From 5adf1768d9809cfee86327ac8704daed9c56806c Mon Sep 17 00:00:00 2001
From: Tarun Chadha <tarunchadha23@gmail.com>
Date: Sun, 28 Apr 2019 22:35:58 +0200
Subject: [PATCH] cleared output

---
 neural_nets_intro.ipynb | 1624 +++------------------------------------
 1 file changed, 106 insertions(+), 1518 deletions(-)

diff --git a/neural_nets_intro.ipynb b/neural_nets_intro.ipynb
index cebf4bb..031aa98 100644
--- a/neural_nets_intro.ipynb
+++ b/neural_nets_intro.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -163,20 +163,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perceptron output for x1, x2 =  [0, 0]  is  0\n",
-      "Perceptron output for x1, x2 =  [1, 0]  is  0\n",
-      "Perceptron output for x1, x2 =  [0, 1]  is  0\n",
-      "Perceptron output for x1, x2 =  [1, 1]  is  1\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Calculating Boolean AND using a perceptron\n",
     "threshold = 1.5\n",
@@ -196,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,22 +206,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "perceptron_DB(X, w, threshold)"
    ]
@@ -262,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -272,32 +248,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perceptron output for x1, x2 =  [0, 0]  is  0\n",
-      "Perceptron output for x1, x2 =  [1, 0]  is  1\n",
-      "Perceptron output for x1, x2 =  [0, 1]  is  1\n",
-      "Perceptron output for x1, x2 =  [1, 1]  is  1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Solution\n",
     "# Calculating Boolean OR using a perceptron\n",
@@ -328,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -337,32 +290,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Perceptron output for x1, x2 =  [0, 0]  is  1\n",
-      "Perceptron output for x1, x2 =  [1, 0]  is  1\n",
-      "Perceptron output for x1, x2 =  [0, 1]  is  1\n",
-      "Perceptron output for x1, x2 =  [1, 1]  is  0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Solution\n",
     "# Calculating Boolean OR using a perceptron\n",
@@ -491,26 +421,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff6cef06fd0>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 250,
-       "width": 597
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
@@ -554,33 +467,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "dense_9 (Dense)              (None, 4)                 36        \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 4)                 0         \n",
-      "_________________________________________________________________\n",
-      "dense_10 (Dense)             (None, 4)                 20        \n",
-      "_________________________________________________________________\n",
-      "dense_11 (Dense)             (None, 1)                 5         \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 1)                 0         \n",
-      "=================================================================\n",
-      "Total params: 61\n",
-      "Trainable params: 61\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Say hello to keras\n",
     "\n",
@@ -625,17 +514,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -647,22 +528,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUwAAAE/CAYAAAAt2PowAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXd8VeX5wL/vOXdl74QkjABhgyBbUIYo7i1qxVG1aq2t1vZXtVWrttVKa5ezddKKAxWkbhygyJS9hADZkJCQnZvcec77++NcQm7uvZk3QGy+nw8f4J73vOc9dzzneZ8ppJT00ksvvfTSNsqJXkAvvfTSS0+hV2D20ksvvbSTXoHZSy+99NJOegVmL7300ks76RWYvfTSSy/tpFdg9tJLL720k16B+T+GEGKhEOIPJ3odzRFC/EYI8dKJXseJQgjxByHEwhO9jl7apldgfs8QQhQIIRxCCLsQoloI8ZEQot+JXldrSCkfl1L+qD1jhRCPCCEWdfeaOoMQ4ishhNP33h/9c9qJXlcv4aNXYH4/uUhKGQ2kA2XA0yd4PT0OIYSpk6f+VEoZ3ezPurAurJcTSq/A/B4jpXQC7wIjgx0XQvxQCLG6xWtSCJHt+7dVCPGkEKJICFEmhPinECKilbnWCCGeFkLUCiH2CiHmNDueIYR4XwhRJYQ4IIS4tdmxJq1RCJHlW8ONvutWCCEe8B07F/gNcLVPe9ve7Np5Qoh6IUS+EGJ+iDVOFkKsE0LUCCFKhRDPCCEsLe79TiHEfmC/77XhQojPfevOEUJc1eYbH/zazwghDgoh6oQQG4UQ00KMixRCvCGEqPSt81shRLLvWLwQ4lXf2g8KIX4nhOj9DR9Het/s7zFCiEjgamB9J6dYAAwFxgHZQCbw21bGTwHygGTgYWCpECLRd+xN4CCQAVwJPN5coAbhdGAYMAf4rRBihJTyU+BxYLFPexsrhIgCngLOk1LGANOAbSHm1IB7fOs7zTf3T1qMudR3HyN9c38OvAGkAj8AnhNCjGpl3aHYAJwCJGI8xN4RQliDjLsJiAT6Akm+9Tl9xxYBDmAwMBG4wDe+l+NEr8D8frJMCFED1AFnA3/u6ARCCAHcCtwjpaySUtZjCKtrWjmtHPi7lNIjpVwM5AAX+GyopwP3SSmdUsptwEvA9a3M9aiU0iGl3A5sB8a2MlYHRgshIqSUpVLK3cEGSSk3SynXSym9UsoC4F/AzBbD/ui7XwdwIVAgpXzVd84WYAmGwA/FUz7NsEYIsaXZtV/zzesF/gTEYjyEWuLBEOjZUkpNSrlJSmkXQmRiCPh7pJSNUsrDwN9p/fPoJcz0CszvJ5dKKeMBK/BT4GshRJ8OzpGCoelsPioAgE99r4fikPSv5lKIoVFmAEeFbvNjma3MdbjZvxuB6GCDpJQNGFr0j4FSn5NreLCxQoihQogPhRCHhRB1GA+A5BbDipv9ewAwpZkArAHmA629l3dJKeN9f8Y3u/a9PjNFLVANRAW5NsBC4AvgbSHEISHEEz576gCMz7Os2VqeBdJaWUsvYaZXYH6P8WkoSzG2oqcHGdKAIRQBaCFUKzC2f6OaCYA4nzMpFJk+zfQo/YES359EIURMi2OHOnZHAASU15JSLpdSno3h5NoLvBji3Od9x4dIKWMx7KGixZjm8xcDXze7/3ifKeCOjixYCDEb+AVwBRAPJAD2INdGSumWUj4ipRyB8ZldhiGkizEeHInN1hIrpTylI2vppWv0CszvMcLgEowf6J4gQ7YDo4QQ44QQNuCRoweklDqG4PmbECLVN1+mEOKcVi6ZCtwlhDALIeYBI4CPpZTFwFrgj0IImxDiFOAW4PVO3FYZkHXU2SGESBNCXOyzN7owBJEW4twYDDOF3aeFtiX4PgSGCiGu992TWQgxSQgxooNrjgG8GA8hM8b7HBVsoBDiTCHEaN/91WFs0TXfe/g18KQQIlYIoQghsoUQMzq4ll66QK/A/H7ygRDCjvGDewy4MZhdT0q5D/gdxhZwP7C6xZD7gAPAet8W9gsMR0woNgBDMATDY8CVUspK37EfAFkY2uZ7wMNSys87cW/v+P6u9NkIFeCXvnmrMGySLR05R/k/4FqgHuNhsLi1C/lMCHMx7IQlGGaCBRhb447wMcfe4wKMz6U0xNgMYKlvzG7feW/6jl2HIWi/w9jWv0Pr5oFewozoLSDcSzgQQvwQ+JGUMtjWv5devhf0api99NJLL+2kywJTCNFPCLFSCLFHCLFbCHF3kDFCCPGUMAKWdwghxgebq5deeunlZKbLW3IhRDqQLqXc4vOCbsYIa/mu2ZjzgZ8B52MEBf9DSjmlSxfupZdeejnOdFnD9AUKb/H9ux7DG9syvu4S4D/SYD0Q7xO0vfTSSy89hrDaMIUQWcCpGN7S5mTiHxB8kNaDlnvppZdeTjo6W5ElACFENEba2M+llHUtDwc5JagtQAhxG3AbQFRU1IThw4MmbZwUNLq9lFY1ogcxa9gsKv2SWovx/v6g65KCI/VoeuD7oCqCgamxiCDfgGq7i4p6Z+ABIDHaSlKMLdxLPaHobgeusjyQesAxxRKJtc/gDs/pKs9Hd9oDDwgFS3I/1IjYzizVD+lx4jyc679uITAnZGCKTgx94knM5s2bK6SUrWWtBSUsAlMIYcYQlq/7MktachBoXpOxL0ZcWwBSyheAFwAmTpwoN23aFI4ldgsNLg/X/PUL3F7/H4DFpHD9zKFcNa3jP4CeyOur9rNo1T6CyEsiLCq/uGgsM0YGWmC25lfwyNubcLq1gHPuv+xUpg49cVl/UkpqNi6j4osX8NqriB5xBmkX3IMlsfMbI0fxLvL+ehW6qyHgWMTA8WT/6r12z+W1V5P/9PU4D1WBHvhgVmzR9Lv5aWJHn9np9R6l4PlbqN9pp6WOo0ZGM2LBeoTaMTGiORvQ3Y2YYpIRwZ6kxwEhRGFnzuuywPSlwr0M7JFS/jXEsPeBnwoh3sJw+tRKKUMF7h5XpJR8seMQ767Lo9bhZvzAZG6YNZQ+8ZFtnhtlNXPT7GEs/GofLo/xo7eYFJJibFwwoX93L/2k4dsD5UGFJYDHq1MZQoscl5XEwNQYcg/XNT10LCaFjMQoJmWndtdy28RdUUTRK3fhKNzepFXVbFhK/Y7PGfLgZ5jjOifIbZkjUaxRAQJTWCJIOK1jVeMOLvoVzpK9oIdIapKS6GFBK8h1mMaCbQTbEOoeF966I5gT2ueO0Bz1HFx0L/U7vwDAFJNM5nV/ImbEGWFZ5/EgHDbM6RhVZ84UQmzz/TlfCPFjIcSPfWM+xij7dQAjwyJUJsZxZ+HKHJ75ZBcFR+qptrtYuesQd764moq64D/yllw+dRC/u3oiU4emMjwznvkzhvDsracTZTV388pPHlJiQ2+dFUUwom980GNCCBZcN5Wrpg0mLS6C1LgIrpg6kL/ceBqqcmI0D3vOWvb9/mwcBVv9t6C6huZsoOLLznfSEIpC/1ufR7FGISwRgECxRBKVPYXE0+a1ex7d1Yh991egeYJdBWGJoP+t/0Qxh8ekEVIgSokaFfyzDUbhv26lfucXSK8b6XXjqS6h8F+34izZF5Z1Hg+6rGFKKVcT3EbZfIwE7uzqtcKN3elh6YZ8vy21LsHp8bJkfS63z21f2cNxA5MZNzBY4Zn/DS6fOogN+8twe/21EAGMHZDEsIzQPyqrWeX6mUO5fubQbl5l20gpOfifXyI9IR6WmoeGfV0roB41eCLDH1tHzeYP8dZXED1kKpHZkzu0NdW97pDHFEsEwx5bh6kDgqwt0s67i6JX70K6HU2vCbONhKlXoliC1pMOwFWeT2P+VmSLtUuvm4ovX6Tv9R2uQHhCCJvTpydSeKQes6oE2CC9mmRHYVWX5vZoOkVH7MREmEmNa9+Xqqcysm8CPztvDM9+uhuvpuPVJRaTwjXTs7l6+uATZqdqL1JKGnLWULV+CZ7astADhcCc3HVTixoZR9IZ85uu3Zi7EUfRTixJ/YgZPRuhmnGW7qdq9Rt4a8uJGTOHuPEXoJiNFHZTVDyW5P64ynL9J1ZUYk89L6zCEiB27FzSr3iIw8sWIDU3SEnC1CtJn/dwu+fwVB1CqObAh5Gu4SrPC+t6u5MeKTAbXB4+3VrMtvwK0uIjuWRSFv2SO+6RTo6x4dECPZYCyEgIWkymXXyx4yDPfbobXUo0XTIkPY7fzptAfJSVynon+0trSYqxkd0n9qQXJu1l7rh+zBqdQaHvIdEeG/DJgJSS4ld+Rv3OL9Hdja2OFWYbKWfdHrZr624n+U/Px3nwO6TmRahm1IhYks+5k8NL/4DUPKBr1O9aQcWKlxn8yyUoFmOb3feGJ8l/aj661wuaG2G2odqi6XPJvWFbX3OSzphP4rSr8dSWYYpObLdmeRRrxrAA7RJAmCxEZfecHJYeJzBrG93c+eI31Dk8uDwaqoDPthXz0LwJHXYUpMVHMqZ/EjsKK/0Ep8WsMG/aoE6tb++hap76eFeTE8h4rYYH39zImP6JfLCpELNJQdclafER/HH+lO9N+IzFpDIkPe5EL6ND2Peson5XO4SlJZK+N/6VyKzWCr93jPJPnsZRuBPpdQHG9lR3Oyh9+7d+9lPd3Yjr8AGq1rxJ8myjI0XkwPEM+uUSqta/i6eimKjBE0mcfg1qZHjef6l5qNv+GfactZjj+5Bw2jzM8X06HSVgjk0hYfrVVK9759jWXigolkiSZv0wLGs+HvS44htvrd5Ptd3VJJA0CS6vzl/e3xE0HrItHrxyPFOHpmJWFSwmhcRoK/dfdipDW7G7tcaS9fm4Pf6eS02X5JfX8eHmQjyaTqPLi9OjcbDCzmNLtoSYqZfjQe2Wj9BdwYWlMFsRJguxp57HyCd3EH/qeWG9dvX6d5qEZRNSDxqnKT1Oaja9D0Bj/lb2/W4OuQsupvrr/yA1NwlTrwybsNTdDnL/fBkHX/sVVd8sovyTp8h5ZBb2nLVdmjdj3qOkX/YAltSBqNGJxE28iOxff4Q57sRFRHSUHqdhrs0pwxskhqXR7aW0upHMxI5tpSOtJh68cgINLg8NTi/JsTaULmyTj9Q5gkbk67rE20KgaxL2l9ZSUeckuRVPcy/dhzDbQCgBQkpYIkg64zqSZt6AJQx2y6CECgkKgRoRg6e61NiKNwtNsu9dTd4/fsCQB5aHxcRT+dVCnKX7m+yNR7fSxa/exfDHv0UondOzhKKQNPN6kma21srp5KbHaZiR1uAyXtclkZbOy/8oq+Gc6YqwBJg4OAWzKfBtDRWnqAhBoytYeMiJp8Hp4b0N+Tzx3lYWrzlATYOr7ZOOAw0uD8UVdj+zR2dJPG0ewmQJeF0IQdqFv+i0sNQ9Tmo2f0jFildwFO0MOiZ2/AUItWX4mQDVTMvAE2GJJGnGDVSufsOwbTZH8+KpPEhjfnh2K9UblwWNFNBdDbgO7w/LNY4nUkoacjdy5PN/Ur1hacgdRXvocRrmpZOyeHb5d34/FlXA8Mx4EqI7Wgg7/Fw8KYuPtxRR2+jB67OL2swqg9NiySmtwav5S06rWSXzJEyhLK918LOXVuPwaLg8GmtMCovX5PK3m6YxICWm7Qm6Aa+m88wnu/hixyFMqkBKuOb0bK7pgic+ov8YUi+4h/IP/wqK6ptHMuD2F1GsxxxXmqOO6g3v4SrLJXLAWOImXBAyztFZso+8v11lxBtqHlBUYoafQf9bn/fLiulz0S+x7/0Gb00ZuqsBYYlAMVnoe+NfOfTGb9BddpAgNS9JM68nZswcqjcsDeo8AfBUFsOgCZ16H5qjmIL/jqSUQR8uncVbX0n1t+/hqS4hKnsysWPO6nDWUFtIzUPBczfTmLsJXXOjmKyUvvNIp+frcQJz7rh+7CutY/m24ibnSZ/4CH5zxaknemkAxEZYeO7WM3hnXR4b9pcRH2nl8qkDGd0vkZ+8+A11jW5cXh1FgFlV+MVFp3Q4SPtAaS0fbSmitsHFacP6MHNUOhaTGtb7+Ndn31HncDdpxm6vjser8/cPd/C3m6aH9Vrt5aUv9rBi5yE8ms7R5+Wbqw+QGG3lnHH9Wj+5FVLn/piEyZdRv+drFLONmNFzUG3HTDvOwwfIe/JydK8b6XZQbY2k7KO/kn3v+5hikvzmklJS+MJtaA3Vfq/X7/2GqjVvkjTj2HZUjYxjyAPLqdv+GY0FW7GmZBE/6RLUiFhiHptNw751eO1VRGVPxhxvdKKIyp5E/e4VfjGRAFLXsPUb3en3oDmJZ8ynpHRfi2sIzLGpmJMHhOUajflbyH/qOqTuRXpcVK95C0vqQAb/4h2/B1VXqVy1iIbcjU33omveLs13UreoaC2X/Eidg/0ltSTF2hiaHndShud4NZ2t+RXUOzyMGZBIpNXEJ1uK2ZJ3pCkcKiu1Y9ra8m3FPPvJLjyaji4N7bV/SjR/ufG0sArNi5/4NOiWVxHw/q/Pw6weX2uOV9O5/E/LcXkDHSIZiZG8eufsTs3bmL+FqrVvo7sbiR1zNt76Sux7vsackE7SjBuwZQ7nwJ8uxdEyPVAxkTDlCvpe/ye/+Vzl+ex//LwAgQZg6zeKIb/+uFPrPIrmqGff7+bgra9osoEKs42YkTMZcPsLIc9zleXiLNmHNW0QtozW2jKB1HWKF/6cuu3Ljf/7wptQzZhjk8mcv4CYkS3bubcfKSU5D52Op+qg3+vCbCX13J+Set5dnZ67JfsfOxfnocD+f6c8X7RZSjmxo/P1OA3zKCmxEaTEnrwB4QXl9dy3aD0ujw5IvJrk8ikDuenMYVx5WudClhxuL89+sstPaDg9GoVH6vli+0HOnxCepz+ARVVCCEzRZTtvZ3B6tKDVkABq7KEzX1qjfPmzlH/ytGGvk5Laje+DEIYDSFGpXr+EzPkLcBTtICCXWvdSt/1TaCEwZSsajNS6bnNVI2LIvv9Dyj54krodX6BYbCSecR0pZ90adLzucVH04h3Yc9YgVBNS14gccAoD7njVT4tujlAU+t/8FM6SfRS+cBvuCl9lRs2Dp7qUwhduJ/ve/7YpeEPhPlKI114Z8Lr0uKj+dllYBaYMEnHQFXqc06cnoEvJg29+S02DG4fbi8Ot4dF0/ruxgI0HjnR63u8OVqMG0excHp2vvwtvLZO54/piaeG8MqmC6cP7nJA87yirifio4La1YZkdDwHz1JRR/tE/DE2waZclj3nLdQ3pcVKy+KEQhQgBJVDfsPbJDhreI8w2EqZc3uF1BsMcl0rf6/7EyD9tYfgf1pJ6zk+COI8Myj76G/acNUiPE91pR7odNOZvbZcdT5jMeKpLQfd/CEivmyNfhmr93jZCNQUNnWo61gV0j5O67Z9RvWEpnpoyEqZcYURChIlegdkN7C+txe4M9Hw7PRofbu5UVSkAIi0mQplQoiPCW+zjxlnDGNk3AatZJcKiYjOrZKXE8LPzx4T1Ou1FCMEd54zE2kyICwyTxI/mdLxmqn3vaoTaDhOG1InIGguK/1hhspIw9Yqg6+x/y7NGgQ1fKqNijcKWOYKkmTd2eJ1dpXr1mwEeb+l1U7NxGVJvXfvyVB1CmIJ8r3QNd1l+p9dkSeqLJTmLlkVShdlG4vQfdHrexvyt7Ll/EsX/voeStx4k57enozkbiOg/GsVqaNNCNXfJcdVjt+QnM063hghRj6TR1Xmj87DMeKJtZhwt6kdazSoXhXE7fnTOBddP5UBpLfnl9WQmRTEiM/6E2opPH5FObKSFV1fsJb+8HiEE2X1icXYivEixRhjxl20gNS/pVzzEwYV3462vNLbciootczhpF/4i6DlRgycy7HffGB7gmsNED5lq5IgrgQLaU3OYxoLtmOPTiBgwNuzvrx7Elnr0vgwtL/R7YMscjvSESGcc0rV0xgG3PU/uX+YhPS5fJIFC9NBpfk6xjiA1DwXP/hDd4V+7vOLLFxhwx8vUbFhGzfp3kLrWrs89FL0CsxsYnhmPHmQfZzUrzBqV0el5FSF47NrJ3L9og09oGrbRa6YP7rZqSdnpcWSfROmOaXERFFU04PEaRT52FFbxmze+5Z4LxzB7dPvT9mJGtcNJpKhY0wYTNWg8Qx/5CvueVbgrirBljiRy8MRWhZspJomUOT8KeVxKSek7j1C1+k1Di5M65oQMBt71Bub48BVOjh4+nfrdK5uZHQwiBoxtc/trikkmcfo1VK17u1k6o4pijSTJl6LZGTw1ZTTmbyNj3iNIdDR7NZGDJhA54JROz2nftx6pByoj0u2k+JW70OorfC/IkOaA9vC9EJiaLimusGOzqF0q+tDg9FBld5EWH9Elj7PVrPKLC0/hL+9vx6vpaD5vdlZqDHPH9e30vAADUmJYdPccdhZWUu/0MKZ/Ykjb3veR11btp9Hl8UsEcHk0nv10NzNGpqO2MwtFsUSQ9ZNXKXjuZkAipTSEglCMAhdSYopNYcCPDVudUNT2Cdl2UvPte1StXYz0uprSI13l+RS9+GMGd6DyelukX/kwjXmb0d1OpNeFMFkQqpnMax9v3/lXPYo1fSgVK19Gb6wzKs9f/CvMsR3u7gBA+fLnKP/o74Y5RCggJVk/ebVLwhIIXZIPiRbEwdRZerzA/HZ/OU++vx231/Ci9k+O5rfzJpDWAcHp0XSe+mgnK3eVNAVEz58xpEstJmaOymBgagyfbC2musHF1CFpnD6iD6YwhOOoivifrb+5Lb8iaNaUx6tTVuMgowOpsVHZkxmxYDP2vauRHidRw6YjvS4a87dhik0mcuD4bjNBVH71amDoka7hKN6Np7q03VXM28KamsXQh1dQuWoRjsLtPlvqDU1xnW0hhCBpxnUkzbiuy2tpzN9K+cdP+R4Sx14veP4WRizY1KWCx1FDpgSPUAiS9toVerTAPFhp5w/vbsHlPWbDyiur575FG3jlzlntDn95fvluvtpd4hcQvWjVflJibR3a5rWkf0oMt88d2enzTySNLi9rcw5jd3o4dWDyCcvuaUl8lJUjQarha7okJsLfmC91ndrNH1C1djHoGglTryR+8mV+W1HFbCV2zBy/8+LGndM9i2++XkeQxmWAUFU0p51wuvBMMcmkXfDzMM7YOarXvR1YbAQAiX3PamJPOavTc6sRsWRc/TtKFv+2KW5UWCMRJit6iySCrtCjBeZHm4vwtvD06VJS0+Bid3E1Y/q33dHO7dX4fPvBgCLCLo/Gm6sPdElgnsx4NZ0PNhfyyZYiNF0yZ0wml08dhM2ssru4igfe+BagKfZxzimZ3H3+mBPi9JFSsmR9Pu+szaWmMdAJYVIFk7JTiGkRKVC88OfU7/i8qXRbY8E2Kr7+NwNuewFLYvttybrHRcO+tehuJ9HDpoWlKlDs2LlUrnwlIM1RmG1Y0zoXp9sdSM1DxYpXqFrzJlLzEj/hIlLO+QlqRMcfoJrbEVzbk0Y4UFdJnHY1kVmnUrX2LbTGWuLGncuRz/9FY+7GLs99lB4tMMtqHUGDmR1ujSeWbuHeS09lbFZSkDOP0ejytrSHN1FlPzmKTXQHj769me0FFU1B8G+uPsDanDL++sOpPLJ4U4AnfuXOEiZnpzJtWPu2cuHkP1/tY8mG/JDFNgRwz4X+4U6Oop3U7fjMb9srPU6cRTvJ+e0ZxE++jL7XLQjquW5OQ+4mCp67qemHLjUP6Vc+3FQxvbOkzL2D2s0fGp53j8PIYzdZ6Hv9k22u6XhS8M9badi3rslGWLHiJep2fkH2rz9C6WB4Tvz4C6jftjyg9qjUPEQPC0+6rS1jKBlX/hYw8v8LX/xxG2d0jB4dhzlhUDI2c/AvV0W9i4fe+pYDpbWtzhEbaSHaFrgBEsCIzIRwLPOkI6ekhh2FlX4ZQ26vzsFKO0vW5zcVDWmO06OxfGvx8VwmYGj6rQlLAJOqsL1FSxH7/vWhM2t0L7VbPqTiy5dbvbbudjaFquhOuxH47XFR+u7vcB7a2+F78VtzVDxDHlxOn0vuJWbUbBJPv5bs+z4IMA+AT8PN24zj4J6QcbjdgaNoJw371/s5VKTXjafqEHXblnd4vpjRc4gefjrC4vMvCBVhtpF+5UOYosP/W6vfsxq6mDvekh4tMOec0pekGFvIvGa3R+etNQdanUMRgh+fMxJrM8ErhOHpvvnMzqV+nex8V1wdUjPPPVwXEFB8lGB1SLubarur9Q57GEL1UKV/61pTZDxKsKBrH9LtoPLrha3OW//dVwHhOADS66Fq3dttrKptVFs0yWfeTNadC8m85g/Y0ocEjKnZ8jF77htPwTM3kPvk5ex7ZBausuPTA6cxf2vQ+9ddDTTkftvh+YSi0P+2fzHg1udImHYNyWfeTPa9/+107GVbVKx8Jexz9ugtuc2s8vQt03nxiz18uq044LOVGDndbTFrVAbxkRbeWH2Aw9WNDMuM5/oZQ+jfSUeH26uxJa8Cj6Yzom8Cew9W4/bqjB+UfFKEACXH2DCpgpZKm9WkMCwjjs25FQHn2Mwqc8Ycf3tuQrQ1ZGbiUay+kK3mxI47l5I20v80Z3DHy1GMuolBri41dEfb36uu4izdz8F/3+On4bmPFJL392sY/ti6bt+6mxPSEYoa8A4IsxVLUufqhApFIWbU7LCGaIXCVZIT9jl7tMAEiLKZ+dFZI/hy56EAx40iaHePmXC1yt1eUMkji40KS5qu4/LqmFWBSVXQdMlNZw7n8ikDu3ydrjBlaCoWk4rTrfn9GBRFMHdcf/olx/D4ki3o0gi5splVxmYlMbMLQfedxWpWuWxyFu99WxB0W25SBHGRFiKtJhxuLxG+ItJqRAwDf/YaBf+81YjDa/k0FQoxI2a0eu3oYdODhqoolkhix53b+ZtqJ1XBigUj0Z12GvatI3r46d16/ZhRs1Bs0Ua2UDNnjVDNQdNCTzbMCRm4WmT+AAhL58OXevSW/CjRNjPnj++P1ex/OxaTyjWnZx+3dTjcXh5evJFGt5dGt7fJRujRJA63hturs3DFXvYcrGJLXgW7i6s61Ydoz8Fq7lu0nqv/8jn3vLqWLXmBGmFrWEwqT954Gv1TorGYFKxmlbS4CJ64bgpxkRamDk3j5Ttncf3MIVx52kAevXoij149sctFN+ocbpZuyOP4Tf2UAAAgAElEQVQfH+1k+bbidqc03jh7GNfNGEJcpOFkiLSasJgUbGaFSKuJKruLhxdv4uq/fM7SDcdynCMHjmfEH78l45rHwGRpSokTJgtqZCx9Lr2v1eua49NIveBuhCWiyUyhWCOJHDLluGhI3tqykG0svPbwhcqEQqhmBv/yXSL6jTYC3s02LClZDLzrDUzRbUegnGjSLrzH+OyaISwRJM8OnYHVFj22HmZLdClZsi6PJevzqXe6GZYRz4/njux0M7POsHLXIf7x0c4AD3NLVEVgNatIKYmwmPjDDyYxuE/7NOGdRVU88PoGP4eN1aRw76XjOH1Ex4Ody2oa8eqSjITIbg0ZKjxSzy8WrsWj6bg8htYaE2Hm6VtO71ClfCll0zrvfmUN+0tr/eyxVrPKI1dNZPwgY7fgqS6lZMnvqd+1AnQNNTqJ+MmXkDLnVkwx7dtRNORtpnrNW+juRuLGX0js2LnHxZNdvf5dSt56KMCrLExWhj36ddiC29uDp7YcqXmNbfpJWHs2FFVr3+bwsifQHfUIk4mk2TeTduEvUVT1xNXDFEK8AlwIlEspA8o+CyFmAf8Fjj7+l0opfxeOax9FEYJ50wYzrwvZOV3F4dbapTFqumwqwuFwa9y/aANv3HNWu4ryvvj5dwFFdF1enX9+9h3Th/fp8Je5IxlRXeEv72+nweltMgE4PUbJu5dX7OX/Lm5/69qj91dS1UB+WV2A88rl0ViyPo/xg5LRHPUcWHARXntVk6am2atw5G9rt7AEiBo0gagwtH7oKHETLqJixcu4yvKa7JhGb5/rjquwBMLe2bGxcAdVqxbhtVcSO+4c4idegmIOv30/cdpVJEy9Es1Rh2qLClkGr72Ey4a5EHgG+E8rY76RUl4YpuudlEwYlBwyprM1vLpkS94Rpgxpu+hCblmgTQbgSJ0Tt1f38/aHm93FVbz85V7yyupIirFx3YwhIQP7NV2yvaCSukY3g9NjOXC4LsB5oOmStTmHgY73+q5zuI3aoEEqsFf7mrVVb1hqOHaabWul10Vj4XYaC3d0OX+5u1HMVgb/31KqVr9Bzab3USNiSJpxAzGnnN3luXWPk+p171Cz+QNUWzSJZ1xHzKjZbT5wGwu2U7d7JaotivgJF7U7xbI5lavfoPSd3xlZP1LHnrOGqq//w6Bfvtul9MhQCEXBFBWenWZYBKaUcpUQIiscc/Vk0uIjmTdtMO+uy8Pt0QIEhCKCd4+UUlLvaF/nSJOi4A0RX1jncJNi7p4q9HsOVvPrRcdMAQcrG/jbhzuxOzxcNCnLb2xxhZ37XltPo9vQoj1ePaTmbe5ky9aBqbHoQd5Ms6owJdvQhhyF24O2igCBs2RvWASm8/ABShY/RMP+DShmK/FTryT9st+gWMLzOSiWCJLPvIXkM28Jy3xgBIrn/e1qnCU5Te+PPWctSbN+SPql9wc9R3M5yF1wIa7DR8P0BIf/u4B+Nz3doX7tmtNO6TuP+sd2uh04Dx+gesNSkk6/ttP3dTw4nk6f04QQ24UQnwghRh3H6x5Xbpg5lD/On8zccX2ZPTqD288ewSWTBnDOuL5cNW1w0EB7TZdtZiQdpWX631HMqkJFkBzroxgtHgK1sdV7SvnZy6uZ/48v+dOybRyuDt6C9NUVOYGmAI/Gwq/2+c0rpeS3b22kyu7C4dZwuDW8ukRgPDCaYzEpnN3J6k1Ws8ptZ4/w06jNqkJcpIXLphpRCLb0oUGrbQsB1pSuRyp46o6Q++fLaMhZB7qG7mqkeu1iCv7ZeafC8aB2y8c4S/ybnEm3g8oVrxgV1oOQ//ermglLAAleD8UL70ZzNgQ9JxiNeVuCboul20Ht5g/bPc+J4niFFW0BBkgp7UKI84FlQGCULiCEuA24DSClTwaL1+QydWjqSVP8oT2M6pfIqH6BXkRN19lVVMWBw3VNHmKbWeXSyVnt7k80OTuVj7cUBcbGCchMCqzUs7u4in98tJPiigZURTB7dAZ3njsKm8XEO2tzWbRqf9NaVu46xPp9ZTx/2xkBts288uCmALdXo67R0+S4yS+vp8ruClifLo16oIoQ6LrhuBmSHsf1M4a2676DccGEAfRLjmbp+nwq7U4mZady6eQsYn1FOBKmXUX58meRHhdN8ZSqGUtyfyIHd9jeH0DVN6/7NKVjdys9LhpzN+Ms2Ycto/P31p1Ur38X6Q58MArVRMOBDcRPutTvdVd5Po6iXcEn03Xse1e3u2CJGhEdsnqQGqZtc3dyXASmlLKu2b8/FkI8J4RIllIGxMNIKV8AXgCIzxwi//1VDotW7eOyyQO5uROtCE4mVEXhieunsmLnIVbuKiHConLBhAFMHNz+2oJXTRvMil2H/GIorWaV807t1yQojnKw0s5vXv+2SSDqmmTlrhKq7C4emjeB11bt94tt1CU43V7eWpPL3Rf452anxUUENRsoQvi1x3B6tJBVorJSYvjh7OGUVjcwuE8swzI6X8G9psHFip2HqKh3MndsX6YMTQ2ohWmKTmTw/y3h0Ov305i/1ahpOXYumT94PCye3oYD3wbvES41XIf3n5QC01mSgz1nTfCDQqBGBaYousryQFEgSMosutah9zJiwDjUyLjApACzrVMZP976Cso/eYa6nV+gRsSQfOYtxE+5ots8+cdFYAoh+gBlUkophJiMYQpos6qnxNiuarpk2cYCpg1PY3gPz+82qwrnjOvX6T7afRIi+cfNRnbTrqJqYiLMXDFlIBdPzgoYu2R9Ph6vv73To+nsKKxkW34FapAvlSZhR2HgR3PDrKE8tmSrn4C1mlUumZzl593P7hMbdN1Wk8Ls0Zm+cJ+uJQjsOVjNr1/fgKZLkhxFmJyrqFbqGD/nElLPuAbVFt001pY+lMH/txTd40IoapebbDVHa6gJ+rr0ujEnda1QdHdR9uHfQsZ2CpOV6GHT0b1uKle+alQo0jViRs4K3QhOKESPOKPd1xeKQtZP/0Pe365Cs1fTNLHmxXloL9FDTwt5rtQ81G3/jIa8zViS+hEzZg55f5lndKDUPHiAQ289iKNoJxlXPdruNXWEcIUVvQnMApKFEAeBh8Eo6Sel/CdwJXCHEMILOIBrZAcDQN1ejZW7StolMEuqGqh3ehiYGhO2Xt2arnOoqpEoq4mkmPB78jrCgJQY/vCDyW2OKzxSjxbkXTarSlNYTzDS4gLNA1OGpPHzC8bwwud7qHO4sZgULps8kOtn+WtRFpPKPReN4cn/+lebz0yM4rzxnUunO4ouJR9sLOBfn+9B0yVjHFv4Qc2/UdFQ0Sl/P4faVf9myK8/DCjB1h0hK5ozRHqkULscvtJdGC2DgyHoe/2fQVEpfOYmQ3v2OWaq1y5GsdgMrbDFdjpz/oIOO7isqQN9iQDNvpy6l8PLniBy4KlEZo0LOEdz2sl98go8lcXorgaE2Ubp0seNOZplQ0m3g6rVb5Jyzk8wx4Wv1cdRwuUlb7XVm5TyGYywoy5eqPXDlfVOHl68iaIj9aiKgkTyk3NGMbeT2txRVu8p5R8f7cTt1dF0yYi+8TxwxfiTIi+8NUZkxpNTUoO3hdR0e3VG90tk4uAUNuUe8ROcVrPK1dODZ0edOSaT2aMzaHR7sZlNITN/ZozMICslho+2FFFldzE5O5WZo9K7/PD6+wc7WLG7BE2XKFJjXu3rWDj2Y1E0F97awxz58iX6XPTLLl2rPVhTB+KpKAo8oChhj1sMF5aULDxVhwIPmMxEZU/GUbCNhtyNLSoUuRCqSvSo2TTuX4fudmJJyaLv/AVEDWn7wd0SowJSYOlE6XVT9c3rQQXmkeXP4S7PbypAHLolBaCacRTtwjzmJBWYxwOLSWXW6NZzmR98cyMF5XW+0B1DCDzz6W76Jkczsm/ntvK5h2v507Jtfh7i3cXVPPjmRp75Uffm8naVy6YM4pOtxXib5UMrAiKtKt/sKeXuC8fw7Ce7Wb+vDFURmE0KP547slWPvRCCKGvb2lP/lBjuOCd4MISUko+3FPP6N/uosrvISIjk1rNGctqw0F/w8loHK3aVNAn3NG8pCoEasvS6qdu2/LgIzNRz7qRh/7dGPUsfwmQlZvSZHQqM7yxS12jYvwGtsZao7Entumba+XeTn7fFf81mG/FTLkeNiKGxYBsEiabQXY1YU7MY+JOuVwAKWfRE6miNwcsx1mz6b4hq7UGm8bjC2kiuOSe1wBQYaYQmRXDxxAGMaEXoFZTXc6iqISDO0e3RWLYhv9MCc+mG/ICtq6ZLiirsFJTXB1TJOZnIKakmwmKioVlrX11CbaOHV1fu5fMdB/n7TdNwe