99hjj1ofNz5vPX3u63fffefjG264wcfp8+B/85vf+Piggw6qdZuASvHRRx/5eOTIkYmy+Pc6+vTpU7I2AaXEiCwAAACCREcWAAAAQQpiasHMmTMT6/EpBA8++KCPV6xYkXUfnTp18vHuu++eKKuqqvLx9ddf7+O+ffsm6r3++us+XrZsmY8ff/zxRL2ePXv6OP0WXkCoVq5cmbXs17/+tY/POOOMErQm5ZprrvHx6NGjffzee+8l6sWnGTG1APXJVVddVe4m6NVXX02sL1q0KGO9+LlRWvcJfEBNMCILAACAINGRBQAAQJDoyAIAACBIFTtH9re//a2P02+lk+1WWumP4Nthhx18HJ9L16xZs6zHjc/1ue222xJlxx9/vI+nT5/u48022yxR77TTTvPxYYcdlihr37591mMDleziiy/OWrbzzjuXsCWZDRw40MfpuZt+Kz2gvnjssceylp144olFPdapp56a8bjLly9P1Pv6668zbr/hhhsm1s877zwf5/r7AuTCiCwAAACCREcWAAAAQSrr1IJvv/3Wx8OGDUuU3XnnnT42s0TZJpts4uP4pY7zzz8/Ua9ly5YFtyl+W63Vq1cnyi6//HIf/+xnP/PxwoULCz4OUOnefffdxPqHH37o4zZt2iTK4tN4ymWvvfbycfrUAqC+SL9sv2rVKh/HbzMpJW+Ll0v8XDd16lQfH3LIIYl6H3/8sY/j5+X0KXPxaX7x/X3wwQeJenfccYePjz322ERZly5d8mo7wIgsAAAAgkRHFgAAAEEq69SCF154wcfxJ2pJycsWHTt2TJTFn9Kz0047FXzcNWvWJNb/+9//+jh+eePAAw9M1Ev/ZmY2xxxzjI/TL8ECoRg1alRiPT7VYMiQIYmyXXfdtSRtAhq6kSNHJtY/+eQTH8fv9pPLRx99lFgfMWKEj6+88sqs28XPxfHzXPxOPdK6UxzWGjRoUGI9fueDxYsXJ8qYWoB8MSILAACAINGRBQAAQJDoyAIAACBIZZ0jG7/lR6NGjbLWa9KkSWL99ddf9/GYMWN8/Pbbb2fdR/PmzX08d+7cRFl8vV27dj6O32okl0033TSxftFFF/k4ve1AKB544IHEeny+99lnn13q5gCQNG3atKxlW221VV77uOqqqxLrt99+u4+dcz7ee++9E/VuvPFGH3fv3j2vY8V17dq14G2A6jAiCwAAgCDRkQUAAECQyjq1IH7ZYs8990yUPf300z5+//33E2VnnXVWXvtv3PiHHy/9KV3Z5JpOsN56P/T7Bw8e7ONbbrklUa9Dhw55HQsIybbbbuvj3XbbrYwtARqu9Ftn5eudd97x8ejRo7PWO/nkk3188803J8qaNm1ao2Nn07dvXx/36dOnqPtGw8GILAAAAIJERxYAAABBKuvUgvidBB599NFE2f/+9z8fX3vttYmy//u///Pxxhtv7OPOnTsn6q1cudLHM2bM8HH8rgeFiD815ZprrvExT+9CffHVV1/5ON/pOABK5/PPP0+sx5+CGY/T3XrrrT6On18l6eijj/bxbbfdVtsmZvXll18m1uPT/4o9bQENByOyAAAACBIdWQAAAASJjiwAAACCVNY5srnE552mz5GtiWOPPdbHuebIbrjhhj6OP8VEkn7961/7ONeTyIBQPfjggz6eP39+oiz+1LtKNG7cuKxlPGEP9UX8yVvp6+llcfHbdqXXq+ktvfIR3/fIkSMTZYcddlidHRcNByOyAAAACBIdWQAAAASpYqcWFMOwYcN8nOtJJnHxW4/88pe/LHqbABTPlClTfDx+/Pis9a6++upSNAeoWCNGjPDxK6+8kiiLr8dvLRm/5aSUvN1lvuJPwWzRosX/b+/ew6So7vyPv78CctMFVsEMmogaQ2CJAgriT2NQEsUrupE8JqsxiYH4SLKi+LgmCoIY4yX4mGzWXBSNmGiC5rKIEXUXL