3XqGt2kxUe0u4lYV/jvxgJeWZHTZA89VNXIH5du4aF5E5iUHVwzO1Bai1lVmj4Ll7ChhPK2Rga3o4YTqXlpLNqBYotC8zpBKAhFJfbUc+k7f0G3X99Zup/8p+ajO+0gBNLrJuXcn5F2/l2tnhc1ZAop59zBkU+fNQSQohI/+TIyrzYS78zxfRAmc4BwEmYblsTw2GWjsqcEKSpiZDHFhojpFEoHRJUQ2Pp2T+TiSV18IznWxg0zh/LULafzo7NGtDq2usEVdIsogYr6zpe/L691BA02VxVBZRfm7W625FWw4L1tIe/d5dE5VNnA17tLibaZyUiMOi7CUpeSRS2882Ckd76yInQ5rpS4CL/g9ypTMmWmdLQWX2GjuELnW8C2l6JXfkbZ+08a7VulBARqTDJ9r30ibEHroZBSUvDsjXhry9FdDUZhY6+bI589h33v6lbPrVq7mCOfPX9MIAqFum2f4q03HH0xY+YY62/Ru1uoJuInXxaW9ZuiE0i75D6E+VhRE2GJIKLfSOInBE8GTJh2ddCY2mCokTHd5iU/qQVmfJSVa07PbpcWNyQ9LmilcJOqMCm7cy1BAU4dmIzFFPg2eTWdwSE8wicD//4qMNC8JU6Pxob9ZcdpRQYOt7cpj74lJVWhA6Cz+8TSNynK76G4MPF2Kk0pSHMEii0aYbKSNPNGYk89P+zrbo6zdD/1u1b429F0L1pDDdXfhq9Fbigchdt9Hnr/J7lRFDl0drLudVP67u/8s580D5qznvJPnwVAMVkY9It3sfUdiTBZDc0ydSAD734zrFXRU868hUE/f4P4KVcQc8rZZP7gcQb9/K2QzrLkOT8iavBEhCXSqJxkjQoQ6gCoJuLGdl8Tu5N6S94Rom1mrpsxhNe/OeCnvXg1naXr8oiymnF7dZJjbEwbntZuB8RFE7P4cFMhtY3uporjNrPKRRMHnNROn9aEz1EUAYnRx9fjH2ExEWExYXcGbslaa5ErhODx+VP407JtbC+oNLJ14jJIuuEjspUSvHVHiMga2+l+2R3BUbgDoSgBPkjpbqRh/4Yu9/tpC2MbHlzX0YLUfzyKu7wgaAV1NC/2Paua/mtNzWLIrz/CdaSQ6jVvUbdrBSWLHyLxjPkkTLkSEaadSOTA8UQOHN+usYrJQtbPFuEo2EpjwXbM8X1QImIo+uet6JoHNA/CHIEaGUvqBfeEZX3B+N4ITICrp2eTmRjFY77it0epc3p59tPdgCEkrGaVv988nax2ZA/FRJh57rYzWLwml/X7yoiJMHP5lEHMHHV8q8V0lKzUGHa06HPTErOqcMGEroX6dBRFCK6bMYRXV+b4x3SaFG6a3XpLkLhIC49dOxm704PTrZEUY/VtvbpfSDbHnJgJQRpnCJMFS2pWt18/cuB4pB6opQtLBHHjQ9e3UaMTgvfuBkwtvPpS81D88p04S/c3adIlJTnY966h/03/6MLqO48QIkDIDnlwOZWrXsN1OJeooVNJnH4NakT37fx6lMB0ezU2HThCg8vLqQOTSY71146klHyzpzSozfEoujRKqv3i1bUsvbd9qnt8lJXb547sUT3Gfzh7GL9+fQMuT7NK2RhV1a1mFST8/MIxJyTl9NLJWVhMCotW7afa7iI9IZJbzxrB5CHtC8WJtpmDNq47XkRlT8YUl4q7osgvCFyoJpKmtxph1yGOhio3t8dJKfHUlpN63l2Uf/yPYz24LZHY+gwm4bQr/c/XvU3bXHNsClFDp2LPWesXuygsEaSc7d9dsW77Z7jKcgOKZNRt+/SkSvu0JPcn/fIHjtv1eozA3HOwmgfe+BYpJTqgaZIfnJ7N/BnHUtKXrM9j9Z7D7ZqvweVl44EyJmV3T/jBiWZUv0R+f81kXvjiOwrL7SREW7j29GyGpMfj8moMSY8LW1B/RxFCcMGEAVwwYYBfQeCeglAUBt2zmOJX76IxdwsIgTmhD31v/FtY6lS6K4o49OYD2HPWIBSVuAkXkTHvYdyVxRS9eAeeuiMgJWpUApEDx4GuETv2HOImXIRitiKl5Mhnz1Px+T/RGuswJ2WSfsVDxI07l/43P03hS3fSeOBbwxuua6Rd+MuAbpX2nDW+9MVAGnI3njQC83jTIwSmR9N58M2NfuExAIvX5jI2K4nR/RORUvLm6gMd6my4q6iK8YNSu9x64WRlbFYSz/6o/Wlr3UlNg4tv9hzG4fYyaXAKA9OMbVNPE5ZHMcelMejni/E21CA9LkxxqWG5F62xlgMLLjbiEaWO1DVqN72P8+BuXJUHkc1iGL01pdj3NjD8sfWotmP23/KP/86Rz//V5NzxVB6k+NW7UX78EjEjzmDQXYvwVJfiqTuCLX1IUK++OT4dYbIE5MoLxXRc7MQnKz1CYO4oqAxa+9Dt0fhkaxGj+yfi8mg0utrXI+Yoi9fk8fbaPE4ZkMRdF4whsxWnQ09E03V2FlXh9uiM7p9IpPXEfNwb9pfx2JItxpo0yaKv9zF3XD/uPHdUjxWYRwlXYdqjVK9fgu5x+qUgSs1jVF0PdoLupW7rxyScNs/3XzcVX7wQUAdUepyUffAkMb68b3NCeqvacMJp8yhf/myLVwXCbCV61KxO3Nnxw1tfQeU3r+Ms3o2t32iSzrg2bIkEPUJgurxaMBs7EnD6+udYzSqxkWZqGoJUjwmBxHAabi+s5O5X1rDwp7NPqG0snOSU1PDgG98aoVZCoGk6d10whrNOOb5FIZwejceXbPWzpXp1yefbD3LasDQmDPrf1VaC4SzZG7TosZQ6BHHY6G4XntpjoWFeezW6N3gxaveRgnavwxzfh6w7XqbolbuQbgdS6pjj0xlw+wsoJkvAeN3rpn7XSjzVJURmjSMia9wJeRg6Dx8g98+XIT0upNdF/XdfUbHiJbJ/9R7WtK63r+kRAnPsgKSgMZY2s9rU+lUIwS1nDueZT3cHeF+vOT2bxWty8Wp6UwWk5khp5Fd/vuMgl00+sS1w24umS3YUVlJtdzGyXwJ9mtWvLCiv4//+vS6g7fA/PtrJ0PS4Tvdb7wzb8iuClntzejS+3HGoV2C2wNZvNMLyfoDQFIoKihqQg61YrEQOMmp7eusrKXjqWj+HTnOs6R2zO0YPm87wP6zFnrMWc2wKtn7BdwTuiiJyn7wC3dVoOKEUlajBExhwxytBhWt3UvLWg76e8cZv3BCcbkoWP8zAuxZ1ef4eITCjbGbuPHc0z326C4+mo/sq4Izun8j04cd6iswd1w+rWeU/X+2jvM5Bv+RobjlzOBMGp3DVtMHklNSwYV8Zy74tCFo9PL8sRPWZk4xDVQ3c99r6plhGryY591Rji/vOujz+vTInqC1X03Q+3VbMbWcfP29/a03hTuaOpSeKhMmXUf7R39G87iYPvDBZsKUPA7MVZ9HOYw3RzDYiBowjasgUAA6+9itcIbRIYbbR5+JfdWgtld+8zuFlT/g88Trxky4m45rHAio/Fb18J976Cj8zQsOBjVR8+RKp5/ykQ9fsClJKGvZvIKBKj5TY960NyzV6hMAEOPfUfgzPjGf59mIaHB6mDe/D5CGpAdrLzFEZTVpnc0yq0lQF/b8bCwOO28wqQ9Nj0aWxXfx4cxEeTWf2mAwunpjVrc3FOoKUkkcWb6Ki3ukXg/z59oOkx0fy2tf7Qjq+NAn2dvYOChenDkxGCyIYbWaVM8cEb6D2v4waEUP2fe9T8vbD2L/7GlQTCZMvo8/lDyBUM5Vf/5vqde+AECScNo+kmTcihEB3O43g82BxloqJrJ+8QlR2+ysL1e34gtIlv/fTdGs2fQBA3+ufbHrNU3cE56G9AWXfpMdJ9dq3jqvAFEIYjqoglYzC1VytxwhMMIKxb++idjSybwIDUmLIK6trKuSgCIiwqJw5pi9/WraNdTllTVXKiyvtrPqulL/fNK1dudZSSvaX1lLv9DAiMyHsjpaDlQ2U1ToCEjacHo1l3+YHmBuaYzOrTG2lIlB3EGExcd+lY1nw3jZ0aTiizCaVmSPTO1Rp/n8JS1Jfsu54OeixlLNuI+Ws2wJel5onpMau2qKIHja9Q2so//TpoI6jmo3vkz7vkWNFmnWNoA4GCBkk350kTL2C6nXv+hcPEQrRI2eEZf4eJTDDgRCCBddP4eUv9vLlzkN4dZ1J2an8eO5IymoaWbP3sJ/tz+3VKT5iZ21OGWeMaD3G7lBVAw+88S3VdheKYjhabj1rREBXxa7g8mgBzcSOEqogMIBJFYzom9CuVr7hZvrwdF79aQJf7y7B4daYlJ3C0IyTv39LT0KNiMGWMQznwd3+BxQTMafM7fB8oZqhoShoDdVNAtMUl4Y5KRN3WZ7fMGGyEj/xkg5ft6ukX/4grsO5NORuPJZUIHXqd62kdNkC0i+9r0vzn9TFN7qLCIuJn54/mvfuO4cPfn0ev503gdS4CHYfrA76rHR4NLbnt95RQ0rJb17fwOHqRpwejUaXF5dX58Uv97K7uPUUxY4wMC0Gkxr4sVlMCrNGZwSNKRUCbj1rBI9dO+mExZwmxdi4fOog5s8Y8r0Wlo35Wyl551FK3nmUxvwtx/XafW940ihC4rMxCksEpphk+lxyb7vOd5XlUf/dKjy1ZUQOGh80X12oZr9e5EII+t/0lO+6xrZXsUZhSc0i5dw7w3BXHUOxRpJx1aMB5eCkx0nlyldwlu7v0vz/cxpmayREWVGCCBSzqpAU27oNJKekhppGd0CsnNuj8eGmwqBdJDuDqij838VjeXzJFry+fkc2s0qf+EhunDWM+Cgrr6/ab8StCiN3+0dzhnNJD/H+92RKlz1B5VcLmzzZVWveJGnG9WFJ3fPWV+Ctq8CSmhXSHhfRdyTDHv2aqjWLcZUdICLrVBKmXO7X4ygYmqOewn/dajSKM5mRHhcxY84y2lK4j8WECksEfS77dUBFoYj+Yxj2+zXUbFiKu7KYqMETiR0794S16ajb+UXQXHupa9Tv/BJbetCGte2iV2A2Y/KQVCwm1a8jIxi1L+eObT1+sd7hCRo+I4HqDsSGtoepQ9N4/rYZfLylkCN1TiZmpzBrVAYWk8o107M5Y3g6a3IOIwScMTydPgmRbU/aS6ep3bacw8seN6oBNeNoubWEqVdiy2i9sEgoNGcDxQvvxv7d1wiTGaQk9aJfkXLmzUHHm2KSSe2gZnfo9ftpzNuM9LqbHCb1u1eSNPtm3OX5NOZtxpyQQep5PwtIoWy6blQ8ySHWdDxxFO2ksWA7QYujKGqT9t1ZegVmM8yqwp9vmMqjb2+mot6JIgxHyf2Xn9pm47MRfROCxopazQrTh4ffbpiZFMWtIRxgmUlRXDWt60G6Jxu1jW62F1RiM6ucOijZr1vliaJ6w3scevPXQYPNwXDG1G7/HHvOWiq+fAmtoZrI7EmkX/ZAu/KxD/77HuzfrTKEmS9Nsez9P2FN7k/sKWf5jXVXFFGxciGuw/uJHDSBpBnXY4oJ3W4EQHc7qNvxWUAKpHQ7qNv8AcN+902bazwZkLpG4b9uo/67rw3bZdBq/JK4LtZK7RWYLRiQEsPLP5nJwcoGPJpOVmpMyD7bzYm2mblp9jAWfrWvKXDealZIj4/i7LFda8LWWaSUrNh5iGUbC2h0eZk2rA9XTRtMTMTx2SodqXOwo6CSKJuZCYNTOizgpJTklNRQVe8i93Ati9fmNc2hKILHrp10QtsuSyk5vOzxkMISDK3GvncVjsIdTePsu78m98BGhjzwKZbk0OX1vPZq6nevDCrMjnz2nJ/AbMjdRMHT1zfVhmw4sIHKrxaSff+HWFpp+au7HSGbC2qOnhGXDFC69HHqd34R5IhAWCNA18ic/+cu9/r5XgjM8loHq74rRdN1Thua1uVMFiEE/ZJbt/sE4/KpgxiSHsd/NxZS1+jm9BF9mDuuH7YTFMP5z8++49OtxU0hUu9tyOPr70r41+0ziLB070e/cOVe3l2Xj0kVCAQmVfDEdVMY3Ceu7ZMxOoDet2g9R2qNLeLRe2geCfDAGxt56xdnHTdN02uvxr7na8PzPGoWQlGbWjuEQiJw5G9tIfQkusdJ+fJng/b/8VSXUrrsCeq2LQ8Qlk1javwr5R9adC+6+1h1IelxoXk9HF72BP1vCd2wVY1KwBTfB09li+6XHew3fiLR3YZDJyiKQtp5d5Ew7ZqwVIzv8QLz061FPPvpbnRpPPEXrdrPvNMGccOsztmMusqYAUmMGdD6Nuh4UFHn5CNf8P1RPJqkpsHF8m3FXNqNTqDNuUdYuqEAj6bTvHXPg29uZNHdc9rlqf/9u5s5VBnY1K45uq6zLb8iZOO0cFK19m1KFj/Y5H2VUqffzU+jWKPQg1U59wVRJ599B5UrXg4UfLpGY/62gNO89ir2P36urwVFCBSVqGGnNf1Xc9ThqghMxkDq1DerpB4MIQR9r3uCgudvMdaoawiTBcUS2W7vutdeTd22T9Ac9cSMmtVpe21naauPUczoM8PWXuPEG4G6QJXdybOf7sbt1fFqRs9wt1fn3XV55B4O3q7zf4WckpqgmpfLo7M5t6Jbr/3RlqKAJmdg9PPZe6i6zfMr6pwcOFzXqrA8NmfHKlR1Bld5ASWLH0J6XEbTMVcD0u2g+OWfkjTrh4gW5fMaVAIAACAASURBVNGEaiZh+nyGP2a0qwiqJQqBrU9g//fKVa+hNYZuM4GiolijSDv/7mNTmSyIEMHjqrXtClzRw6aTfd8HJE67msjsySTPuZUhD32OJal1U5LUdco/eZo990+kZPFvOfz+nzmw4GIOvfVgh9NepZS4qw61qbEHPVdzI9Tgup9QTVj7dN4r3pIerWGu31cetBiAR9P5+rvSdm//vo8kRluRQYxTqoC0+O7tauhwB8/wEEI0ba1bo9HtRRWCtpI4vbpkXCs91FsipaR+55dUrnoN3WUnbsLFJE6/us20uZpN/w3eEkJRMCdmknruT41OjB4Xii2atEvuJen0a5vGxZxyFvU7v/ArnCHMNlLOuSNgzoZ960M4LAwSplxJ6vl3+dklFbONmLFzqd/+mV/7WmG2kTTzhlbv7Si29CFkXvvHdo0F0D1Ocv96Nc5CQ0s++k2Tmoea9UuIPeVsYkbObNdcDbkbKV54D966cpCSiAFj6X/LM37xnq0RPWx6U/fJlqRecE/YehBBmDRMIcQrQohyIcSuEMeFEOIpIcQBIcQOIUT7Oh91ZU3dfYGTnOGZ8STH2AIcViaTwsUTB3TrtWeNyghqt9V02a541MzEqFZz942+TAo3nzmc2Mj2V8M5vOyPFL3yM+zffUVj7iYOv/dH8v4yDz2EnfAoutsBWqCg191OvNUlpJ77U0Y+uYMRCzYzYsEWP2EJ0O/GvxI/5UojpEUxYUnuz4DbXiCi3+iAOa1prZhKFBOZ1z4e1InTd/4TRGSNRVgiUGwxCJOV2HHnkDzn1lbvrbNUrHglMKvIh+5upHr9O+2ax11VQsHTN+CpLG6qLNSYv4W8v16F1FvvenoUNTKOjGseMwLnhfG9EaqZmDFnkXLW7e27oXYSLg1zIfAMEKrH53nAEN+fKcDzvr+7xGlD03h+eeCHZlYVZowMLMDxv4QQgieum8rv391MXlmd0cvHpPKLi07p9vJuZ47J5LPtBzlQWovTo6EKo/jJ3ReMbpcDTFUEv7z4FB5bsgWvV0eTRpk+m8XE6H6JxEVZOO/Ufh3KGHJXlVC5cqFfjrH0OHCWHaBu68fET7o05Lmxp5xtBKS39IZLnYovXyJh2tVYEjNRI4PvaBSzjb7XPk7m1Y+iu51GVkwIjShp9s1UrVkMARqtIO7U80JuPdWIWAb/cgnOkhzcFcXYMoe1uaXuCjUbloQsIwfgrjzUrnmq1rwZqL3rGt76Chr2ryd62LSQ5zpL9lG97h10l53YseeQff9H1GxchtZoVMFvyNvCgScuJPGM+SROv8YokddFwiIwpZSrhBBZrQy5BPiPNAwb/8/eeUfHUV7v/zMzW9V7lyzZlrtxxR1jbDAt9GJK6CWQECCkAAG+tCQECCT00DummGbA4IYx7h13W7aKVa3et8+8vz9WXrTaWTVLshx+zzmcgzVd2r1z33uf+zzrJUmKkiQpWQgRZGC1c4gOM3PbmaN4/ttdiJamjyxLXDJtUIBnuMOtsmBdLku3lyBJcNoJaVw8dWC/USHqDcRFWHjm+ulU1NuxuzykxYb1yWikUZF54qrJrN1XzrqcciJDTZwxNr1LhmuTsxN5/oYZLNx8iPI6O+MHxnH62PRui5nYcjciKQZ/UQZAOG007FzebsAMyRpP5NgzqNPxHNc8LiqXvkzqvEc6vAdJMaJ0QOmyJA1mwC2vUvjKLT/fqyRjThlG6uV/7/AalpShfdN0CWLzewT2wp2ojqYOp4xcVYeCMAEE7rrg/lzVqz+g7JOHfSZwdZu+IGzYSaRf/xy5T17gZ+BW9umjNO1bzYCbXurwsTpCX9UwU4GiVv8ubvnZUQVMgNPHpjMuK47Ve8vwaIKpQxIDKEGaENz97nryyht8whofrjnIptwKnr52WsCy1eFWKa5qIjrM3CFhvT/D5VFZubuMLXmVxEdYOGl4MiaDQlJ0SK9TnRRZ5qQRyZw0ovumYBnx4dx2ZuCytS3sxXupWv4qzop8QrMnE3fK9Rjb2MYqodH6dS5ZwRiRQHPuJuo2fQlCEDHmdIwxKRijklEsoUiSRMzMq6n/6bvALFN105yzrtvPqIeIUbMZ+cx+mvatxlVVhDVtGNbMcf3KziN62jzKFz6pK6UG3kZU095VRI47s93zhGZPoWH7kkBlJE3DOuAE3WM8zXWUffKQX01Yc9po2ruK0o8ewFmRF+B22bjre+zFe7GmDe/sI+qirwKm3l9at40mSdLNwM0AGRmd88xOiLRy4ZSBQbdvzauioLIxQIWooKKRn/KrGT/wZ7+PBetyeWflAW/TQdMYOyCWey8aR6j52FpX1DU7eWdlDmv3l2M2yPxqwgAunJIVVHLO7vLwhzfXUtYiBiIBH6/Nw2yQQZK4YsZgLpsR2KU93tC4+wcOvfIbb5YiNBxFu6hdPZ/B937jV+sLGzod2WRFczTT+qMnKUY89gbyn7vK+yUTgppV74FsQFIUYmZcSfJF92OKSYEgNTXn4YMUvf0HUq94rMd0FyVJ8vnv9EfEnnw1TbtX0JSzzs9q+AgkSerUEjhy/NmUf/FP1FYBUzJaiDjhVF0WAUDz/jVIijFAfV5z2ahd+1HQa9nythx1wOwrWlEx0LqgkgaU6u0ohHhFCDFRCDExPr5n9BL3ldT5vH9aw+FS/Wgu6/aX887KA15DNZcHt0fjp4Iqnvg8kC/Xl7C7PNz22moWbyuitsnJ4To7767M4e+fbgt6zJebCiipafZ1pY+ECKdHw+lW+WD1Qb7f2bk6U3+FEILi9+9pCXTeYCY8LlR7I+Vf/ctvX0kxkHXHBxhjU5HNIciWcC8955w/Ub+5xRKiNRVG8yDcTmrWzKfi2+cwRiURNvwkJIPOLLLQqN+6iJL5feePfawhG0xk/v49ki+8H/TqqkLrkPguhKDo9dtQXa2zVAnZGu4nUtwWUjdeSpJiCFh1dAd9FTAXAle3dMunAPVHW7/sCuIjLLpLUEmCT9bm8vvXVrM+p5yP1+YG8AfdqmBLXhV1zc6A4/sKy3cU02B3+ympOz0amw5WUFipP762cndpgKdPazjdKh+tye3xe+1LeBoqUJt0pPOEStPewBloS/IQhj6ymoF/+JjM377B8Ce2gabqUoZ8p3LZqfr+NQAyrn+OiLFnoLdgEm4H9Zu/Qm1lg3s8Q2gaDbu+p2T+fRz+8gmcbfQuwZtFxp5ynVf13WgBxYhksiKZrKTf8IKufW9r2A9tpzl3M/jVlQXCaaNx1/dBjwsbNp0u8WAkyUu9GnVK548Jgh5ZkkuSNB+YBcRJklQMPAgYAYQQ/wUWAWcBBwEbcF1PXLezmDkimVeW7kVy+6sQaQJsLpWcsnr+8dk2zAb9P4JBkWiwu4kKPTqlk+5ix6EaXSK4IkvklNXrdr0txo7/tLXH8CXQE5DNoQQbhA7WsZYkCWvGaMDrAd64Z6W+rUMraPZGhKYim0PIuP5Z9uSsQ22o0Lkhf3Hd4xVCUyl46XpsBzeiOW2gGKj6/nVSr/gn0ZMv8NtXkiRSLv4/YqZfTtOelcjmECLGntmpyRpb/jbd5bzmbKb54CYix5+te5xstJB56+sUvHgdtNhzBLIKWqAYMCcMZMDNL/eI3FxPdckv72C7APpeTbQFVpOBp66ZymOfbaOkphmPpgVYPDjdKqomIQNt8zJZkkjphESay6Pyw+5S1uw9TESIibMnDGBY6tGL5abFhmJUZF1F9cQo/fs6Z+IA8sobghLFJWBk+rETrmgNt+rNlmubnIxIiyYrMaLjgwDFEkb4qDk07lru12mVTFZi59zY7rGay8HBx8/FVdNxWcKUOMivHhc6aCINP30XQDD3iut2v8nVX1C/7VuaD2xEHJlNVz0I1UPJB/cQMWYuiiVwesiSnN1lnUljdIq3FtmGuSAZLRg7oESFZk9m+ONbaNz1PU0HNlK77qPAhpzRzIAbXiDihNO6dF/t4bie9OkKMhPCefmWmVQ3Orj15R+p1zEDkyUJs1nB4VZ93jhmo8Jv5o7QVTlvDZdH5U9vr+NQZZO3ySLByj2lXouKiZlHde9njR/AZxvy/eayFVkiLtzCqCBB75RRKewsrGbJ9mKEELSOtbIkYTbKXHfKsZm3b43i6ib++PY6nG4VTQg0DYamRHL/JROI7kRGn3bVkxx6+WZs+VtbxG9dxEy/nJjp7b7Dqdv0BZ76ina5hOD98qZc+rDfzxLP+SNNe1a2KP38LK6bfOF9QXmSxxPqNn35c7BsBUkx0HxgfVBNzK4iYvRsZHOIVzSk1ctHUgxET76ww+Nlk5XI8WcTMeZ07PlbcZTl/OyoabISPuykHg2W8AsKmEcQG24hMSqEerv+rPkzN0zny40FbC+oJj7CwqXTBzE2M05339b4fmcJBZVNvqWzEN657VeX7mX2qFRCLd1fDsRFWHjsysk8+eV2KurtCCEYPSCGv5w/NijVpKi6mW351SiyhBASigwp0SE4PRoj0qK58qRsUmM7njPubTz88Rbqm/2V6ncV1XLlv5dxzSlDmTe9/U6+Yg1n4J3zcVYU0HxgPZJiJDR7UocUnKacdX7qPkcgKUZMiQPRHE2Yk7JJOOM2TAmZaB6Xz2PbkjSYwfd8RfnXT7eI66aScOZthI88+hpZf0B7tce2FrtHA0kxMuhPn1L4+u+8zpNIGGNSyLj+uS6JZUiKgYF3fUzVijeo2/g5kmwkesblARNXPYFfXMAEuOKkbB77fJtfXdBkkJk6NJGU6NBOcf/aYtXew/p1RkVmd1Etk7KPrkM3PC2a1397MnXNLowGmbB2ArCqafzlnfXUNTv9AlFZrY2Xbp7pFyidbpWNBypocroZmxlHch+qs5fWNFNeZ9OtQqoC3l91kOzkKD/alx5UewPF7/4Re9FuJNmA8LiIGHsG6dc8HTTjM8VlgGIMyDAlg4mUSx4kbOh0Kr9/g4KXrkd4nEiyQtycm0g4604kWcacOIiMG17o7qP3a8RMn+cVFW6zxJVkg88Dvadgistg8N1f4W6oBNWDISqpW3xT2WQl4fTfkXB671b+jmu1ou5i6tBEbpk7nDCLAZNBRpLA7dFYv7+cy55eyqo9uoyndhFuNeqTTYXoMatdSZKIDjO3GyzByzt1uj0BgcijCRZt+1n3cH9pHZf/exlPfbWd/y7ew83/XcnLS/d0WWmmu/CoWrtfDqdb5ctN+R2ep+T9e7EXbEe47GiORoTHScOOJVQueyXoMTHTLw9sAsgKSlgModlTqVm/gPKFT6DZG1pUimxULnuFysX9I0g6y3Np2LFUt3t9tAgbOp24U65HMpiRTCHIllBkSxgDfvtGr/n0GCPiMUYn9ytyvh5+kRkmeOuCp49N584315JbVo9KC0fRo/Hkl9tJiAphaBdmlc+eMIC1+8sDssxQs5ERfdxcqdcxYwOv+EVNo8P3/w/M30Sz07+7uGhLIeOz4vpEYzItLoxQs6FdBaNGnVqz5nbQuOt7PE21hGSNpWH7Yj+VHvDSgWpWvkPC6b/VPa8pJoWs296m6K07vZJiQsOaPoqMG19EkmUqFz0TOH3islO57GXiz7itT77Y7rrDCI8LY2y673qay8GhV39Dc463/CBUN2FDppJx8397jDQPkHTeX4iZcQVN+1ajWMMJHzUH2dSzU2+qo5m6jZ9jy9+KOWkQMdPmYQjvuPx1LPGLDZgAh+vsFFQ0oraJLi6Pxqfr8vjrRZ0XVRqdEcPVJ2fz9g85GBTZm1maDPz9ikmdsrjoLNyqRk2jg6hQc9A5+FEZMb6mVWtYjIovEO4prtXlaTrcKt9uK+qTgClLEvdeOI7752/SDZpmg8yM4f4SX/biPeT/53KE6kZoKiAQQWhBqrM54Geu6mLU5lrMyUMIHTyJoY+uwV1T7O3MRvw8KOGuLw84FkBzNCPcTqRuBA/NaaN20xfY8jZjThzcEiAC5elcVYUcevVWnGUHQJIwRMSTcf2zhGSN5/AXj9G8fx3C4/Q1OJpy1nL4yydIufj/unxP7cEUm0bM9Mt69JxH4K6vIPfxc/HY6hAuO5LRQuXiFxl41ydY0/S9qvoD/qcCpqoJ9pXU4vZoDE+L7lBYo6bRgVGRAwKHwBtMu4qLpw5i7ph0dhXVEGYxMjI9psfELoQQLFiXx/urDiKEQAjBryYO4IY5wwOukRQVwlnjM/zsKcwGmfS4UN9st8ujBpMQ1K3F9hZGD4jljd/N4vlvd7E+p9wnGmw2yt7nGPfzeKwQgkMv3YBqa6tGrvMgkuynx+hprOLQyzdjL9rlXVYKQfIlDxIzbZ6uqo8lZSj2QzsCfm6MSuyW86C7oZLcx8/B01yPcNlaAsQLAQFCqG5yn7oYT0Olr3Psri4i/9lfM+ThldSu+zhQQMTtpHbNhz0eMHsTh798AndDhY+HKdwOhBuK3/kT2X9ddIzvLjiOi4AphGB3US155Q0kR4cwfmB8QJA4UFbPA/M3tlB6JFRNMD4rFlmWGZsZy2lj0gJ8bDITInS5jUZFZmxW92wmIkJMTBvaOeHTrmDJ9mLe/fGAXzD7esshzAaFa3ToQbfMHcHojBi+2nwIh1tl1sgUzhqf4VNhH5ke4/UubwOLUWH2qN6XxlM1QVFVE1aTQmJUCA9eOpF9JXV8tbmA2iYnU4cmctoYfz8kR/FunWAJILzqObICqhvJaPZaLJx/t2+PgpduwF60y8spbJlBLv34QcwJAwkdfGLAGZMuuI+CF67xE3GQjFaSLnygW8vx8i+f9DU2oHWA+CPZf/3Wt1/j7pVozuYAjqfQPNSuX+AlaetACyKC0V/RuGOJLmndUbof1d6IYu1dCcLuot8HTIfLwz3vbSC/ohFNCBRZIirUzNPXTiUmzLsscnlU7nlvPU0O/6XZuhzvNMbm3Eo+WZfH8zfOILKV4Gy41cjFUwby2YZ8XyamyBIhZgMXTu49z5vu4INVBwMyP6db4/ON+fz65CEBLxBJkpgxPJkZw/WJ1Bajwh/PPYEnv9yOqglUTWAxKgxPi2ZWLwfMBevyePP7fahCIOHlyD4870SGpUYxLHVs0OOExxVUVsySNoLQ7Ck4y3IIGTTRazEb5hUrdpbneWkrbZbuwuWgavmrugEzbMgUsm5/zzsWWJqDKT6DxF/d1W3qUMOOJboTRY6yHFR7A4rVS9Z31x3WLTEItxN3dTEhgyZiO7ixzVaJ0MGTunVffQnNZadx9w/eDFkO0jyS6LXGUk+g3wfMd1bmcPBwg18m6HLbePqrHfztcu+HZMOBCt2a3RE43So1jQ4+WHWAW08f6bft6llDGBAfzoL1eTTYXEwcHM+VJ2UfszHIYAg2xuh0a7g9Kko3XCBnjkhhcFIkS7YX02B3MWlwApOyE3q05toWH689yOvL9/v+LYC88kZufeVHbj51ODOGJwflrFozRnuzyDaQTFZipl9G7MyrdI/zNFa1qNu0zcLa11wMHXQig+7qnHJ4R5AMwYNA6wARkjVOV4ZONocSmj2Z2JOvJvdfF6K5XaC6vH4+BhMpl3asx3ks0bR/DYf+e5P32YTXNRNZ8c8yFQPhw2f2eHOpJ9HvA+bS7cUBy2ZVeKkzLo+KyaDQ5HAHjDq2hUcTrN13OCBgSpLErFEpvZ5VHS0GJUawpzjQQCw2PHjzpzNIiQnl2j6a+HGrGm//kKO7rcnh4fnvdvPSkj387fJJjMoItLKQFCMZ1z3DoVdu9QpmqG5kcwiWtBFET7006HUtqcMDuugAksFM2MhZ3X6eriB62jyqlr3qH7RlA2HDZvgRxa3pIwkfdhKNe3/8eWrFYMYYk0rE2DOQDSaG/N9yqn94G3vRTqzpo4mddU2n/W+OBTSnjUP/vclbamgNSQaDGUlWkCQJY3QKaVc9eWxuspPo9wFTbScSHkkqx2bGoXWCO2g2Hb/q6jedNpx73l2Ps1WDymyQuWXuCN2amqppHKpsIsRkIKmHyOiH62x8uDqXXYXVJEWFMG/6oC5ZChdVNQWTlATwNd8e+ngzH911qk/r0+VRWbPvMCU1NrIShjP+gWU0bPwUT0Ml4cNnEj56Trvai4o1nISz/0DFov/4qEKSYkQJjSJuVt/owCSc8XvsBdtpzt0ESN4AEZWkK2OWcdNLVK98h5rVHyA8TkyJg3BVFZPz4MmEj55Nwpl3+NVn+zsad68IskUicvyZhA2djik2g9Dsyf+fh3m0mDY0ie93lvgtuSVgcFI4X27MZ1t+NYlRVk4ansyafYeDcvrMRoVzj3Km+1hiRFo0T14zlbd/2E/e4QZSYkK56uQhjMsK5K2t21/OU19t91kPZ8SF8eClE0mI7L5bZGlNM7e9thqHy4MqvKOXOwpruPNXo5k9KrVT5wi3Gr2rzY5WA6pgT3EdozNiqKi3c+eba7A5PdhdKlaTQkyYhf9cd2uXDNAS5t6CJWUIVctexdNUTfio2cTOvIq6jZ/RsH0JhvA4Ymdd02u1QNloJuv372Iv2oW9aDemuAxCs6foBghJMRA3+3riZl9PyYcPULv+E1+gr1nzIQ0/LWHI/y0LqsjU36C57Oj+0YWKYgohpp3VQX9Dvw+Y150ylO0F1TTYXDjcKmajgkGWKK938N6PB3B5NGRJwmiQuWhKFvnljTQ63JRWN2FzeXwd8xnDkjh7Qu+6JfY2hqZE8Y8r2h9NO1TZyGOfbfXLRPPKG7n73fW88btZXX6Dq5pGdaOTN1fsw+7y+HmFO90qL323m5NHpHSKPhUfYWVoSpRuaaE1JAlfB/+Zb3ZS2+T0XdfuUimvs/Hasr3cde6YLj1LxKjZRIya7X0uRzO5j5+Dq6akZekr0bBrOUnn30PcrGu7dN6uwJo+StctUg/uunJq137o73mjelDt9VSveq/XxwB7CmHDT0LouG7KphBCh07za3r1d/TrgHmgrJ6rnv2ecZlxTJgUT1ldM2mxoRyqbGLxtiKfoK4mBE63yqIthXzwh1NbBCcEe4prqax3MCQlkpSYYy800Rf4avOhgJqvJgS1zU72FNd2yub2CL7bVsiry/bhVrWg3EynR6OywU5SEJm5tnjw0gncP38TeeUNQRt1EjAiPRpV09iaV0Xb3Tya4Me9ZV0OmK1Rs+bDVsESQCBcdg5//hjRUy7uF5qW9qJdSAZzgEmYcDtp2r/2uAmYxshEEs/5I+VfP+2zEpGMFoQkUfTWH5CA0CFTSb/2392e9PE01dK090ckg4nwEScjm3tHE6FfB0zw8vW25ldyqKqRN287BaMic81z3/upjx+B3a1SVttMWqzXxnRkeoy/McZxBreqYXd5CLcYO50ZVtbbAwIMgIREbVPnBYM3HqjgxcV7OiSxa5ogvAtKTFGhZp6/cQbF1U1UNThYsD6PnYdqcLhVjIqMLMG9F47DqMiomuaNnkGe52jQsH2xroGXpBixF/xE2LAZR3X+noAxOllfDV5WMMcfX6ul+NN+Q9jQadSs+wS1sZqGHUsRLU0ggXdaKe8/l5N9/5Iur4K8DpIPIckGXxc+4+aXe8UTqd8HTPA2d5ocbtbtL2fmiOQW8YnASRynW2VLbiVpsccuOyioaOSTdbkUVDQyJCWKS6YO7HJ261Y1Xlm6h++2FaEJQVSImd+eMYLpwzoWp50wKJ5tBdU6Vhsaw1I7P9M+f3Ug77MtTIpX4ak70nVpsWGkxYYxJjOWnYU1bMmtIiLEyCmjUnz8WkWWmZAVx5a8Sr+XgEGWuu1GqXlcNO5YGoQAj7euFnr0os89AWvaCMyJg3CU+nNIJYOJ2D5qVunBVV1E84GNKKFRhI+Y2WnepDVjNKkZozm88MlAgRfVg7umBFveZkIHBfJig8FRdsBrt+t2Ivg5ISh8+WaGPbaxxwnwx41akdOtUlzt9Uu5YHJWUCrNa8v3+fbra+wsrOH2N9bw/c5SDh5uYPG2Qn776iryyhu6dJ5nv9nJ4m1FuDwaHlVQ1ejg8S+2s7NQx7+mDeaOSSM2zOyb6AEvSf1XEzKIi+g8v62iPvhoqMWoYFRkJg6O565z9K1QOwtJkjhhQCzXzR7KRVMG+oLlEdzxq9HEhFmwmhQkwGry2gTfeOqwLl/LVVPC/gdOovjdP+MsO6hzMzKGyEQsaSMDtx0jZP3+XcKGTPXyLY0WDJGJDLj55S6rm/cEhBCUfvIwOQ/PofSj+yl683b23jsZR+n+jg9uBWd5Pqh6XuTg7oQCfmvUbfxcX0tAkmjYuaxL5+oMjosME7xd7qwEb2F4zuhUfthdyqaDlQH7qZpgyfZirp/d9S9UMDjcKk12N9FhZhRZot7mYlteFWajwoRBcZgM3uD93KKdflmZKrxNipeX7OHxq6Z06lpNDjcrdpUG1CGdbpX5qw4w+sr2mz4Wk4Hnb5zBZxvyWbW3jFCzkfNOzOTkkV3LyEakRbNqX1kAv9VqUvjnryeTGBlCdFjvk/vjI6y89ftTWLvvMCU1zWQlRDApOz6ovXB7KH7nLjyNlYEjeYoR2WDEEJlI1m3v9CtqiyEshqzfv4enqRbN2YQxOhWpG8/eE2jYvoSa1fO9wh++GNVEwQvXMvRvazv9ewsdfCKNu7/X8SJXfX5LnYXmsumOWAqhIYKMkR4NjouAaZAlYsMtTMr2qslIksSMYUlsL6gOEM5QNUGTo33bgc7CrWq8tHg3S7cXA96gPXFQHGv2laPIEhISkgSPXHYiQ1OjKKzSz2w76gq3RnWjA4MiobcaLqkJVAjXQ6jFyFUnD+Gqk4d0+rptcdXJ2WzKrcDh+tk4zmxUuGH2sA6X9vU2F0u2F1FU1czI9GhOHpmi69rZWRgVmZNHHt1ggepo9joU6ny5ZJOVgXd8gCV9VL8Klq1hCIuGLqiQ9wYqF7+AcAeuPDzNdTiKdnU62EVPvYTKpf/Fo7p9pQbJaCF81GzMiYO6dE8RJ8ylZs1HgZYamkZYK/GVnkK/DpiSv6jbQAAAIABJREFUJGE1KZw0PJmbThvul1WMy4rTpfNZjApThyT2yPVf+HYX3+8s8QVll0djxa5Ad+AH5m/igz/MwaTIfnSeIwg1d77GlxgVotu0kSWv101fISM+nGevn85bP+xnb3EdceEWLj9pcIfCIrmHG/jTO+vwqBouj8bK3aW8/+MBnr1h+jEeNxVB+Z+SrHQ5szkaaE4bFYtfoG7DpwgB0ZMuIP6M23TNxfoLhBA4Svbqb1TdQUVB9KBYwhh8z9dUfP00DduXeE3rZl5F3OwbunxfoUOmEnHCad4mkssGkoxkNJNw+u8wxfT89F6/DpiDkyL44u4zdLclRoVw8ZQsPttQgKvFPtdiVDhhQCwTBsX77SuEQBN0SWrN7vKwvFWwbB+CbXlVnDkug0XbCv2OMRtkzp+c2enrWowK86YP4qM1uW0sNBR+PbNv61YZ8eH83yUTu3TMvxZux9ZKlNjhVnE32Hn7hxzuOLv7QcmjaqzbX86OwmriI6ycdkJal0oCiiWMkAEnYCvYRus6g6QYiZx4brfvq6sQmkbefy7DUbLPJ9NWufS/1G74lMF//Rajjj5mf4C7uggRZExLaB6sA7pWyzZGxJN6xWOkXvHYUd2XJEmkX/cMTXt/pG7L18hGM9FTLiYkM7iIy9GgXwfMjnDtKcMYlxXPd9uKcHpUThmZwrRhST7xCJvTw0uLd7NiVykezWv+dftZo8lM6LhzVt/s6rQIhcAbGG44dRi1zU7W7i/HZPDqbM45IY1LpnZtmXHFjMHEh1v4cE0udc1OhqdFccOc4br+4/0JTQ43hyoCG1yqgB/3lHU7YDpcHu56ax0lNc043Comg8wHqw7w9ysmdYlXmnbN0+Q+eQGa24lw2ZDNoRijkkg654/duq/uoGnfapyHD/hrWgoNT305OQ/OJPv+JZhiOjc51ZeQjBZdURAAQ2Rij5qjBYNqq6f044eo3/YNQlUJH3EyKZc9iikmlfARJ/vpn/YWjuuACTAmM5Yxmfpv5fs+2MiBsnpfA2VPUS13vbWW1387q8PsJC7C0umM1KMKxmV5mz9/vWg81Y0ODtfZSI0J7dYyVJIk5o5NZ+7Y44tEqsiSbjkBvBl7d/Hp+nyKqpv8SiMAj322jXdvn93puqM5IYuhj66hfuvXuKoKsaaPJOKE03y0GCEEtoMbcRw+iCVpMCGDA90nnRX5OMvzsCRne43Uugh74Q40lz4fVnM0c/jzx8i44fkun7czcDdUUvrxgzRuXwJA+Ji5pM57pFNkcWNkAtaMUdgLtvtrdRrNxPcigd5evIfyr/+NvXAnqq3OS3xvqUM37vmB3MfPZcjDP/ZZOeO4D5iqprFoayHfbi1C1QRzTkjhvBOzKKxqIrfcXxZO4G3kLNpayJUdLG8Nite3+9Xl+3xLYwlvMDMoUstIprchcfWsIX4BODbcQmx4/5Wo6i1YTQYsJgW7S5+/WVrT3K2JqxW79EsjDXY3pTW2LtkFK5ZQYqbNC/i5am8g7z+X46rIQ2gakixjShjIwDvno1gj0Fx2Dr3yG5oPbGjx0nERPmo26dc967Pf7QyMMalIRrOu7zcIGnf/0OlzdQWax0Xukxfgri2DFjJ8w/bF2A/tYOhDKzrFpcy44UXy/zPP64GEQGgaEWPmEnvSlb1yz7aC7eT9Z553wEBPXEdTUZ026jcvJGZG+z70PYUeCZiSJJ0BPAMowGtCiH+22X4t8CRwhGT1vBDitZ649iOfbGFbfhVOt/cL9e7KA6zZV855J2ailyC6PBq5neRFnnNiJtFhZuavPkhVg4OhKZFMHBzPltwqKhrsZCaEc/6krC6ZpR1v0IQIKE0IIfhuWxFfbMynyelhcnYCv56ZTUyY90VRXB3opWNQZN3prM7AoOjTaIQQGJSe6WqXLngEZ+l+nwycAJyl+yld8AjpV/2L0gWP0pyz3s9Lp3HXCiq+eYak8/7c6etEjj2TsgWPoOoGzPY9wbsCIQT1W76iesWbqPZGzEmDvIGu9eSQ6kFtqqFhx1Iix53V4TlNMSkMeWglzQfW4a4rJyRzTJe72npo2Lmc6h/exNNUS+TY04mddS2KNYLDn/8jgHrUFsJlwx6sGdULOOqAKUmSArwAnAYUA5skSVoohNjTZtePhBC3He31WiOntI5t+dW+YAnegHiospFGu0vXgsFkkBmSEkm9zcWry/ayem8ZkiQxc0QyN84ZTrjV/017RLXco2rcP38Try/fj8OtYlBkSmpsPqWegopGPl6bS0FlI0NTorh02qA+9fjuaSzaWsi7K3OoaXISF27hutlDOfWENABe+G43S7YX+zLv77YVsXZfOa/cOpPTxqTxfosoSmuEW4ykdyETbI2zxmfw2rK9fgwECa+WZ2InZ9g7Qv3mhYHOk6qb+s1fkfbrJ6nbsEDHS8dBzer3uhQwZZOFQX/6jINPnIdmq/fbJhktxPRQtlb26aPUrp7v5SkCzoo8XUqV5rLjPKxD4m+Bu76C8q/+RcOOZcgmCzEn/Zr4U2/qMVX08kXPULnkJV9gdB4+QO36BQy+dxH2wp0dHi+ZQrCmDu+Re+kMeoIBOwk4KITIE0K4gA+B83rgvB1iV1GtblC0u1QqGhyMSI/GZPj5ESUJzAaF005I44431rBiZwl2l4rN6WHZ9mLuemttUEGI5TtL2Ftc65OP87QIUjz22Ta25Vdy+xtrWLGrhNzDDSz+qYhbX/mRgorG3nnwHoRH1dhdVMPe4lrfsy/aeoj/LtlDTcvseVWjg2cX7eKHXSVUNzq8TbbWBH1N0Ox0882WQ5w/KYsB8eFYWrRHTQYZi1Hh3gvHdZvjePaEDCYMisdsVDAZZKwmhchQEw9c3HlXz44QzHlSqG7Q1AABjCPQnJ3jxraGOSGLYY+s8k4UGa3IljAvD3HETBLOOPp6oLuunJof3/MFS++N6pdJZJMVc9Jg3W2qo4mD/zyH2g2fojZV464poWLRMxS+1jM1S09TLZXfveCXRQq3s0Wl6SNdR03/m1dQzCFETDgH26EdNO1f2yIl13voiSV5KlDU6t/FgN44ykWSJM0EcoA/CCGKdPbpEmLCzLokbwnYW1zLHWeP5rttRSz+yTtmOH5gHLfMHcGeolrqmp1+S0SPJqhssLM5t4LJ2YE8zqXbi3W1NoWApxfuCAggdpfKy0v38FgHkznHEltyK/nHZ1vRNBAIzEaFhy6dyDs/HNDxD1J564ccbj19BEaDHDCJ5PJobC+o4fIZ2Txz/TTW7S9nZ2ENcREWTh3dNQpQWyiyzIOXTuRgWT17S2qJDbNwYnaC3/jn0SJ8+Ewa96z0b2i0OE9KigFrxmgdF0mJ0OzOTXC1hRISSfZfvVmUq6oQS+qwHlneAtgKfkIyGAMy4sCbMKCExRBxwmm6m2vXfYJqr/ebYxduB417VuIoO3DU45n2Qz8hGUy6mXvDzuXEn34bpZ886L8slxXvl06SCR85i9jZ13PwH6ejNtV4Fdw1jeTLHiVmysVHdW/B0BMBUy9taJumfQXMF0I4JUm6BXgbmK17Mkm6GbgZICOj/S7ktKGJvPCtjAPV74IC2FdSxx1vrOGpa6bxm7n+PsfLdhTrNiacbpX88kbdgBnsyykQVDbok3Z3F3V+wqevUd3o4OFPtvgFRrtL5d73NmAPIrpRUW8nPsKqm4XLEqTEeJfHiiy3a8DWXQxOjmRwcsfkfaF6cNeVoYRGd1qmLeWyRzn4+HloLpvXJ9tkRTaFkDLP65WTevk/yPv3PDSPC1S3d5zSZCH5KK1trRmje5w0b4yMD3CdBECSMcam4an1Dl+Ej5lLyqUPB11eNx/cpFtDlGQFR/Huow6YhvA4EDqfNUnGGJVE9LRL8TRWUbn4eUBCaB6ip80j5eIHkRQDQlPZd980PPXltA45pfPvw5o2olf8zXsiYBbjL6KWBpS23kEIUd3qn68Cjwc7mRDiFeAVgIkTJ7bbJTAZFJ68eiqPfrKFkppmv6B5JMt7cfFunrpmqt9xabFhWHW6uWajErTjeub4DPa0WpIfgcVkwKNquNXAWw0z918SwopdJbrlDCQIsxgCHDgBkqNCGJgYQUZcWICepdGgcP6Jmb14x51DzZoPKfvs7y1LaY3Iib8i9fJ/IBvbZy2YYtMZ+vBKajd+jqN4j9cnaNIFPrUba8Zosu9fQtWKN3GU7CVkwBhiZ12LMapnpsp6EtbMcRiiknBVHvJbiksGE5m3vI4lpXMjs+akQS0ZYGA5whiTdtT3aUkfhTEqGWdlQcB9xs66FkmSSDjjd8TNuQF37WEMEXF+L8DmAxvQHI20zc+Ex0X1ijd07T+OFj2xptkEZEuSlCVJkgm4DFjYegdJklqnGucCPdbWykwI59VbZwZ1PdirM8c9Y3gSVpPBr/srSxBmMQYdqzxpeBInj0zBZJB9dbRQs4FH5k3kjLEZfrVS8NZKL+hnVr2tUW9z6Xqye1TBiYMTAtSgzAaZG+Z4BU3+fsUkxmTGYlAkzAaZmDAzD1w8/pgT6xt2fU/pJw+h2RsQLjvC46R+yzeUfPDXTh2vWMOJO/lq0q78J3EnXx0gDWaKTSPl4gcYeMcHJJ1/d78MluClvg28/QOs6aOQjBZkcyhKaDQZ1z/X6WAJEDvjisDsUzFgjEkjZOCEHrnPzN+/iyVlqDejt4QhW8JIveIxQlpNDslGC+aEzIDVgmqr0yfTC43a9Z9S/O6f0Nyd14DtDI46BRJCeCRJug1YjJdW9IYQYrckSY8Am4UQC4HbJUk6F/AANcC1R3vd1pBlb2NBr8Zo1bGfNRkUnrl+Ov/5egfb8qtBggkD47jz7BOCUlgkSeKuc07goilZbC+oJsJqYsrQRCxGhcyEcOpsLjYcKMeoyLhUjVPHpHLR1IE9+Zg9inFZ8SzcdCjgdybLcOm0QUzOTuCtFTk+NfUb5wxj2jDvHHlkiInHrpxMg82FzeUhIdLaq9a8enBVF+FpqMScPNRHWm7bQABvPax+y9ekXPpQv7JBEELQvH8Njbt/QAmNImrShT06+2yMTmbw3QtxVRejOZowJw1GUrr2dTdGJ5N1+/sUv/snXFWFIAShw6aTfvVTPSZSYopJJfuv3+Isz0W1NWBJG9HpqaGQQSciPEGEdoRG3eavQDGRdsU/euReAaQAIc9+hIkTJ4rNmzd3at+Xl+zh6y2HAua4L5icxXXtSL15VA1JoltyYW1xZMInLTaMyC4YdB0LCCG4f/4mdhXW+IKmxagwY3gSfz6vd+ZwewKqrZ5DL9+MrWAbkmJCqG4Szr6ThLm3su+vU3DXBYqjyKYQBv/1W8wJmbrntBfvxVNfjjVjdMed2R6A0FQO/fdGmg+sR3PakAwmkGQybng+aAPmWMPTVINkMPUL647WKP/mP1Qu/W9QvqZkNDPiyR0BXueSJG0RQnRNKIH/gUmfI7hu9lAqG+xsOFCBUfF2cacNS+pQ4ixYRtkdHA8TPtWNDt5fdYDNBysJtxqYPTqFkmobBkXi9LHpzOymknlfofD139GctwW7ZkDyuLAIJxWLnsGSOIiQQROp3/pNYMNDVjDqZG/uhkoKnr8GZ0UekmxAeFzEnnIdSeff06syb/VbvqIpZ71v2udIjbDozTsY/sTWDuutxwKGsM7P7PclEs++k5Cs8RS8dL23GacD1V4fEDC7i/+ZgGkyKNx/8QQq6u2U1nh9fbqiMP5LQF2zk1tfWUWTw42qCcrrobjaxgWTM7n2lJ4TXO4tuOsryM07xPyouyg1piIBA10HuKzuHazLXibt10/QuOt7LxevJWhKJitJ59+tO75Y+NpvvWrhmsdXA69e+TbW9JFE9aKCUe2Gz/RHIyUJW+7mDv2EPI3VeBqrMMUN6LFAcDwjfMRMLy1s1/e0bQDJ5jAM4fH6B3YDx41FRWeREGllbFbc/1ywFEKwJbeSZ77ZyctLdpN7uGu2FwBfbMzH5nT7dbcdbpVP1+fTYNcnZvcn1NdU83zU7RQb09EkA6pk4KBpCM/H/glnQzXmxEEMunshEePOxBCZiHXAGDKuf47YmVcFnMtdV+4VkmhjMiZcdqqWv96rzyEFmz0XtDtBozltHHr5N+y7bwq5/7qQvXePo/L73r3X4wVJ59+NbLZ6uZgtkExWki96oEcV6v9nMsz/ZQgh+Mdn29h4oAKHW0WW4JsthVw/ZxjnT+p8J35bfrUu/cmgyOSXNwZVfeptCCEoq7VhUGQSIoPPUq8uN+KRDH5fCiEp2ORQCtLOZQRgSRrMgBtf7PCaqr0eSVFaWS202hbMIK2HEDNtHs371vhP4gCSwdhu97no3T/RuHsFwuPyLePLFz6JOTadiDFze/We+zssKUMZ9JeFlH/zb+z52zDGppN41u097v55XATMBruL8jo7iVFWIqz9u5nSG9icW+kLluB10XR6NF5fvo9ZI1M6LSGXFGVlf0ldAAXLo2rEHaPa676SWv7x6Tbqmp0IvPPhD1w83s/5UwhBzar32bX8IG5DoKOgKik4s7vWLDEnDEQymKHtaKNi6PXgEz76VKImX0jt+gUghK97PeCW14J2slVbPY07lgZ6lLvsVCx+8RcfMAE0ZzPGqCSM484iauK5hGR237c+GPp9wHzmm50s3V7sHcfzaMwdk8bvzhylq1XpUTXW5ZSzt7iW5OgQZo1MDRDTOB6xeu9hXcqUIktszati9mh/wdkGu4tvtxayp6iWjPhwzpk4gIRIKxdNGci6/eV+IhYGWSI7ObJLEmk9hbpmJ/e8t8FvgOBQRSN/fHsd790xxzddVfHNv6lc9gppymhMEaNxyf7B3WAyMyRLX3S3sKoJm9PNwMQIn1kdgKQYSL3iMQrfuM1v9A/V4xXL7UVIkkTq5X8ndta1NO1bjRISScSY09vVdPQ01XobUwSWTryTLv0bqr2B2nULsBftxJIyjOhp8zD0oJ1x2Rf/pPqHt3zGZzWr3if2lOtIPv/uHrsG9POAWd3oYPmOYtyq5iNZL9tRTEy4mV/P9O9+Nzvd3PXmOg7X2XC4VcxGhTe/38+/rpnKwMT+w7/rDsxGr8WsHgHM2IYwX1Fv57bXVmN3eXB5NDbnVfLV5gIev2oKQ1Oi+PP5Y3lu0S6cbhVVE4zJjOXuC44NjWj5zpKAMUuBd0R1Q045M4YnozltVC57BeGycwJbWRJ2FnWSgip5X4Qmg8zgpEhGpPkbhJXX2fi/DzdTVmdDkSWEEPz+rFHMGf3zhIoldZgu8blq2ctET74oKA2pp2BJzu70eKEpNhX0sk9JIXTI1MCf9yO4qos5+Pi5P4+dGi1ULn6BQX/+vEfm5x2lOVSveNMnuwcg3HaqV7xB9OQLsCR33wywLfp106eu2RVgKub0aHy+oSBg3w9XH/RZGID3S9fs9PDPz7f1xa12iGanm5cW7+bSp5Zyyb+W8Py3u2jupLvlaWPSAiaJjuDEwQl+/35t+V4a7S4fH9WjekdE//2VVzjipOHJzP/Dqbx400m8d8ds/n7FpGNW5qiot+sKA6uaoLrR++F31RQjtdQsjXi4vepJJtnWEqo2EiEauXByFv+4crIfDUgIwb3vb6SwqhGn26tGZXepPPPNLg6U/Syp1rB9se5bSGgaDdu/6+GnPTpIitHbwGitlykryJYQEs++89jdWCdQ+snDqM21Pq6kcDtQ7Q0Uf3Bvj5y/YeeyAGk+8HoNNe5c3iPXOIJ+HTC1IKT6ZmfgL0fPyxugtMZGTVPP+xN3Baom+NPb6/hmSyH1NhcNdjffbivkzjfXogYxlmqN7ORIrp41BKMiYzEpWFv+e+jSiQH2tZsPVuraRBRWNfl+b4oskRrbPfuMnsTojBispkD7XUmSGN6SMRqjkvyk10JFMxc1fMTDFXfzRMwirps9LGCMc39pPTVNjoDfg9ujsnBTQasLBZ/qCrbtWCJm2qVk3vIaoUOnYYrPJHrKJWT/9dtuWWX0JZr2rgzkxgqB7eAmRBDZua5ANpiQZL3PkeKtU/cg+vWSvO0X4QgGJwUq1rTnv9PXY3ttsTWvkrJam19A96iCihaifUfWtQAXTx3E7NGpbMmtwmxUmDQ4HovO2KfZqNDsDGz9SlI7iktCsKuwhjX7yzEbZOaMTu2TufCpQxNJiQmlqOpnvx6zQWZsZixDWlTsFWsEUVMuom7DZ35LLsloIeHM23XPW9fs1CWeawJf5goQOfYMyr/6V6BgjiQROe7Mo3y63kHYsBk93vntbUiKCRFkprtm3SdEjT/rqMZWI8adxeGFT+hcGCLHd6wk3xX0v9doK8RHWDAbFZ/VhCx5A8JvzxgZsO/csekBy1ZJgoGJ4cc8k8o93BCgLwngcKn8bcFWnvjiJ2qbOhYJiAmzcNqYNGaOSNYNlgC/mjAg4PdgUCSmDUnya3ocgRCCfy3czv3zN/HFhnw+WZfH715bzVebCzr3cEcBRZZ5+pqpXDZ9MGkxoQyID+O6OcN48FJ/ak3qvEeJPflq73JUVjDGppNx44uEDtKfbBueFo1HZ7VhNshMyv65hGGKyyD5wvuQjGYkQ8t/RjPJF96HKfb4MqDrz4iafKF+picEZZ88zL6/TqE5t3Mj0HowxaSQevljSEYzsjkEyRyCZDSTesU/MUZ1nIx0Bf1+lnzBohXMX32AvPJGBiaGc/mMbF2bXJdH5b4PNpJTWo+qaRgUGYvJwL+vnXbMrSJ+2F3Kv7/aodvpBu+LIDbcwmu/nRWwxO4qPKrGY59tY+PBCgyyjCoEmfFh/OPKyYRZAhkD2/KreOijzQH3ZjLIvHv77GP+smkNoakItxPJZO1wdPHdlTl8si7P96IyGWTiI6y8cNOMAEEWV3UxDS1OihFj5mKKPXrpsv+Pn6E5beQ/fzX2ot3eqSoduTglPJbhj23SXVp3Fp6mmpZpHwgfNQdDWHTQfbs7S97vA2ZnxTfAmy3tKa5lf2k9CREWJg9J7FFV7u7C5VGZ99QybO1YzVpaMufTe8hat7i6ifyKRpKiQshuR3T32W928s3WwoCfW42Kt6t8wvEbPNbnlPPlxgIaHW6mD0vk3BMzCTUf/zSz4xFCCOyHtlP0zh9x6XgIyeZQsm5/n5CscX1yP7948Q3wFutHpscwMr1/CQWYDApzx6bxxcaCoPs43CoHSut7LGCmxYb5kb+DwWiQ9SlLUs8KkxwLTBmSyJQg+qYdobrRwYdrDrIlt4rYcDOXThsUwEj4pUIIgS1vC80HNmAIjyVy/NkB2qFtIUkSIZljMYbH6QZMJAmhpxLfz/A/FTD7M04emcJ324qCLsvNRoX0+L4nj596Qhrfbi0MoG9pIpCy9EtBdaODW17+kWanB1UTlNQ0s7+0nutOGdquKLTmceFpqMQQHtsjikOay8HhhU9Ss2Y+wmXHFD+AlMv+Rnirpo8Qwjst1IPz0u1BqB6vP/v+tWgeJ5LBTNmnj3qzw8yO+bzRUy/Bdmh7gBybpBgIGdDzkzk9jf8fMIHtBVU8+80uDtfZMBpk5o5J4/o5w4+6ntgaw1OjmDIkkfU55QFBUwJMisypo/t++ZudHMkVM7N5b+UBZBlkJDTggYvHE9KBxYYQghW7Slt0SFVmjUzlnIkDgrIbjhd8sHQLzXYnaqueqNOt8taK/Zw5PiPgcyGEoHLpy1R+95yPJhN78jUknXf3UQWyQy/fRNO+1T5Kjqsin4JnryRs+EwMEXG4a8tpzt8CHichWeNJuexvveJj0xq16xfQtH/Nz5xKlw0BHHrlNwz72zrf8wohsOVvxV1TijVjtG8IIOrE86nfuojmnHVoLpt3qkqSyLjxxS4LHB8LHFc1zGaHmy82FrB632HCrUbOn5TJ1CGJR6VduGZvGY8u2BqwJM2IC+O/v5nZLl2pq9CEYP3+chZtKyT3cIN3flrAiPRo7jrnhE4toXsLFfV2NudWYjbITBmSSKhOg6gtnv5qByt3l/peACaDTGZcCE9fNwOjTkf+eICjZB+/eWU1hw2BuqAhJgNPXD0loCZcs+ZDSj95yC9rkkxW4k/9DYm/+kPAeYQQHX5mHaX7OfDYOaB23mJBNoeS/cCyHlVub4vcJy/Alr9V59ohDLxrAdb0kbgbKsl/5grcNcUcMS+LGHsm6dc8jSQrXrX5Axto2r8WQ1g0URPP7RPh5tb4n69h2l0ebnttNVWNDh9nb39J3VFpOQoheGbRLt2Rw+LqJjYeqGDq0J7zbZEliWnDknxWDx5VQxNCl+7T10iItHLW+M4ToIuqmlixq8T3txjgyuXCyg9JLipl704TcTOuIOmCezttN9Bb6Exwao3DXz5OhGeEbsD0aBpRoYFTURXfPRdojeGyU/X9ayScdYcv66pZ+xHlXz+Np+4wxth0ks6/l6gJZ+veh6M0R99RsR1oHjfVP7xF8oXtexgJTaM5Zy3u+gpCssZhTui84lXwBOvnSnjRm3fiLM/zk85r2L6Y6h/eJm729UiSRNiQKYQN6Z5F8bHEcRMwv/upiOpWwRK8jZIF6/I5f1JWt+gvjXY3jUF0IDUBu4pqejRgtsXx3FTZXVTjG8OO95Rzc81zmEXL79LjpGbNfNz15Qy46aU+vzchBF9vOcQHqw5S0+QkOdrrSdQZ219b3lZO8RSTbxqEW/75M6UINyOSY4mPCJSf8zRU6p5Lc9oQqgtJtlC9+gPKFjziC6zu6iKK37kLSVGIHHtGwLHmxIHoqwe0A9WFvXh3u7u4akrI+/elqE21gEBoKpHjzibt6qc6VT6InnoJjtJ9AS8I2RyKJXUEqq0eW+5GXZ3R6h/fIW729V17pn6G4+Ybu+lgZUBjArxd3p/yq1i4qYBnF+3ku22FQRsrbWExKUGzDwlIbEeb8ZeOqFCzb4JqVtMSDG2EJYXbQePO5bhrAz12ehtfbCzg1WX7qGkZBiirtfHEFz+xPqdjVR9DRDzZrhzObVj9t+OkAAAgAElEQVSAWbNj1uwYhJtB7lzuu0i/qWFJHa77c2N0MpLBjBDCO1GkY9B2+EudCRXAmj4SS/LQDu/XD4qRkIwT2t2l8NVbcdeWoTmbvQHd7aT+p2+pWTO/U5eImXYpoYNORDaHAJLX7dEcSsZNLyHJMprLgffbEwitrZTecYjjJsOMC7cgSwTMB6uaxjOLdqFqAqdbZblR4Z2VOTx3w4wO/XVMBoXTxqTx3baigG1Gg8ysUb1XCzreMWFQPGajgsOlkuwpRSHwZSYZTTgrD2GM7jufIE0I3vvxQMBkldOj8daK/R3SjBLOvI3i9+9hqn0NJ9o3UGFIJFx2MWDy6USE61Nnki+8n/znr/KvYRotJF/8f0iShOZxoTbV6B7rrg787B3BwD9+Qt5/5uEo3NXuPbdcEdloJnbWNUH3cNcdxlGyz88DHLzZX82P7xJ70pUdX0UxknnbOzQfWN9CK4ojauI5KCHeuq4hMgFDZELgcynG/wnNzuMmwzzvxAEBJHRZkhAC7E6P7wvicKvUNTl5ZWnnrM9/d8ZIpmb702fCLUaeumbqL1KsuLMwKjJPXjWF5JgQSs2Zfh3lIxBuV8vSsu/gcKnYgwwIlNV2nOFETjyPhLPuQDaFYDKbSaWKjAmzSL7kwaDHhA4+kYF3fkjYsJMwhMcRMnAimbe+7ltqS4oRQ5h+U8PYzgimYgkj+55vGPLwKpIvfpCEc/+CMToZ2RyKZPSOiR75L3ToNAb9+fN2RwE1l8Pro6y7rfPZn7cGOZXEs+8kduavfcHyyLb0a572jrG22G1IJivG8Lh+r6rUGRxXXfKVu0t55pudCOHNLFNiQjlU2airzmMxKXx5d2BtKBga7C4OltUTG2YmIz68V10DW8OtamzIKaeszsagxEjGZsUec7GQrkAIQcGBfTS9eCG0+tJJRgtRE88h7ap/9cl9VDc6WLv/MB5V8O7KHF0BkoGJEbx080mdOp/mcuCuLcEQHucXELqLmtXzKV3wcEAWmn7ds0SOPb3dYz2N1dgLd2AIj8OcOpzm/Wtx15URMmCMV9OzkxCaxr77pgQIDksGE3FzbiTpvJ4T23VVF1G96n1cFfmEZk8hesrFHZLb+xK/mNFIt6qRX95AqNlIUrSVcx77LkCEFiDMYuDTP7f/QTzWqKi384c319LsdOP2aBgNMmmxoTx59dSAeef+DnvxXsoWPIwtbwuyJZzYWdeQcPrv+oRbt2xHMc98sxMJb8nmiCxg68+F2SDzwCUTjikZv2bdJ1R8/TTuujJMsRkkXnAvUe2oIgkhKP/6KaqWvuythWoqpthUsn7/XrdFJZpy1lHw4nUIVQXVhWQKwRiVyOC/fNkjL4bjBb+YgNkWj3yymfU5FX5fDqMic+b4dH53xqjevsWjwt3vrmfHoRo/3U+jInPOxAH8Zm7vEpD/V1DT5OCa51YECBEbZIkwq5EGm4uUmFBuOnV4t8ckjxXqf1pM0Vt3+DeLZAVr+igG370w6HGbcyv5YmM+DTYX04Ylcc7EAX4z9K7qYmrWzMdVXUTY0OlETTzvF2fXe0x5mJIknQE8AyjAa0KIf7bZbgbeASYA1cA8IURBT1z79rNGU1i5jqpGB6omkCWJAfFhXD+773y2vcICO3DXFGPNGN0pQVeHW2VnYU2ASLJb1Vi+s6RbAdPm9LByTynldXaGpEQyOTsBpY9G5o4V1udUBNG+FJw9PoOrZ3Wx09yPUP396wGddTQVe8k+tm3bztARIwOmsT5ac5APVh30MUXyKxpZ8lOxn0qTKTaNpHP/3CfPEAzVjQ7e+/EAGw9WEGYxcuHkLOaOSeuzUlh3cdQBU5IkBXgBOA0oBjZJkrRQCLGn1W43ALVCiMGSJF0GPA7MO9prg5fe8sqtJ/NTfjUlNU1kJUQwMj26z37xnsZq8p+9ElflIZBlhOpu4bX9q12pqvYy++5k/YcqG7nrrXV4VA2HW8VqUkiKCuHpa6f5vlSqvRF3TQnGmNR+VU86Gqiad5Y66LbjGJ5mfbtfhyrx5tcbKP6ulJtOG845EzMBL6/4vR8P+GXbLo9GVYOdb7cVcuHkvm3AgTcw1jQ5SY8N9Wm41ttc/PbVVTTa3aiaoKrBwYvf7aagorHfr6x6Iv2YBBwUQuQJIVzAh8B5bfY5D3i75f8XAHOkHoxosiQxfmAc50zMZFRGTJ++pYreugPH4QNoLhuao8nHa6te+U67x1lNBoamRAUw1gyKxMwRXafh/PPzn2h2uH2Zhd2lUlzdzPzVBxGaSunHD7H3ngnkPnURe++ZQOnHD/aIPcCxxpQhCfrmcIrMSZ0gqvdnRIw9XVd4VyCRL5JwejReXbaPPcW1AOwvrdMdhnB6NNbnVPT6/baG3eXhwQ83cc1zK/jLu+u59KmlfLjGq1K0cFMBzQ6P3wvN4Vb5avMh6po7Pwp6LNATATMVaE26Km75me4+QggPUA/07fBoL0C11dOcs97fppWWqYaVbwfs7yg7SNGbd7L3vqnsvW8qV5lWEW4x+MQcrCaFpMiQLo961jU7KaxqDAgcblVjxa4SKha/QM3aDxFupy+o16z9iIrvXujSdfoj4iOs3DhnGCaDjCJLXlV+g8z5k7MY3I4O6PGA+Dk3YoiM99n+qki4MLIg4nI0yfuZcbl/9imKDDGh6WTVEhAb1jcjqnaXh6Xbi/nDm2vZnFuJW9WwOT04PRofrDrIj3vK+Cm/Std/y2iQyS1v6JP77C56ooapl861/at1Zh/vjpJ0M3Az/D/2zjpMrir72u+5pe3uko52QlyIIwkEl+AOgw4Dgw0M/hsYBhhgBtfBh+AWNBAIkIQkxF07aUu7e/m95/ujOpVU1632TtJ8s56Hh87VU7bvOXuvvRZkZvauuZNUPTirCjCERmGKTNA9RrU3UvXjf2jYsADFHErcsVcQM+Vc3bYxzeUIapalOVv231fTKH73r9Sv+tzPDMr026vcH51ByRmvU97sYUhyFNOzk7rcMtkeDUkIoZsLky47Nb+8QdIp+r44/QlnTh7IpMGJLN1eikeTzBie3O+tlQEMoVEMvfd7ape9T8naH9lUa2RxyLGUmvZzNyX47E2GJEcSH2mltLbFj2pnNhk4c3JWn493d1kDd81biUfVdLvynG6Vj5bvYVBSJNuL6wPy96qmkdBBs8mhRm8EzGLgQPZtOlAa5JhiIYQRiAJ0Wx+klK8Cr4K3Su6ur0BqHkwxqb6ldsPGhVR88xTu2mKsqdkkz72bsCGT2x1k/dqvKf3wfqTqQqoeQodMJvPqF/3M5DWXnT2Pn4G7tsQno1/60d+w5a4j/dLHA67p7WpIwF1T7L/DYCRyzP6uhvrV82lY902gc57mwdBczmT7CuJnX97u+NtDZKiZwUmR5JQ1+KXzzEaFOWPSUHfpP7VVW4Pu9oMJt6rx6/YyVu+pJC7CysnjM7ql2pQWF8ZFR3XO47s/wRASQcKcPxJy1FX839OLAtgAFpPCjFYxFyEEd581jsfmb6Sq0YFBEWia5E8njmR4WnC7ht6AlJK/f7xWl/96IOpanJw9ZSBLtpXi9Oz/shoVwaCkyINivtcT9MaSfA0wVAgxUAhhBi4E2nIevgL29WydC/wsO1HZcJbtZtffZpLz91nk/H0W9r1bqFv5GUVv3YyzdCeaoxlb3jryn7+Mlt2rgl7Hlr+B4nl3oNrqvf2zHhctu1dR+MrVfsfVrZ6Pu77cz3NEuuzUr56PqzrQxkEIQfplT/p3NZisGMPjSDzlFt9xNUvf8XM8PBDSZaclZ0VHb0WHuOus8USFmgkxGzAoAqvZwODkSC6YMSRor7M17dAm2J1ulVvfXM6z327hl62lzF+Vzw2v/sqKneWHdFyHI8KtJv4wK9tPa9RiVEiODuWEcRkUVTdz42u/cutbKyivt5EUFcJNJ4/i49vncNL4vjd0K6hsoskeaH99IBQB47LiGZgUyX3nTiA23ILFpGAyKIwfGM9DFx7Z5+PsKXo8w5RSeoQQfwYW4qUVvSml3CaEeAhYK6X8CngDmCeE2IN3ZnlhZ66tuR2+4OWqzCf36QtQTNaA4CPdDsrmP8qQO7/UvU71T68F2nyqbux7t+KsLPCJmzbvWBZI48CrBm3L36BLFwofNpVh9/9AzdJ5OCvyCBs2ldjpF/jZhmo61zzg6jjKdlPx7TPEzrgIU3T3uIJpsWHMu3k2K3ZVUNngpRWNHRCHEILU8x8k//nLkB6nt6IsBMJkJfX8B7t1r97Ct+sLKapu9i3fVE2ial4Xy4+GJh4WfkyHE86ZOoihKVF8uaaAhhYXM0ckc9K4DJCSv7y9gia725fnKqpu5uWF23xSgn0Nzfu1CgpFgFFRmDgoHiklU4Ym8d6tiVQ22Am1GPtNG3Kv8DCllAuABW22/e2Avx3AeT2+j+pBddXp7nOW7Q56nqumGL2UqTCY8DRU4Gmsomz+o9gLNwe9hjEqeCDbZ9eqB83j6sBMXuIq30PVwpeoXvQqA2/5gNCs7kn1m40Gjh0ZKBgSNmQyg+/4jMoFz+Mo3YE1dQSJp9xESMahJfYv2Vamm+uSwJ6yBkak9+0ysj9izIA4xgzwr5f+uKkYl0fz+4ZLvOmOZTvKOP4gGNkNTIogxGzE7gp0E0B4V2OqlDz/3Vbmr873dbMlRx9aR9euon/133mcCIMJqVdha6dVLHz4TBwlOwLsPaXqQnU52PvqdUGXzCgGDOGxHeZIg6Hss3/gKN3V4XHS40R6nBTPu51h/7eoW/dqDyEZoxjwx//0+nV7AqvJQLK7lKm2pUSpDeywjmJ9yJFomqFX7UF+76hssOtKGjpcKpUN7a1ueg+KENx/7gTufW81UkqcHg2ryYDRoGBzun0UIrtLpaCymbd/2cWfThx5UMbWm+hXax7FEkbEuJO8OcMDIMwhJJ1+e9Dz4mdfjRISCQf0NQtzCAkn/ImqhS8GDZbCaCEkfSSDbvuoW94smttJ3YqPwaPHLdNfv7gqC/AEkQI7XOFpqqZl9ypctW1rfe3jrKg93FzzBNNsyxjt3MSZDZ9wa/XjJIVKXe/5/0Efw1KjsJoDHzAWs4FhqdE6Z/QNRmbEMu/m2Vx9/AgumjmEe8+egN3lCRDHcasaP28pOWjj6k0c3jPMA5IiwmjBnDCAjMufouqHF6n+6XWkx4ViCSP5zLuImqAv9Q9gjIhj6L0LqPz+RZq3/YIhPI6E468lasKpVP/ylv5JBhOD7/yiR6ZSmrMlsDLue0EEFdQWxr7P50hNxV1XhiE0qttdP1LTKP3wfupWfoowmZFuF+FHHEPmVc932JusuZ1ELP4nmtw/67fgIk6t5o6MHIQILkrxP/hj4uAEMuLCKKxq9lXRTQaFzLhwJgyKD3pek93Nsp1l2JweJg5K6JWHVGSomTOPzAJoV8j7MJawaBeHdcA0RSVhThyE1DxEHzmXhDnXo5jMJJ16G4kn3YTmbEGxRnRq9meKSiLu6EtBdeOsKsRZmY+npR5zbBoOHXqNYjRhTekeTUW1N+Es340hIgFDaJSuhYE5Pgt3fZn/7FYxEpY9HYO1b83Q6lbNp+zTB9HcTtBUIsefTPolj6OYu6YwX/3Ta9St/tyXTgBo3r6E0k//TvrF/2z3XEfxdt3tJulG2bUIuEV3//8QCEUI/n35ND5YtoefWmdux49J48IZQ4JydDfkV/PAR2sRePmP//1lF3PGZvDnk0f2Wqec1WQgOzWaHcV1fnMDoyI6ZRdyOOKwDpjGyASyH/xFd58wGLskR9W0bTGFr13vzWNqKra8ddQsfpvkuXdT8uH9AY5/cbOvRhg6dk5si8rvnqfy+xdac60uzAlZeOxNsC8wtlaoM656jsoFz9G889fWgC8wxaSQfvmTXb5nV9C8awUlH9zj93obN35PkephwDUvdela1b+8GUiI9zipX/kZaRf8o11pN8UaFrQ1U/md9LkfTFjNRq6cPZwrOyE64/KoPPTxujaq9JJFm4uZOiyxVyXw7jhjLLe9vQKnW/VpHMSEWbhqdv8URTmsA2ZvYV+nzYE/bul24NE82Ao2kHbRo5TPfxS1pR5hshA+4mgat/xM7a/vE5I5ipRzH8Sa3LFwQf26b6hszYnumzm6KvMJyRyFYjDjqt5LSNZYkk69DWtqNll/eh1H6S7sRdswx6YROmRyn/fBVy58QcdbxknT5kV4mmsxhsd2eA1H6S7qfvs0qPmX1DxIj6vdgGlJHoo5JhVnZb5f2kKYQ4k7JrjNwv/Qc2wurNVNoTvcKj9sLO7VgJkWF8Z/b5rFkm2llNS2MDg5ihnDk/stZazfBUypaTTvXIatYAOmqCRCBx9J7dJ5NO9ahik6hYQTric8e4bfOe66ElSbTreL6qFpy8+kXfgI0ZPPQnM0U7N0HhXfPuMr1DRvX8Luh2aTcfULRE88rd2xVf/4H50ZlwvH3q0Mf3Q1xvBAmow1NRtr6sF72gZ0JbVCaipN25cQM/msds+vWfIOZZ8/glTdAd4w+2BJyGo1yQoOIQQDbniL/GcvQm2pByGQHhdxx1zxu/B+OZyh12++D2ofJBdDzEZOGt+7bc6HCv0qYGpuB/nPXoyjZAea0w4mC7idXp8STcVZtpuW3DWknvsAsTMv8p2nWMJB0y++7Fv+CSEQBiMVC57VqWpLit66hfDs6e3OwDxN1fo7FCOqrV43YPYWpOpBtTdiCI1qV1YudPBkLy+1bbDTPBS/dzeephoSjrtG91xPUw1lnz8c2ASwD4oBYTSTetGjnRqzJWEA2Q8tw5a7Bk9TNaGDJnWbuP8/dB5jsuJ0g6bVZOD4MW11cw4eVE3jq7WFLFi/F4+qcezIVM6bNjhA8/NQol/Ni6t/fgN70bZWu07ZmheUfj9+6bJT9vnD3oJGK4zhMYQNm+JHK4LWXOWs/T7Jzsp8RLDStaZSv0a/k2gfwkcc5TWlagPFZMbcjtlVTyClpGLBc2y/Yww7753CjjvH6yol7UPSKTd7izt6S3+3g4qvnsDTrN8c0LxzGULR//IawmKInnwWQ+78ivBhUzs9fqEohA2dQtSEUw9KsFQ1jS9W53PNy4u54vmfeeOnHTQ72m/p+73BajJw19xxWIwKJoP3e2BQBCaDwsKNxWzZe2hobf/4ZD1v/byLvVXNlNba+PS3PG57ewUeHd71oUK/Cph1Kz8LTjA/EFLiqirw25Rx5XOEpB3h9VG2RiCMZmKmnUfs9P06xqaoRKQaTDxABphHtUXiKbd6KTq+YpHXtzm1gwJIT1D1w8tU/fASmrMF6XGh2hoom/9P6lZ9rnu8OT6TIXd/gzFKn+gvEbTsXqm7LyjdSShETTqdjMufxJo6rFuv42Dh0c838OZPOymqbqG83s78Vfnc/MZyXJ6+0wYtqWlhZ0ldn96jq5g+PJk3b5zFhTOGEGI2IIAmh5uVORXc/e5Kvl5bcFDHs6esgfX51X6FKJdHo7i6mdcW7dCVgzsUOHzmup1AZ8shUnVjaLN0NobHMuTur7EX78BdV0pI5ihMbdodjRHxhA2ZTEvOb4H3NlkJGzat3fuaY1MZet8PVC16lZZdKzDFp5Nw/B8JG9xl65BOQUpJ1Q+v6Eq3VX77NDFTztY9z5I4kIiRx3hJ9W15om4HZZ/9g8gxxwewBCJGHqt7PWE0EzO1x52vfY6CyiZW7670U/xxq5KaJgdLtpUxZ2zvthDWNDl44KM17K1qxmBQ0DTJ9ScewcmHST4vPtKK06PhVjU8ByzRParkhe+2MXZA3EFTD9pRUq/rNODRJF+vLeC3XRU8e9UMYg6Srmcw9KsZZsy083xiqsEgjGbCsqcH1bsMSR9B5OjjAoLlPmTd8BbGNm2WwmAiZMBYwod3bNFqik4i9dz/Y+h935H1x9eCBkvV3oireq+3eNJNSI8TzdGku8/dzmzYW5nfHpRU764toeSjvwVsV8whDLj+NRRLGIo1HMUSijBaSDz1VkIHjOneiziI2FVar8tLdLhVNhXW9Pr97n9/NbnljTg9XhFdh1vl5YXbfQrphwNW76nEo+qnoR7/YmOv3ENKyfbiOlbvrgyqaBQfYcWg6E+JVA2qmxy8vHBbr4ynJ+hXM8y4WVfRuPUXHEVb0FxOFJMF2fqjFwYj0uMmbOhkMq96vtv3UMwhDH94BTVL3qHut49BMRAz5Rxij7qkW+2RbaG5HBS/dyeNG77zFkkMJlLOvo/YGZ0ScPKDMFowRiXhqS8L2GdJ1ifdu2qKyf3XWX4Cx3qoX/2FLvk8PHsGIx5bS+PWn9FcNiJGHKObe9Rcdiq/f4G6lZ8BkugjzyTx5Jv7nJTfHuIjrLqpW5NBIaWXRSAKKpsorbMFtAW63CrzV+ZzxLndLwDaXR5eW7SDnzaX4FY1xg+M58aTRpIaG9bla0WHBe8qy6toxO7y9MjyuaS2hXveXUWj3YUQAo+qccWxwzh32mC/444ckkCI2YjDrep2AamaZMWu9lNiBwP9KmAqJguDbvuIlpzfsOVvwBSdTNT4k0EInOV7MEYm6IpweJpqKJv/KI0bFyIUA1FHnknymXcG/fEKxUD