/nFeOE6gIiLBo3KRhAVjfkpmO/vjzpTVhXdPd1Mz3RXz+f1PPXMt/qcqjozPd8+p6tOV6fKpk2bVvH+RLJ0RlZEREREckkDWRERERHJpYaaWpD9ROSVV14Zx9u2bSu63bBhw+JYn6IUqV/JqQQAc+bMiePktxUdeeSRqXrjx49v34aJtKPkJ/83bty4U/tITgtYtmxZquyUU06J4+nTp8fx/fffn6q3cOHCON59990LPg7pvnf58uVxfNlll6XqjRkzpqy2i5SiM7IiIiIikksayIqIiIhILmkgKyIiIiK5lPs5sk8++WQcZ2/l8dZbbxXcJjm3B9K33OrevXsVWyeSL4MGDYrj5Lfc1dL7778fx9/97ndTZcnb6u2zzz5F63XtmvuXOunEBg4cGMcf+9jHUmUvvPBCHC9evDhVlrx9VvLWV01NTal6Tz31VBwn57sOGTIkVS85Dz3Z32Y/n5I8VnJebHL+rUi16IysiIiIiOSSBrIiIiIikku5v96W/DafrVu3Fq3Xu3fvOF6wYEGqLHurHpHO6phjjonj5OVMgDfffDOON2/enCrbc88923Tc5ubm1PqNN94Yx8lbBSUvgWb97Gc/i+PDDjusTe0RqVdz585NrZ944olxfO+996bKjj322Di+8MIL4zg7tSDpiSeeiOPkt3xly9w9jgcPHpyql9zutNNOK3oskWrQGVkRERERySUNZEVEREQkl3I5tSB5N4Jrr722rG3OPPPMOB47dmy1myTS8NauXRvHxx13XKqs1KXKciQvWcKOUxda9O/fP7V+8sknx/GoUaPa1AaRPEjenQNg0aJFcXz00Uenyv74xz/G8cSJE4vuMzlNwMzKaseXv/zlOM72w8lvERNpbzojKyIiIiK5pIGsiIiIiOSSBrIiIiIikku5mCP79ttvp9aT3zby3nvvFd3u4IMPjuMbbrih+g0TaWDZW+/Mnj07jpO3xGoPu+zywXvs5Hy75C2EAC655JJ2bYdIvUvOT3/88cdTZb/85S/jeP369XF80003peqdc845cZzMvaxkvY9//OOVN1akHeiMrIiIiIjkkgayIiIiIpJLuZhasHjx4tT6yy+/XNZ2119/fRz36NGjqm0SaXTZb+RJflvW+PHjU2WrVq1q07EmT56cWh8xYkQcn3vuuW3at0hn0bdv39T61772tYL1rrvuuo5ojkiH0BlZEREREcklDWRFREREJJc0kBURERGRXMrFHNnp06eXVe/iiy9OrR9zzDHt0RyRTmngwIFx3NzcXMOWiIiIRHRGVkRERERySQNZEREREcmlXEwt2LJlS9GyAQMGxPHUqVM7ojkiIiIiUgd0RlZEREREckkDWRERERHJpVxMLbjwwguLrifvaNDU1NRhbRIRERGR2tIZWRERERHJJQ1kRURERCSXNJAVERERkVzKxRzZCy64oOS6iIiIiHQ+OiMrIiIiIrmkgayIiIiI5JK5e/mVzTYBL7Rfc6SAfd29fzV32MrzuCewuZVddNY61dhHVZ9P5WTNdPTz2Gh5UG91lJeNod7ysp7+x+utTtVysqKBrDQ+M1vi7oeqTvsdR6RSjZgH9VZHpFKdNVc66nWkXJpaICIiIiK5pIGsiIiIiOSSBrKS9RPVaffjiFSqEfOg3uqIVKqz5kpHvY6Ux91rtoCfD74afA341CJ1jgJfBr4d/PRM2dng/xOWsxOPHwK+Cnw9+PfBw1xgvwa8GXxeou6ZxY4dypvAF4Z4V/Bbw75Xgo9N1Psv8H61/Htq0VKNBXw8+LqQP5cUqTMx5O3fwQ/NlH0zbLsO/LjW9gv+85CXVyUeuwz81BJtHAE+N8QW8nx92M/I8Hh/8EW1/ntq0dLWBbwv+N3gz4CvBT+8QJ266SvD+kHgfwyvE6vAe4TH1VdqqepSuwPjw8Igthd41/DP/dEC9QaFhJiXTE7wfwR/PvzsF+J+oexJ8DGhg7sP/HjwPuAPhvKbwT8B3hP8v8G7lWjndeATQjwF/NYQDwBfCr5LWD8b/NJaP6FatLRlAe8C/hz4/uGN20rwoQXqDQEfDP5wciALPjRs0x18v7CvLsX2G3L75rDtgyFPm8DvaaWdd4EfHOITQp5byPsnEvVuBT+i1n9XLVrasoDfBv7VEO8K3rdAnXrqK7uGgXBLju4B3iXE6iu1VHWp5dSCIcAT7rzjznbgEeCfs5Xc2eBOM/D3TNFxwIPubHHndeBBYLwZTcA/uPO4Ow7MA04N23czw4BewDbgIuDf3dlWop2fBRaFeCiwOLTrVeANoOVTdwuAz1f0FxCpP6OB9e487857wC+ACdlK7qx1Z12B7ScAv3DnXXf+BKwP+yy2321ATzN2AboB7wNXAJcXa6AZuwMHubMyccx54TXtcaBveB0A+C3wLxX+DUTqhhl9gKOAuQDuvOfOG9l6ddZXHgs0t+SoO6+5834oU18pVVXLgexq4JNm7GFGL+AE4MMVbL838OfE+kvhsb1DnHrcnbeA3wHLgY3Am8Bh7vy22AHM2A943Z13w0MrgVPM6BrKDmlpc3iB6G7GHhX8DnXFzMab2TozW29mlxQov8XMXjWz1SX28WEze8jMnjazNWZ2foE6PczsSTNbGerMKrKvLma23MwWFinfYGarzGyFmS0pUqevmd1tZs+Y2VozOzxTPjhs37JsNbOpBfZzQWjrajO708x6FKhzfihfU2gfOVEsr9q6fcHH3VkLbAKWAfcAHwV2cWdZiWMcSvT6UU6blwCfrKD9daW1nAx1SuZlNXMy1K2LvOxEObkfUY7casZyM242o3cF29eir/wY4Gbcb8YyMy5uqau+Mq7TYX1lqFMyL/PcV9ZsIBs6sGuAB4jexa2A+B1bex3zWneGuzMNmA3MMOOrZsw347ICmzQRvYC0uIUo2ZcANwCPZdr8KjCwfVrfvsysC/AfwPFEZ54/b2ZDM9V+CoxvZVfbgWnuPhQYA0wpsJ93gWPc/WBgODDezMYU2Nf5wNpWjne0uw/34vej+x6wyN0/Dhyc3Z+7rwvbDyd6Y/IO8JtkHTPbG/hX4FB3HwZ0Ac7I1BkGTCI683gwcJKZfbSVtgvgztSQl3OI8nK6GZeGvJxUYJNsXpbS6DkJredlNXMS6iAvO1lOdgVGAj90ZwTwV6Dgm5pqqUJf2RU4kuhqyJHAaWaMS5Q3el7+lPrrK6F0Xua2r6zpXQvcmevOIe4cBbwOPFvB5i+TPoO7T3js5RBnH4+ZMQIwYB0w0Z3PAQeYcWDmGH8D4ncT7mx354KQ4BOAvpk29wjb5FG49OvPu3vBS8ru/iiwpdRO3H2juy8L8VtEybB3po67+9thtVtYUt/MYWb7ACcCN+/sL2RmmUty/p6773BJLmEc8Jy7F/oml65ATzPrSnS57ZVMeZgq4++4e9GpMjlQLK/aun2r+zVjArAU2A04IOTl6eGKTVIqL1vZd0PnJLSel9XKSai7vOwsOfkS8JI7T4T1u4kGtuXq8L4ytPlRdza78w7RGd5kmxs6L9VXdmxe1nQga8aA8PMjRL/IHRVsfj9wrBn9zOhHNCfnfnc2AlvNGBPm+HwR+M/MtrOB6UT/FF3CY3+HHTrMZ4FBifb2armkY8ZngO3uPB3WDfgQsKGC36GetPWS8g7MbBAwAuIX4GRZFzNbQfTO/EF3z9a5AbiYHed7JTnwgJktNbPJBcoTl+RsuZndbGalLsmdAdy5w0HcXwa+C7xIuNTm7g9kqoWpMraHme3MVJl68RRwoBn7mbEr0d9kQQXbLwDOMKN7uNx4IPBka/s1oxswFbgW6MkHL9ZdgF0zx1hLNAUhecwvmmFmjAHeDK8DEF3iLHp5r87VW05CneRlZ8pJd/4X+LMZg8ND4yDqd8rU4X1lOOYnQp/ZFfhUS5vVV+6oA/pKKJ2Xue4ra30f2V+Z8TTR3LgphSawmzHKjJeAicCPzVgD4M4WoiR7KixXhMcAziN6d7IeeA64L7G/U4El7rwSjrfCjFVAj8SHRwjH+CvwnFncaQ4AlpmxFvg34KxE9UOAx8MH1zo9M9sN+BUw1d23Zsvd/f1wiWIfYHS43NCy7UnAq+6+tJXDHOnuI4ku8Uwxs6My5YlLcl7ykpyZ7QqcAtxVoKwf0Tvu/Yguh/U2szMzv0+HT5VpD+H/9+tEHdFaYL57lHNJZpwW8vJw4F4z7g/brwHmE3Vai4jy+v0y9jsFuC2cvWkGeoW8XJp9XXDnGaBP+NAXRGd7nifK95uI8r/F0cC9O/0HaSBtycmwfd3kZWfKyeAbwM/NaCa6xHxVtkI99ZVhHuz14XgrgGXucR6qr0zooL4SSudlvvvK9rodQqMs4KeBX1lGve+Bj6t1e3f+9+Rw4P7E+jeBbxaoNwhY3cq+uhENWC4s89gzgIsS698hepe7Afhfork4P2tlHzOT+wiPfQjYkFj/JHBvke0nAA8UKZsIzE2sfxG4sZX2XAWcV+vntVEX8AtabkfUSr1Hyek9K8vNyVBWMi/bmpPhsbrJS+Vk/S3qK3eoV5d9ZdhuZmY/ue4ra31Gtu658xvKuwSy2p3/bufmtKdw6df2C++4Kr2kDICZGdE8m7Xufn2ROv3NrG+IewKfAZ5pKXf3b7r7Pu4+KLRjsbufmdlHbzPbvSUmulyWuoTs7uGSnJVzSe7zFLhUErwIjDGzXuH3G0eBifVmFqbK2M5MlZHK/BDiT0gXZEZ/4HqPzg7lUd3kJNRdXion64z6ysp0VF8Zti2Zl7nvK2v97kZL/SxEc1WeJbrEtMMNq8M/70ai+wq+BJxToM6RRHNxmokuGawATsjUOYjo1i7NRMk0o0SbxgILCzy+P9Ht0FYCawq1N9QbTnSXiWaie4rucHYO6A28BvQp0Y5ZRC8gq4Hbge4F6vyeKPlXArk946ClfpbWcjLUKZmX1c7JUL/meamc1FKrJU99ZShrNS/z3FeGr6MTEREREckXTS0QERERkVzSQFZEREREcqlrJZX33HNPHzRoUDs1RQrZsGEDmzdvtmruU89jda1cCduL3Eima1c4+OAP1pcuXbrZ3ftX69h6LmujvZ/HUv9TsOP/lbSN8rIx1MPzWEl/IMVV8lxWNJAdNGgQS5YU/OpsaSeHHlrsGx53np7H6rISbzO2b4fkn9rMCn0Tyk7Tc1kb7f08lvqfgh3/r6RtlJeNoR6ex0r6AymukudSUwtEREREJJc0kBURERGRXNJAVkRERERySQNZEREREcklDWRFREREJJc0kBURERGRXNJAVkRERERySQNZEREREcklDWRFREREJJc0kBURERGRXNJAVkRERERyqWutGyAiIiK1M3PmzDieNWtWHI8dOzZV76GHHuqgFomUT2dkRURERCSXNJAVERERkVzSQFZEREREcklzZEWk3bz++uup9eXLl8fxokWL4vi6665L1TOzOJ44cWIc77vvvql606ZNi+O99tqrbY0V6aQeeeSRgo8//PDDRdez82dFakVnZEVEREQklzSQFREREZFc0tQCEWmzbdu2xfGcOXPi+Ac/+EGq3saNGwtun5xKkF2/++67ix538+bNcXzLLbeU11gRSclOISinnqYWSL3QGVkRERERySUNZEVEREQklxpqakHyE9EA06dPj+Pf/e53cezuqXrFPiH97W9/O1WvqakpjpPfcDJu3LhUvZ49e1bSbJHc+/GPfxzHl156acXbZy9TFvsUddZtt90Wx5paINK+kt8AJlIvdEZWRERERHJJA1kRERERySUNZEVEREQkl3I5RzZ5q5/kXLovfelLqXrJW/1kb++TVOxWP9m5ri+++GIcJ29DMm/evFS9M888s+ixRBrB6tWrU+uzZ8+ueB/XXHNNHJ9//vmpshkzZsTxtddeW/G+RUSkc9AZWRERERHJJQ1kRURERCSXcjm1YNmyZXF83HHHFa03cODAOE5+w1CvXr2KbvPCCy8UrfeNb3wjjrt37x7HydtyiTSq5HSCb33rW6myTZs2xXFyqs6+++6bqrdgwYI4Hjp0aBzvskv6PfUVV1wRx6eddlocn3LKKUWPe9BBB8Vxc3Nzkd9CRLIuv/zyOJ41a1bResnbb+lWXFIvdEZWRERERHJJA1kRERERySUNZEVEREQkl3IxRzZ7q5/sPLkWn/70p1Pr3/nOd+J45MiRZR3rlVdeieMJEyakyt544404vvjii+M4+xW1Io0o+RXQCxcuTJUlv/a5W7ducTxlypRUvWHDhpV1rOQ+Ro8eHcfZW+zNmTMnjletWhXHkydPTtX7yU9+UtZxRTqjUvNiReqdzsiKiIiISC5pICsiIiIiuZSLqQVXXnllaj15y52TTjopjpOXGQEOPPDAio+VnMaQvM1X1vjx4yvet0ie3XfffXFc6pvyxo4dG8fTpk2rahuuvvrqom1KTi146qmnqnpcERGpTzojKyIiIiK5pIGsiIiIiORS3U4tmDRpUhzPnz8/VbbbbrvFcfJS485MJQDYtm1bHCfvdJD8JDakL5l+6lOf2qljieTFa6+9llp/4oknytrurLPOao/mtHqs5J1ERESkc9AZWRERERHJJQ1kRURERCSXNJAVERERkVyq2zmyS5YsiePsrX569+4dx0OHDq1438k5sQDTp0+P40cffbTocWfMmFHxsUTyaunSpan1DRs2FK171FFHxfGJJ57YXk0qW/Jb+AA2btwYx01NTR3dHBERaSc6IysiIiIiuaSBrIiIiIjkUt1OLai25GXRG2+8MVWW/UawFgMHDkytDx8+vOrtEqlXyek9rZk1a1Yc9+vXrz2aU5EXX3wxtZ78xj5NLRDZOTNnzqx1E0R2oDOyIiIiIpJLGsiKiIiISC7V7dSCIUOGxHFzc3OqbMuWLXE8YsSIsva3adOmOH7llVdSZdm7E7QYN25car1v375lHUukEbzzzjup9ew33SXVwzfdlWqfiIg0Jp2RFREREZFc0kBWRERERHJJA1kRERERyaW6nSM7d+7cOH7rrbdSZffee28cZ+fPlmPBggWp9dtvvz2O77777jg+99xzK963SKPI3n6r2FzyepFsX723VUREqkNnZEVEREQklzSQFREREZFcqtupBT179ozje+65J1X28MMPx3Gpbx8aOnRoHJ9wwglxfN5556Xq3XXXXXE8ePDgOD7ggAPKb7CI1I3dd989tb7HHnvUqCUiItKedEZWRERERHJJA1kRERERyaW6nVpQytixYwvG5frRj36UWk9+wnnUqFFx3L9//4r3LSIdZ968eQUfnzlzZmp95MiRHdAakXxK9qPJqXtZybzK5phIreiMrIiIiIjkkgayIiIiIpJLGsiKiIiISC7lco7sztiwYUPRsuSteqZOndoBrRGpf1dffXVqfcWKFXG8adOmVNlXvvKVOL7lllvat2EJyXYMGDAgjvWtfCIinYPOyIqIiIhILmkgKyIiIiK51GmmFlxxxRVFy0466aQ41m16RCLDhw9PrV933XVxfPbZZ6fK5s+fH8df//rX47ja+TRp0qTU+l/+8pc4/tznPhfHPXr0qOpxRRpJ9hZbpW65JVLvdEZWRERERHJJA1kRERERyaWGnlqwevXqOP71r39dtN748eM7ojkiuXbEEUfE8Re+8IVU2R133BHHjzzySBxXY2rB4sWL4zibx3vttVccz5gxo83HEukMZs2aVesmiFSNzsiKiIiISC5pICsiIiIiuaSBrIiIiIjkUkPPkV2+fHkcb926NY7NLFVPt+oRad3+++8fx1deeWWq7A9/+EMcJ+ffZb8B7Kqrriq472effTa1/uSTT8bxhRdeGMdvvPFGqt5FF10Ux0OHDi3adpHOLnmLrXJvt/XQQw+l1seOHVu9BolUic7IioiIiEguaSArIiIiIrnU0FMLkpc1k9MJhg0blqp3+umnd1ibRBrBoEGDUuuPPfZYHJ977rlxfOONN6bq3XfffQXrZW+dtXnz5oLHPfnkk1PrkydPLq/BIlLU5ZdfHsczZ86sXUNEdoLOyIqIiIhILmkgKyIiIiK5pIGsiIiIiORSQ8+Rvf322ws+ftZZZ3VwS0QaW1NTUxzPmzcvjtetW5eqN3v27Dg+77zz4jh5G62sz372s3Gc/crbrl0b+iVMpGqSt85y99o1RKTKdEZWRERERHJJA1kRERERyaWGvi43ZMiQOG5ubq5hS0Q6jz59+sTx6NGjU2X33HNPRzdHREQamM7IioiIiEguaSArIiIiIrnU0FMLjj/++Dh+/vnn43jUqFG1aI6IiIiIVJHOyIqIiIhILmkgKyIiIiK5pIGsiIiIiORSQ8+RTX6Dl77NS0RERKSx6IysiIiIiOSSBrIiIiIikkvm7uVXNtsEvNB+YQwcjgAACeZJREFUzZEC9nX3/tXcYSvP457A5lZ20VnrVGMfVX0+lZM109HPY6PlQb3VUV42hnrLy3r6H6+3OlXLyYoGstL4zGyJux+qOu13HJFKNWIe1FsdkUp11lzpqNeRcmlqgYiIiIjkkgayIiIiIpJLGshK1k9Up92PI1KpRsyDeqsjUqnOmisd9TpSHnev2QJ+Pvhq8DXgU4vUOQp8Gfh28NMzZWeD/09Yzk48fgj4KvD14N8HD3OB/RrwZvB5ibpnFjt2KG8CXxjiXcFvDfteCT42Ue+/wPvV8u+pRUs1FvDx4OtC/lxSpM7EkLd/Bz80U/bNsO068ONa2y/4z0NeXpV47DLwU0u0cQT43BBbyPP1YT8jw+P9wRfV+u+pRUtbF/BbwF8FX12iTj31lXuAPwT+NvgPMvXUV2qp6lKzM7JmDAMmAaOBg4GTzPhogaovAl8C7shs/4/A5cBhYR+Xm9EvFP8w7PvAsIw3ow8w0p2DgPfM+IQZPYEvA/9RoqkXAjeFeBKAO58APgPMMYv/hrcD55X324vUJzO6EOXD8cBQ4PNmDC1QdTXwz8Cjme2HAmcA/wSMB240o0ux/ZpxEPC3kJejzOhjRhNwmDu/LdHUbwHfD/HxfJDrk4nyH3c2ARvNOKLSv4NInfkpUT6VUk995f8DpgMXFainvlKqqpZTC4YAT7jzjjvbgUeIOsYUdza40wz8PVN0HPCgO1vceR14kCgJm4B/cOdxdxyYB5watu9mhgG9gG1ESfbv7mwr0c7PAotCPBRYHNr1KvAG0PKpuwXA5yv6C9QZMxtvZuvMbL2ZXVKg/BYze9XMVpfYx4fN7CEze9rM1pjZ+QXq9DCzJ81sZagzq8i+upjZcjNbWKR8g5mtMrMVZrakSJ2+Zna3mT1jZmvN7PBM+eCwfcuy1cymFtjPBaGtq83sTjPrUaDO+aF8TaF95MRoYL07z7vzHvALYEK2kjtr3VlXYPsJwC/cededPwHrwz6L7Xcb0DO8IewGvA9cQdTxFmTG7sBB7qxMHHNeeHP+ONA3vA4A/Bb4lwr/BnWjtZwMdUrmZTVzMtSti7zsRDmJO48CW1qpUzd9pTt/def/Eg1os9RX0rF9ZahTMi/z3FfWciC7GvikGXuY0Qs4AfhwBdvvDfw5sf5SeGzvEKced+ct4HfAcmAj8CatnPUxYz/gdXfeDQ+tBE4xo2soO6SlzeEForsZe1TwO9QNMytwxsyyZ+J+SutnBbYD09x9KDAGmFJgP+8Cx7j7wcBwYLyZjSmwr/OBta0c72h3H+7Fb+PxPWCRu3+c6Mx/an/uvi5sP5zo+XwH+E2yjpntDfwrcKi7DwO6EJ11TNYpcIXBCl1hqHfF8qqt2xd83J21wCZgGXAP8FFgF3eWlTjGoUSvH+W0eQnwyQraXzfKzEloPS+rmZNQB3nZyXKyrWrRVxalvjLW0X0llM7L3PaVNRvIhg7sGuABondxK4jOxrTnMa91Z7g704DZwAwzvmrGfDMuK7BJE1En2+IWomRfAtwAPJZp86vAwPZpfbsLZ8z8eXcveCbO3cs4K+Ab3X1ZiN8iSoa9M3Xc3d8Oq93CkrqhsZntA5wI3Lyzv5CZ9QGOAuaG477n7m+U2GQc8Jy7F7oBdlegp5l1JTpL8UqmPFxh8HfcvegVBtmRO1NDXs4hysvpZlwa8nJSgU2yeVlKQ+cktJ6X1cpJqLu8VE62kyr0la1p6LxUX9mxeVnTuxa4M9edQ9w5CngdeLaCzV8mfQZ3n/DYyyHOPh4zYwRgwDpgojufAw4w48DMMf4GxKfF3dnuzgUhwScAfTNt7hG2yaO2nonbgZkNAkYATxQo62JmK4he0B5092ydG4CL2fEyWZIDD5jZUjObXKB8P6IX11vDZZebzax3if2dAdy5w0HcXwa+SzQHbSPwprs/kKkWrjDYHma2M1cY6kWxvGrr9q3u14wJwFJgN+CAkJenhys2Sam8bGXfysmENuYk1EledrKcbKsO7yvLoLxM6IC+EkrnZa77ypoOZM0YEH5+hGhEfkfpLVLuB441o1+YuH4scL87G4GtZowJc3y+CPxnZtvZRBPRuxGd+obonyDbYT4LDEq0t5cZvUP8GWC7O0+HdQM+BGyo4HdoWGa2G/ArYKq7b82Wu/v74RLFPsDocLmhZduTgFfdfWkrhznS3UcSXeKZYmZHZcq7AiOBH7r7COCvQLF5hrsCpwB3FSjrR/SOez+iswi9zezMzO/T4VcY2slTwIFm7GfGrkQvWAsq2H4BcIYZ3cPlxgOBJ1vbrxndgKnAtUBPPjjr0AXYNXOMtZD6YOgC4ItmmBljgDfD6wDAx0hPQ+i02pKTYfu6yctOlpNt1eF9ZSnqK9M6qK+E0nmZ676y1veR/ZUZTxPNjZvizg6nss0YZcZLwETgx2asAXBnC1GSPRWWK8JjEH0i8maiD5o8B9yX2N+pwBJ3XgnHW2HGKqBH4sMjhGP8FXgucTeFAcAyM9YC/waclah+CPB4+OBaHrX1TFzMzLoRJebP3f3XpeqGyxcPkZ5PdARwipltILpsc4yZ/azAti+Hn68SzdUZnanyEvBS4h3s3UTJWsjxwDJ3/0uBsk8Df3L3Te6+Dfg18H8KtGeuux/i7jtzhaEuhP/frxN1fmuB+e5RziWZcVrIy8OBe824P2y/BpgPPE30IjXFnffL2O8U4DZ33gGagV4hL5dmXxfceQboEz70BdF8vueJ8v0m0p+IPhq4d6f/ILVVTzkJ9ZWXnSYnAcy4E/gjMNiMl8w4p0CdeuorMWMDcD3wpdDmlvmf6iuDjuorw/al8jLffaXXwT3A6nkBPw38yjLqfQ98XK3bu/O/J12JBgP7EZ0BWwn8U4F6g4AS9zLEiD79ekOJOv2BviHuCfweOKlI3bHAwgKP9wZ2T8SPAeML1Ps9MDjEM4HrihznF8CXi5QdBqwhOgthwG3ANwrUGxB+fgR4puV31NIe/69+AfhXy6j3aF7vWVluToa6RfOy2jkZ6tQ0L5WT9beor9yhXl30laGs1bzMc19Z83+KPCxldpiTat3Otv+enED0zug54NIC5XcSzXvZRvQO7pwCdY4kuizcTHTJYAVwQqbOQUSfiG0muuw7o0SbinWY+4cXkJUhcXZob6g3nOjDec1Et2LaYVATEvs1oE+JdswKCbea6D6I3QvU+T3RmciVQG5fqPOwgPcAP6uVOv1LfalCHpbWcjLUKZmX1c7JUL/meamcrL9FfWVcXjd9ZShrNS/z3FeGb/EQEREREcmXWs+RFRERERHZKRrIioiIiEguaSArIiIiIrmkgayIiIiI5JIGsiIiIiKSSxrIioiIiEguaSArIiIiIrmkgayIiIiI5NL/B6l6H+cU4y7sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, pred, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, pred, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot images and probability that model predicted wrong"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def error_mnist(prediction_array, true_label):\n",
    "    error_index = []\n",
    "    \n",
    "    for i in range(true_label.shape[0]):\n",
    "        if np.argmax(prediction_array[i]) != true_label[i]:\n",
    "            error_index.append(i)\n",
    "    return error_index\n",
    "\n",
    "# change num_cols, num_rows if you want to see more result.  \n",
    "def plot_error(index, prediction_array, true_label):\n",
    "    num_cols = 5\n",
    "    num_rows = 5\n",
    "    plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "\n",
    "    assert len(index) < num_cols * num_rows\n",
    "    for i in range(len(index)):\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "        idx = index[i]\n",
    "        plt.imshow(test_images[idx])\n",
    "        plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "        plt.bar(range(10), prediction_array[idx])\n",
    "        plt.xticks(Number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find index of wrong prediction\n",
    "## Plot first 10 wrong predicted images and probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[119, 149, 222, 247, 259, 282, 320, 321, 324, 333]\n"
     ]
    }
   ],
   "source": [
    "index = error_mnist(pred, test_labels)\n",
    "index_slice = index[:10]\n",
    "print(index[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_error(index_slice, pred, test_labels)"
   ]
  }
 ],
 "metadata": {
  "file_extension": ".py",
  "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": "3.6.8"
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
  "version": 3
 },
 "nbformat": 4,
 "nbformat_minor": 2
}