8rCuJn3Vlj8brrq+gZsl/se/dQkjmaOKOuZyyzx+mcdMPPiEQiZ3STx7EFJNCxBHHdOn6QghSzr6X4nl/9cvtCpOVlLPv1T2natGraG1ESHTRzsxXsYS2S7GSmkbeMxfiKN7h6wCq+eVtmrcvZcg937Zbxe9LjBsYT0SIGafb4af2rQg4cVw6y3eWs2D9XpxuldmjUpkzLqPbfMHqJodu+VACNc2dyMMHgZSSe99fze7SBl9eb11eFTe/uZy3bpxFREjXmi3OnTqIjfn6s2shRI9aGKWU3PveKiob7H7vxTtLdjMsNdpPdcloUHjyimk8/On6oDYVh4OhZL8KmOD9EMOzpxOePd1ve0jmaN3jNbfDq6LeUO7z3qlb/gH2/PUMvuvrPiOKO0pzyP33WUiPq1WweCU1i99G8zh1PYAqv3+xywETIHrSGSiWMCq+eRJX9V6sKcNIOuPOoJVq+97N7QbDfQgZNLHLY9mHlpwVOMtyfMESvOkDV/Vemrb+QuSY47t97Z7AoAievGIaj3y2nj1lDaiaROItLtzy5nKa7G6f3FxOWQM/bS3licumBl0qBsOO4joe/Wx9m04aL8xGhclDu08M313WQF55o18RRErvzHXhxiLOndY1Z8gjhyRyRHqMbppA0yQ3v7mMq2ePYFp21xkMOWUNNNhcAQ8OZ6sPUVuZutTYMF667ijuf3816/Kq/GbnRkUwU0fb0+VRWbOnika7izED4kjrxiy7K+hXOczuoGH9AtSWOr8gJT0unBW5usWd3kLpR//nNRzbN5P0uLzL4CB8UHdd99VbIkcfx9B7FjDyya0MvuPzdmk9IWkjdKlPB0IYTGT84Zluj8dWuNmP1rUPmrMFW+Gmbl+3N5AYFcKjF08GsV/7RALVTU4/bU6nW2VPWQOrcrq2DHR5VO57f7WuVYPRIIgJs3D6xAHdHn9hVbNu9dPp0dhT3j3LkYcvPpLMuHDMRv9wIIGi6hb++fl6nwp+bnkjX67OZ/G2Ut0HwoFocXiCTkgabcEf2refMZaEqBBCzAYU4TUHTIwO4fo2cnC55Q1c/MxP/OvLjby8cBvX/2cpzy/Y2i2b6s6i380wuwp70VbdfJ1UPThKdgTMVHsLLblr9HfoFVqEQtjggyPPH3/ctdStnu9fWTeYUMwhKNYwwrNnkHL2/T0i2Ztj01BM1oD3XZhDMcf1vZhtR3huwdaghY4D4XCrrMmt6pJq+brc6qDL2MFJkTxy8RTCrMGXzRX1Nn7cVEx9i4uJgxOYPDTRb4abER+uq3JlMSoM7sD4rcXpZsm2MmqbHAxPj2HCoHgUIQizmHjl+qNZs6eSJ77YGBDsvXa+O1i8vZSVuyrQpDf4P68oPHHZVAYn6993eFq0LofSYlKYOSL4exoTbuHNG45l1e5KiqqbGZAQweShCRgOqCFoUvK3j9YGFJEWbS5m/MC4PhP3+N0HTGvyEIQ5FOmy+W0XBhPmhKxev5+nuY7yLx8P2jYoDCYwGPcHLMWAYg4h8ZRbe30serAkDWLQLe9T8sF9OEp2IIwWYqafT8rZ96F0wEDoLCLHnUjppw+By37AA0KgmMxETWi/vfRgYF2efg98WxgNot2iiB7sLg8ySPNDelx4uznGVbsreOTT9aiaxKNJftxSzJDkKB67dIovl5qdGkVWYgS5ByzLBWAyGjhxXPDmiNzyBv76zkpUTeJwq1hNBgYmRvD4ZVOxmLw+UJOHJgY1MSurt1HbvH8W7p1cqvz947X896ZZujPJUIuR6+aM4LVFO3G5VSRgMRlIjfH6ELUHo2G/uZse9pQ10KLTcOBwqyxYv/d/AbO7iJp0BuVfPoHqduz/8SoGjBFxQXmF3YXmcZH7r7m4avWX18JoIXbmRYQPn0nl9y/iqS8ndMhkkk67zecrdDAQOnACQ+/9zkvSVwy9nsdVTFYG3/E5RW/djKPISwWxpGaT8YdnMFj7NsfUGXS2kGMQghPGdq1Da9zAON3Zq9VkaHdW5VY1Hp+/0S8t4HCp7C5r4IeNRZzauowXQvDPS6bwyg/b+WVrCR5VY2xWHH8+eRSRofrBXUrJI59t8AuGDrdKbkUjn6/K46KZXkaFIgSx4RZqmwPTKUZF6OpbNthcFFY1B9XSPH1SFoOTo/h6TSH1NiczspOYMzbDz8ytO3CrGiIIM7ujVEFP8LsPmAZrOIP/+gVFb9+CvTV/FpIxisw/vtbr1dqmzT96FXx0iirCYCZs2FSS596DYrYSOWZOr967O+ir7iPw9okPufPL1jZL2SkVpIOF0yZl8s5ifQ+oEHPrA0TCX+eOJSVmP93Io2p8sGwP36wtxO5WGTsgjj+eMIL0uHCklPyWU8H3G4pIjLJS0WD3BQmm0GUAACAASURBVE6rycCozFimDA1eOMkprdddyjvdKj9vKfEFTPDO3P5y+hj+cvoYpJQdPvAqGuxUNQZaVbg8Gos2l/gCJsClRw/lPz/u8As6FpNCTJiF8nodg0BBh62LR6THcEQv+zMNS43WrZpbTAZmj+67fvjffcAEcJbvxlGyE4QRNDfO8t2UvPMXsm78b68GDW+fuw6/UVGIm/WHoEZpvweojhYaNy1EtTUQnj3D1yLZl4Ij3cV50wazenclOaUNvkqs0SB45OLJhJiNuDwa2alRmI3+D9THv9jAypz9nUJr9lSyraiW1/50DPOW5vDLllLfLMxsVIiPtDAiNZpjRqUyPTu53Wq7yaAEXcq3HceB6MzqIJiIsB72BeZ3FufQYHMRE27hylnZ2Fwe3vxpF8427Z0Wo4GBHeRO+wImg8KNJ43k6W+3IKXEo0qsJgNDkiM7XO73BL/7gKm5nRS9fasfT1Fz2mjJW0f9mi+ImXpur93LkjgQxRyK1iZfqphCCO0BTedwR0veOgpeuByk9C7zhSD6yLmkXfJYn+t7dgdmo4FnrpzBxoIadpbUEx9h5agRyViDELRVTeP5BVtZut3fL30fJWnekhx+2lLi13Lp8mg0tLg4bdIAxg3cbxPR4nBTXm8jISrEz1p2SEoUYRZTgOui1WTglIk9a6VMjAohOTqUoupmv5BsMSqcOC6wCHfqxAGcMiETjyZ96QuXR2Xp9jJyKxpxuFTMRgVFCO45Z3yXaVc9RbPDzWOfb2BjQU3rLFMwdkAMcycPZMqwpD4dz+8+YNry1+tuly47davn92rAjJpwKmXz/+lVUdqXLxUGDKGRRI4+rtfuczhBqh4KX7kGzdHst71+7VdEjJpF1LiTen4PKfE0VWOwhnfJRkNqKrb89WguB2GDJvppdAohGD8wnvEDg3ve7MMrC7fz42Z9HVG3qrG5sAa3jl2wW9X4bsNexg2MR5OS137cwTfrCjEqCm5V47gxadx8yigMijf4/P2CSdz17ipUTUNr5YjOHp2myz/sKu47ZwJ3vPMbbo+G26NiMhoYlhrF3MkDdY8XQviUjMD7kPnX5dNYm1vJxvwaYiMsHDc6jdjw3ikUdgX/+GQdW4tq/XLFO0sbiAoz93nw/t0HTGEwBXVcUtr1C+86FEsoQ+78gqJ5d2DLXQt4WwnTL32iW3YX/QG2gg0B9sUA0mWjdvmHPQ6YTdt+oeT9e/A0ebtRIsefTPrFj3UoUGwv2kbBi3/wpkiEgtQ8pF30T2KmtC+QHHAdl4fvNxYFpSEZFUF0mIWSWpvu/vyKJjQp+deXm1i8tQRNggtvcP1lSwmRISauPm4E4J1lfnDbcaxq7bkenRnrpRH1ArISI3j35tks21lOTZODEekxjM6M7dIKwKAIpgxN8uViPapGk91NuNV40FYSFfU2thfXBXweLrfKp7/lMTKjb3Plv/uAGTpwvNeWt01uUTGHdqt/uyOY4zMZfNvHaG4HXirNoVVX6WtItR1nwB4Ii4CXQ1v42p/8/Yc2fM9eWyNZN74d9DzN4yL/uUu8DQsHoOT9uwnJHIk1pfMSdHXNznZzgEaDwknjM9lWpC+oIQQ8/Ol6lu8sD9jn9Gh8vbaQq2YP9wUcs9HAUX1EibGajRw/puc8WFXTeOOnXXyzrhBV1YgMNXP9CUdwzMjUXhhl+6hpdmI0KH7pD/CmRyoOggd7jzp9hBCxQogfhRC7W/+vm+EXQqhCiI2t/33Vk3t2eYyKgaw/vYESEoliCUOYrAiTleip5xLRh5VqxWQNCJZSU6lf9w0Fr1xD4es30LR9SZ92JRwMhA4cj17riWIOJWbKOT26dtWPrwSou0uPk+ZdK9r1QG/e8atusJaqm9rlH3r/lhKnW/XrKddDfKS+YAd4q9WPXzaVGcOTMBoCDzIoMDApknW5wXmfDlfHYzjc8MrC7XyzrgCnW8WjSWqbnTz59WbW5wVxHOhFDEgI163KGxXRqfRKT9HTGebdwE9SyseEEHe3/vsunePsUspxPbxXtxGaNY4R/1xN4+ZFqLZ6wrNnYEnqWs9tTyE1jcL/XEfzrhU+En3T1l+InXkRqecGSqn1FygmCxlXPcfe128ATW31ig8ldMgUoied0e65UlNxlOxEGM1YkocELOucFfm6nVHCaMZdV4I5Vn9Go9oa9NMwmoqnuZaVORW8tHAbVQ12LCYDc4/M4rJjs3XzX2ajgYuPGsK7S/f4UW3MRoV/Xz6Vwcle59LLjh7Ge7/u9s18DAIiQ8xEWE3t+nOnx4f7dbAc7nC0pijazvCcbpV3l+a064PeGwizmLj4qKF8sGz/56EIUBRBVYOdhRuLOHZkao95nsHQ04B5JnBs69//BRajHzAPORRzCNGTTj9k92/etZyWnBV+HUfSZaP213eJO/ryg0pc721EjppN9oOLqVs9H7W5logjjiEse0a7ea3mncvY++ZNSLcTKTVMUUkMuP41v+Vy2OAjcZTuDBQrUV1B5esAwodN01XOF5ZQStPneIUxWn/wdpfK56sLsLlUbjjJ26sspaSgsgmPJhmUFMl50wYTHWrhg+V7qGt2MjQlimuOH+ELlgAXzhxCZkI4n/2WR73NxeQhCZw/fQgLNxVhNCj6syKD4M8njQzYfjijvsUVNEVRXq+fx+1tXDRzCOlxYXz6Wx5VDXbqbS6klCzZXsaq3ZW89+tunr96JlFtiPzFNc18v6GIelsn1LqCoKcBM0lKWQYgpSwTQgSTYbEKIdYCHuAxKeUXPbxvv0PT1p9bvYjaQtC889d+HTABTNHJJJ7wJ79tzopcKn94GcfebVgzjiBhzvVYU4biriuj4JVr/HKTrqpC8p6+kOGPrkRptcKIP/466lZ95q3A79M9NYcQd9SlGMOig48lJoX4OddR/dPrvnsIcwgh6SOZVxqL0+PvWeN0q3y3YS9Xzs6mvM7Ggx+vpb7FhRBevt89Z0/ghHEZHfL7pmcnMz3bv6I9e1Qa7y0NJMkrAv55yZQAxZ7DHbERFt0UhQCGHPAA6WscNSKFo0akcO3LS6hpdrJvDu9wq3gaHcxbksOfTx7lO37JtlKe/GoTHk2ituOa2RE6DJhCiEWAHq+hKyzsTCllqRBiEPCzEGKLlDI3yP2uA64DyMw8PKT8uwKpaSBEwOzKEBrl9fppk1sTigFDSATuujLqVn2Gp6mG8OEziRh57CHTjewN2Ao2kf/MhV45O03FUbaLhvXfMuiW92nasUyn114i3U6aty322eyaY1MZctfXlH/1BC27VmAIiyH++GuJnXFR4A3bIPn0OwgbMoXaX99DczYTNekMoo+cS8mLv+oeb1AEFfV27py3ksYDBB3sqPzfB6u5cvZwThqfQXg7whl6SIwK4e6zxvHEF5tQWpf8ioAHzp/U74IleFMUlx49lHeW7PZPUZgMXHFs+8W0qkY7ry/ayZo9lZiNCidPyOTio4Z2W3O0weaitC6wUcSjSZbtKPcFTKdb5alvNvu1nXYXHQZMKWVQ8UIhRIUQIqV1dpkCVAa5Rmnr//OEEIuB8YBuwJRSvgq8CjBp0qR+kw13VhZQ8sG9tOT85hUpnnAqqRc85A2UQMyUs6n64WXdYoQwhbDr77N8OcC6FR8SkjmWrJve8c22+htKP3nAn8CvqUiXndKP/kbIgDH6VCTNE2BVbEnMYsA1L7V7L1vBRurXfg1Ioiee3lqIgogRRxExwt9WZFBSJNWNgeK+UkJ+RaPu0tmjSd78eSfzluTw4AWTulxcmDE8hY9uT2RLYQ1Gg8KozNhuB4nDAedOG0x0mIUPlu2htmlfimK4X4qiLVocbm56fRkNNheahBYnfPpbHnvKGvjHRZO7NQ6jcoBGXxuYDpCq21mib03SHfT0U/sKuKL17yuAAB9aIUSMEMLS+nc8MAPY3sP7BsCWv57cf5/N1luy2Xn/DGqWvX/QKtCqrYHcf8316mtKDam6qV//LXnPXOgbgzk+k/TLn0Ixh3r9cKzhGEKjGHDDm5S8+1eky+4LIprThq1wI3UrPz0o4+8L2As362/fu5WwYTMQQXiUoV2UuSv/8gnynr6Qmp/foOaXt8h79iJv80AQXH7MMMxtCgJWk4Hzpg+i2ekJulzbp/Lz0CfruuX+aDUZOHJIIuMHxvfrYLkPx49J540bjmX+XSfyxOVTGZYaPEUC8P3GImwu1U8U2OXR2FRQQ0Glvi9VRwizmhiZGUvbWp3ZqHDy+P3pE4tJ6bVY0NNP7jFgjhBiNzCn9d8IISYJIV5vPWYEsFYIsQn4BW8Os8sB09NST/lX/ybn4RPIe+ZCGjcv8u2z791C3rMXY8tbh3Q7cNcWU/bpQ1R9/0IPX17nULfyMy/v8sCKrurGVVWIbc9q36boiacy4vF1ZF79IgOu+w8jHl+HMJjQdAoU0mWnftVnPRqX5nagOtr37ukrBLP/UKxhRI07EWvSED9DO2EOJWr8KVhTghdznBW5FLz4B7beks32O8dT8uH9VP30OtJtB6T3YeWyU7P4bRylObrXGJISxeOXTmFkRgxmo0JiVAjXzhnBJUcNZXRmbLAJix+2FB4a3+7+jB3FdboqQooiyAtiSdEZ3DV3HEnRoYSYDVhM3v/GDIjjvOn7PYOGpUYTaukdynmPriKlrAECev6klGuBa1r/XgHo+0d0Eqq9kT3/PAVPY5VXLR3vMizhhD+RdMotVHzzNNLl75MiXXaqFr5E/PHX9prOYzA4SncGWN0CSKnhrMgjbOgU3zbFEuonK+coy0EGMSQT3exE8jRVUzzvTpp2LAHpdcpMv+zfWNOGd+t63UHsMVdQvejVAK+huKMvRxiMDPrLJ9QsfYf6NV+gmKzEzryE6MnBu3Dc9RXseeLM1gKQRHU7qF3+AegQ56XqpnHzj0E90kekx/DUHwKFo7MSIzhqRArLd5a3SwXqSdHg/1cMSIhgZU5lgL+4lJAa230DurgIK2/eeCwb82uoaLAxLCUqIDWgCME/LjySu99dhUfVAozpuoJ+0elTs/RdPE3Vfnkvb0B8kbhjrsBevI1gyQx3fQWWhO5bAnQG1oxRCPNXAUFTILC0M2PSXHbKP3tYf6fJSuzMi7s8Fqlp5D11Ps7qQh8dx753C7lPnkv2Q0sPmsxa0im34Kkvp37NlwiTGel2ETXxVBJPvRWpelDMVhKOv46E46/r1PWqF7/lJbEfuLTSmZmDt5AmTN3L/d5x5lgmDornw+V7KKpuCfhWaZpkTFb/K9YcapwyIZPPVub7BUyjIkiLDSO7g+V8R1CE6JD/OTg5ivdvO551uVU02d18dU/37tUvAmbTtsUBHR/g1Zi0792CJWEgnvrA1jMptaD+5L0FzeUICObgJVdbUoa1q1LUtPVnggV6S3wmURNO7fJ4WnavxF1fHhBMNLeDut8+IWHOH7t8ze5AGIykX/YvkufehbOyAGNkAlULX2T77aOQqpuQAWNJu+hRQjI6x0O0F2zSLRQB2IWVZaGz2GIdS6i0cZRzGcPGd/29A++P77gx6cwancZjn29g1e5KHG4Vk0FBCLhz7lisfUSKPtSwOT0YFNEnpO+4CCv/vnwqT3+zmfzKJgQwZVgSt542+qD1oZsMClOHdd3M7UD0i4BpiknxNuW2SdxKzYMxIp7EU2+l4MUN/ss/cwixMy7qUKShJ5CaSt6zXjtZP5qMYiB66nmknH1vu18GzWkLKgwSNnhSt75Iruq9ujlRVDeNW346aAFzH4wR8Rgj4sl75kJseet9Qc9esJG8p85j2AM/61ojt4U1Ldvrk9SGZeAyhvFMzJ00GKLxCC/lpygkG9fmJi4/tvvjVoTgnrPHs62ojjV7qgizGpk1KpWEyM6rJfUXFFQ28eRXm3z2thMGxXP76WOJCe9dHYQhKVG8eO1R2F3ewNyezufhin5RroufdVVgPk8xYEnIwpo2nPBhU8m4+gVMsekgFBRLGPGzriblnPv7dFxN25fgLN3lF6jBm3uMmTw3aOFjH8KHz9QVrxDmUB8XsasIyRgZ1EbXUbzNyxM9yHCU7sKWv8HPdhdA87ipWfJOp64RP+sqFKM/B1IYLWzIuJBma5IvWAI4NcEnv+VR3xK4KukKhBCMyozlytnZnD998O8yWDbaXPzl7RXktNoOq5pkfV41d7zzW5/1uIeYjf0yWEI/CZihWWNJu/gxHx1HmKyEZIwi68//9c3CosbMIfsfyxj59A6OeHIryWf+tc+J37a89brdO1J1BdXhPBCmmBQSTvqzV02p9XUIS6jXd31E5z3KpepGtTchpfT6sweZmUq3C9VW3+nr9haclfn6yvaqC0dx5wgT5vhMBt7yIdaMUSAUhNFM9OSz2JN8vC4h2WhQ2FV68F9rf8MPm4oDuKeqJqlpcrCpoOYQjerwRb9Ykqu2BsKGTmH4Y+twle/GEBqFOT6wC0gIgTAfPEFTU3QywhwSUOxRjBaMUZ3LlSSdcjPh2dOpW/ExmttG1ITTiBxzAqITggyay0HpJw9Sv+pzpKZijk0j7eJHsSQOxFm+J/AERUHpYNbbF7CmDNPv7TZaCMnqvCZLaNZYht7zLZrHhVCMCEUh4atNKKIuoPKpaZLosP4lrVfX7KS4toXUmFDiIg7O93hvdbPuA0eTUFZnY7y+vrAPDpeHZTvLqWt2ckSG17unM6mksjobO4rriA6zMDYrDoMiaHa48ajaYf25Hd4BU1MpeOkqmnf+CkLBEBpF+qWPe2dRfQQpJbbctVQvnYensZKIEUcRP+tqFJ1AHD3pdMq/eKxN2UYgDKYuCeeGDZ5E2OBJuvvcdWVUL/kvjqJthAwYQ9wxl2NqDcZFb99C09ZffEtdV3UhBa9cQ+LJt1D53bN+gVyYQog9+pJD0jlkSRpE+PAZNO9cvj99IQSK2Urc0Zd2+XoHvoYzj8xiybZSvx+9IgTxkVaGpfR9b/Ou0nq+WVtIvc3FzOHJzBqV2uXlpqppPPvNFn7eWorZ6FVjnzosib+eObbPl64j0qNZsq00gEYlIKjf+D7kljdy57zf8KgSt6phMiiMzozlwQsmYQxCzpdS8tyCrfy4udjbqYN3iZ4YaWVPaw41NSaMO+eOY+hB+Py6CnE46zGOzoiR78+N88vJCVMIQ+76EmtqdrvnumqKcNUUY00ZhjGiYxpIw/pvqfjmKZyV+QF9zsJkYfAdnxOSMSrgPPveLex9/UbcDRUAmOPSybzm5aAcwK7AXryDvKfORXO7QHUhjObWsczHEBLBrr8dHZAXRChETTqd0KzxVHz9JFLzgJTEzriQlHP+r0+dItuD5nZS8e3T1C37AM1lJzx7OinnPYAlsYMpTCfw89YSnl+wFaQ3+KTHhfPgBZNIjOrbnOO36wr5zw/bcakaUnodCzPiwnj6yuldCnTvLc3ho+W5fkHfbFQ4dWIm15/Qt2pGDpeHa15eQm2z08cvNRsVRqTH8MRlU4OeJ6XkyhcXU1bnn5KymAxcc9xwzjgyS/e8nzYX89yCre3yXMEbRN/+87EBs01NSrYX1dHscHNERoyfL1JXIIRYJ6XUn6W0d97hHDBHJlnlh+e0WdoqBmKmnEP6Zf8CvB8cmsdnAaE5bex9/Qaac1YgjF7+X8z0C0g9/+9Bl7k1S+dR9vkjuuTzfTBGJTL80dW6yw0pJc6KXFp2LUdzOwkdMJbQIZN7TJfI/ffZ2PLWtdkqCB8+k8RTb6HgpSvR7IFtZdaMkQy9ZwFSdeNuqMQYHtslL5z+CJdHJb+yiXCLibS4vvc+b3G6ufCpRQG6kBaTwh/nHOFni9sRzn/yRxp0JMcsJgNf3nVin9Nu6pqdvPnzTlbsqsBkEJw4LoNLjh7abtAvrmnmhteW6XbvDE6K5KXrjtI5C255czk7SzrOLZuNCpcePYwLZuzv2Cmqbuae91bR7HAjhMCjalxx7DDOnTa4nSvpo7sB8/BekusZtWsqzqoCpJTULH6bygXPorbUY4xOInnuPTTvWOoV6fU4fdzN2qXv4KreS9afXg/w1pGaSsVX/243WAKoLfU4irfr8gZd1XvJf/oCNJcdzeNCMZqwZoxi4E3zut1lJDUtSOFI0rJ7JZak55FuHV6iwegTnxAGE+bYvvNo7i5URwuuqnxM0ckYI3pHcNZsNPSYAN0V7Cyu17VKcLo1ft1R3qWAaXPqE/Bdbm/vtY6Ye68iJtzC7WeM5fYunNNet0x71fWOZpb74PJolNTu74DTpOTe91cFCKe8s2Q32anRjD5Iyk+HeZU88I0XRgthw6ZR/cublH/5eKtvi8RTX07Je3dRv+bLwGUq0Lx9MXtfvzFgu9pSh9ZBsGy9s27hAqDozT/jaa71Gm6pbjSnDXvhZqp+eKUT1w12OwEG/eWGMFsxhscQO/Mib4X9gDEqJisJc67v/n37EFJKKr55mh13TSDv6QvYed80Cl+/Aa1NW2t/QKjFiBaEohUR0jUJuBHp+oF+YFLEQbew7Swy4sICBHrBa907Z2xw36BjjkjBbOw47FhNBkZl7ne8ySmtp8nuDogITrfKi99v4+FP1/He0t3UNvftd+mwDpjG8Fj/gKAYUKzhxB19OVULnguYFUq3AzT9oAbQtH0xzoo8v21KSGSn6EeK2ao7u/Q01eAo2RlgpSDdDupWftLhdYPB01StN78GYSBmute8LeXcB0g+405MsWko1nAiRs1i8F+/wBzXOSN7T0s9rpqig8bNrFv5KVWL/oN0O9AczUiPi6Ytiyj9qG/5sn2BBpsLhzvwfTMbFU6f1LVW3OtPGEmI2eCbSSrC221zoADu4QYhBPedM4FQsxFLawC0mg0MTYlq9/WfNWUgydGhvm4poyJQBL4C0L5t0WFmjjlivwVJi8MTNDVRUNXErzvK+XD5Hq5+aQn5PRDz6AiH9ZLcFJNKynl3UPPzG6i2BiJGHkvSaX/BYA1DdQSThBIEazcUBhOOkh1+fj6K0UzssX+gZvFbQZflwmgm85qXdAsmUsdzxoceBKLqn14Pcm3pm0EKRSF+9lXEz76qS9f2tNRT/PatNO9a7vVND4kg7ZLH+tw7vfrHV3Qeck7q13xF6gUP6zIRDkd4VK/Mmx7GD4zrsjDw4ORIXrr2KD5dmUdOaQODEiM4d/pgMnvJYrevMDwtmnduns3ibSXUNDkZlRnLhEHx7WpPhpiNvHjtTBZvK2VdbjWJUVbmjE1n2Y5yvlu/F7tbZdqwJK49foRfi+bw9GhUHa1S2N8s5/JouDwa//pyE/+6fCphXRR77gwO64AJEDfjQuLa2OFKKTGGxQSIzQKY4jNw15boKHp785V6/M3kM+5AKAo1v7yF9LgQlnBCB4wGBKEDxxM782JM0fq8SlNkAubEgThLd/ltF0YLUUeeqXuO6mihbuWnNO/8FXNsOnHHXIYlyT9x3bJruW7HjmIJw1WZjymy+7m/wpevxFa4xXd9j9vO3tdvYPBfvyAkfUS3r9sR9nmLB0KiOpr6TcD8aUtpUMWivVXdk9NLjQ3j5lP6ji7XV4gIMXH6pKwunWM2GjhhbAYnjPWuhKSUKELQYHejCMEvW700pzvOGOsLmmEWE9fNGcGri3bgcmvtyvDlVjRy/pM/MmVYIrefMZYwS+8FzsM+YOpBCEHSmXdS+vEDbbiGVtIu+AcoBgpe/IP/8txgwpoyTJfDKRQDyWf8laRTb0NztqBYIzpFHN+HzCufI/ep85CqG+myo1jCMMdnkHjSnwOOVW0N7HnsNNyNVd6xKwZqV3xI5jUvETlqtu84U1wG9r1baTtblqo7aPDuDBxlu7EXbQ8IxlJ1U/3LG2Rc9u9uX7sjhA4+kqYtPwb0zxvCYnzFHyml9+EjBJaUYQdNmKErKK8LbvblcAdPCfUnSCnZWlTHutwqwixGZo1KIz6ybx5oi7eV8v4yf1fOlTkVPL9gC3ecub+x4bRJWQxJieLrtYU0tLjYsrc2aBHJo0lW767kkU838Ogl3VN010O/DJgAsdMvQJgsVH79FO66UsyJA0k+6x6f1uTgOz6n5L27cJTtRghBxJg5pF/yWLvXFAajz1KiK7CmDWf4w8upX/s17poiQrLGEzn6ON0lfNWP/8FdX75feafVuqH4nTsY8dgaXz414fhradr6s3+fusFEaNY43VlyZ+GuL0cYTAH972gqrsqCbl+3M0iee1cr9crhWwEIk5XUCx5CCIGtYCN7X7setaUBAENYFJnX/ofQrLF9Oi49SCnZureWxdtKMSgKs0enMTzNW5wZlRlcIm/cQfDG7mtoUvLoZ+tZs6eqValJMG9JDveeM6HHaj96+HB5bgA9yeXRWLy9jD+fPAqref/vaHhaDMPTvMWg1xft4Ms1BQFMhX1wq5Ite2uobLD3Gie33wZMgJgj5xJz5FzdfaFZYxl63/eojmaEwYRi6tt2K0NIJHFHXdLhcQ0bv9P3s3HbcVbk+mxmQwdOIP2yf1P60f1oThtSdaNYQomacCpS9XSbgB6SPkL3/sJoISw7UFS3N2FNHsKQe7+j6vsXseWtw5yQScKJNxI2eBKqrYH85y7xCgS3QnPZyH/uYoY/8huGkPa7TnobL36/jR83FXt/yMJrsXDu1IFcfmw24wfFkxBpparR/6FjUATXn3DEQR1nX2D5znJfsARv4AHJY/M38NFf5vS6/Ft9s75IigCaHR6/gLkPxTXN5JY34u7A2MxoUKhucvRawDysq+S9AYM1vM+DZVcQTMFIqiqKxX9f9KTTCcuegRQKSIlma6Bs/qNei9puNhwYI+KJPfrSNuwDI4o1nPhjrgh+Yi/BkjCA9MueYNgDP5F1w1u+ltCG9QuQunlnjYb13/bpmFRNY8Wuct5ZnMMPm4rYUljDD5uKcbhVJN4MgtOt8uHyXLYU1qAIwfNXz2TioHiE8DLABsSH89K1Rx3WfdCdxU+bS3SXukIIRiDNkQAAIABJREFUtuztfXuOUZmxunoxoWYjsRGB72dds5Ob31jOhvxqX8IqWOLGo2oM6MXiWb+ZYUpNo+rHV6j+6TVUWwPWtBGknvcgYUO6Zpp1qBF37JWUvHdX4CxPagHanbbCzV6R4QOWz9Jlx7ZnFS27VxI+bFq3xpByzv8RkjaCqtb3MnLUbBJPuRWpqZR//ST2ws2EpB9B3LFXdEqrsjfgaarSFYmWbgeexsDiXm+hxeHmtrdXUNlgx+5SsZoM3jyqzsxF1SR3v7uKxy6byujMWB69ZApOt4qqyV7zjDkcoATjfrYWZ3obf5iVzbq8Kpzu/SZpFpOBG04aiSIEblVjzZ5KapqcHJEew/KdZbhV/8KPL3AeIJtrMRk4Z8rAXq2W95tPuWz+I9T++p6vyOMo2kr+C5cx+PZPdXu8D1dETTyN4nfvCtgugepFr5J85p2+bS05v+mS5TWnjeZdK7odMIUQxEw7j5hp5/m2OSty2fPEXKTbgfS4aMlZQc3Sdxj0l0+7VDlv3PQDld+/gLu+jNBBE0k67fZ2jc32IXTwZBST1d+aFy//NWxI7yXt2+KdJTmU1ra0Ljs77kTxaJInvtjIOzfNQoi+USc/lHB5VI4+Ipm1eyoDHhpCCEYP6H2Lk4zW2fkHy/awraiW5JhQLpoxhNED4iiuaeaO/67E6d7v6BlqMermLa1mA8NSoiipaSEqzMx50wYza1RqwHE9Qb8ImKqjmdql8wJmINLtoGLBc2T98dVDNLKuw1mei2I0oaltZpiqm8ZNP/gFTEN4DMJoCvAyFyZLr3vzlH70QGtfuvdLKT0upMdF6Yf3MfiOzzt1jbY9+Y0bF9K8fQmD7/oaa/KQds8NGzqF0METacld6ztfmEMIHTSJ0D4MmIu3lfqCZWfR0OKkot5OckzfqfkfbNhdHp77ditLd5SBlJhNBkyaBCF83UZ/O39in1kEp8aGcfsZgcW9f3y6nvoWp99s0qO5UURge6bUJDeeNIqsxIg+GSP0k4Dpri1FKEYkbZZsUuIs2XFoBtVNGEIivApCevvaVOijxp1M6UcPBB4oFKInndGr42rZvRI9wr8tfz1S0zqkWWkel1fq7kBiutTQXA4qvnmKAde81O75QgiybniL2uUfUrfiYwBipl9A7IwL+pRaJIJkv/R+kPugSTCbfl/p/398sp7NhTU+MWGP04PZqDD3yCzS48OZMTyZ8D4ggreH8nobpbWBRnSqJgM+NZPBq7DUl8ES+knANMWkBAkyAksHMm+HG8xx6VjTRmDfu8WPXC/MoQEdO66aooCWSxCkXfhwpyTrugJhDkHaA4nywmgOquB+INx1ZfotllLDntex+jx4O7Hijr6MuKMv69TxvYHjRqfy5ZpCPzdDRXiteKcOTeS/S3LwHDADVYRgaEoUseH9g2TfGZTV2diytybAAtejShrsbq4e17lW296GR9WCPiwTo0OIDbews7gek1HhuNFpB4Wh0C8CpiEkgtgZF1O74sMAonrSKbccwpF1DwOue5X85y/FVVOMUAxIj4u4Y68gcvwpvmPc9eXseeIM8LQNYpLaFR8RM/XcXh1TzPQLqF3yjp9wiTBaiJ5ybqdmeMbwWN3uKgBTbO/mkXoTlx4zjI0FNZTUtuB0q1hMBqxmI3eeOY6EqBByKxpZsasCRQiEgOgwC/eeM/5QD7tXUV5vw6SjvKRJSVF1c5Cz+h5psWFEhpioapNXNhsVThzrlaBTNYkiOGgNDv0iYAKknPt/GMJjfH3lltRsUs//e5+qr/cVTNFJDL3/BxxFW3E3VBKaNTZA5qxy4Us6wdILW946NLezV+lSyWfcgasil+ZdKxAGI1L1EDpoIqmdNJIzhEQQNel0GtZ9E+DemaDT8XS4IMRs5PlrZrIhr5o95Y0kRYcwPTvJpwV5z9kT2FvdTE5pPfERVsZkxfVJpfhQIishQreIYjQIjsiI0Tmjd9Bgc7GjuI6IEJOutYVode68973VqJpX1d1qMpAaG8Y5U73C051Vc6pssPPDxiKqmxxMGNR96+0eCQgLIc4DHgRGAJOllGuDHHcS8CxgAF6XUrbfctOKSZMmybVrAy8ppTwsW+Z6E7v+PgtXG2UlHxQDo57dFaDt2RtwVuTiKNuNJWmQj0TfWWhuJ6Uf3kf9mq+8/kFGM8ln3UtsGy2A/+Hww3MLtrBoczHOVgUmIbw933PGpjE8NYajj0jpVUbA+7/u5oNlezAaFKSURIWa+eclU0iNDRR/rm128OOmEqoa7IzJimN6dlJQCww9rMur4u8fr0PTNNyqxGoy8NU9Jx98xXUhxAhAA/4D3KEXMIUQBiAHmAMUA2uAi6SUHdoFBguY/z8g75mLaMlZobsvfPhMBt783kEeUeehOlpQW+q8JnGHyBLjf+gaNCn5YnU+81cVeM3IPBoIb4ui1WQg1GLk2atm9ErHzLrcKh76ZJ0fhUvgrZS/ccMxvToZUjXJhU8vorGNov0PfzutWwGzR6U+KeUOKeWuDg6bDOyRUuZJKV3Ah4C+jE8buKr3Uvj6DTRu+anbnS39FQkn/Al01NqFyUr6H545BCPqPAzWMMxx6b+rYLmnrIFPf8tj4cYiWpz6qZL+DEUIzp4yiHk3z2bioHhUTfMt0x1ulfoWJy98t7VX7vXlmoIAvqsEapoc5FUEk23sHgoqG3F7Oqfy3hkcjG90GlB0wL+LgSmdOVG1NdC4/luat/5C5MRT+1RJ53BDxBFHk3rO/ZR9/iigIT0erOkjGHjTPIxhfZdX+h/8oUnJv77YyPKd5ahSYlQUXl64jUcunszIjJ5xYWubHXy9ppCdpfUMTIzgzCOzSIo+9NzOlTmVtKWmahLW5lb1Sjqs2aH/wFEUga2XH0Ymg9KunUZX0WHAFEIsAvT64+6TUn7ZiXvovbtBX4IQ4jrgOoCUcG/ORHPZaFj3DXFHX07ogDGduOXvA3FHX0bMtPNwVuRjjIjDFJWoe5yrtpSKb5+ieftSDKHRxB93DTHTzvvd53kPBpZuL2PFrgpf14tH9c5W/v7xOj647fhOFR3cqobN6SEixOQrGJXUtHDzm8twujXcqsbmghoWrN/Lvy6fdsjtZYO9pN76Ns0YnsyesoaATiJNkwztpC+TR9XwqJquMMeByIgPJz7Cqsvn7A46DJhSyuN7eI9i4EAiVzpQ2s79XgVeBRiZaPG9Rul20bx9yf9XARNAMVnbbU10N1ax55+noNobQVPxNFRQ+vHfcJblkNLJCvf/EBw/bCzSbZd0eVR2ltS1O8v0qBqvLdrBgvV7kRLCrSb+eMIIZo1K45UfttPi8Ph+xB5N4nGpPPvtZl64Rt9xsT24VQ27y0OE1dTjB+XMESks2VaK54CpmUERTB+e3CsP4VMnZLJwYxHl9XacbtVXYLrhpJE+64pgcLg8vPD9NhZvLUWTkvS4MG45dXTQz0EIwYPnT+Sv81bicquokh6l9w7GknwNMFQIMRAoAS4ELu7qRYTRhBJE6ac9SCmxF25GczQRkjUeg7XvLVgPJmp+ectrvnYAB1K67NQseYeEE2/o9RbKwxV9xZwIpqwuEG11kAPw0sJtLNpU4ssF1rU4efqbLUSFWthUWKM749lT3ohb1TrdgujyqLy8cDuLNhejaZKYcAs3nTKKKUO7rltpd3lYvbuSEenR7Cypo7bZ6RtLbLiVG0/qHY90q9nIc1fPZNGmIn7LqSQ23MLpkwYwrBOzy4c+Wcfmwlofyb6wqpl731vNS9ceFdReOTMhgvduOY61uVXUNnutNL65t3tj71HAFEKcBTwPJADfCiE2SilPFEKk4qUPnSKl9Agh/gwsxEsrelNKua0794uaeFqXjneU76HgxStQm+tAKEjNQ+p5D/6uaC7NOb8F0bc04yjZSXgfa1y2B09LPeVfPEbDum9ACKInnkby3Lu7JdIcDLtK63nxu63klDYQYjZy6sRMrpiV3Ws9z3PGprOzpD5glqkowicorAe7y8OPm4p1bHhV3l2ag9Vk0PX0NipKl5win/xqEyt2VfjuU9Xo4JHP1vPEZdPaHV9brMur4qGP1/mautwejaxEr2vl2Kw4Lj16WK/SiqwmA6dNyuK0LthblNS2sGVvbUBHktuj8vmqfG46JbgIj9Gg9Ir4cU+r5POllOlSSouUMklKeWLr9lIp5SkHHLdASjlMSjlYSvlIp28gFBRrOIoljMxrXsIU2XnCqdQ08p+7FHdNCZqzBc3RhHTZKf34AW9b4u8EloQsEIEfo1TdmGJSDv6AfPf38P/aO+/wOKqrD793Zpt6lyxZkrvcbVxxAYMx1WB6C6EbSEJCh0DCF5JAGoSWQOi9h47BYIyNbWxw792SbNmy1btW2jpzvz9WlrXaXWklrQpk3+fRY3nL3DOj3TP3nnvO+eU/egHVaz5Et9ej2+qoWv0B+Y9e6LfvZWc4XGnlt2+sYW9RLRJodLpZsL6AxxZsDcnxAWaP6c/4gUnNS0WTQcFsVPn9RRPazAWsaXAGTHAvqbFx9sRsH7lZo6owe0xG0InxNQ0Ovt9T6uOUnS6d91blBXUM8OiiP/i+J83H5vT8uHVJXkkde4tqWbD+IHe89kPQmuLdRXF1o98boSbhYHlod9cD0afzPszJ2WRd/xTROTM6LJDVmL8e3VaHjyaO20nld2+SeeUjIbQ0MJqtHqQe0llVS5Ln3ODp4t6y6YVqJHLgeMypg7plzGCo274Ed02pt3aQ5sJVXUz9zuXEjp3T5WX0B6v342yVMuJw66zaXULlqXaSYrpe760qgj9fNpltB6vYtL+CuCgTs0dnkBDddpVVcqzF77kJICc9jitmDaOw0sra3DIMqoJb0xmVmdChZW95nR2jQfGZcUk8s7FgWZdbhmxjS8Tu0jhcYeXz9QVcMuOYWF9Ng4N3Vuaxel8pkSaV86YM5MyJ2d1WCTUgJdpvRZJRFYwIoO0eavq0w1QiYogZPZv67Uuo+uG/SM1NwvEXEjfx7Ha1xLXGWv9NI6Tehnph6HBWHaHw9Tto3L8RgcDSfwSZ1zze4eqZ9ojIGk32/P9w5O37PBs/Uid61ElkXf1YSMfpKPaivZ7Yait0p43q1R9S9N79uKqLMcSnk3bu3SR2ojZ+f0md35QRk0GhqKohJA4TPBsH4wcmMX5g8A1PjKrClbOG8saKXK+lt8mocvXJORhVhf+7eBLF1Y0cLK8nIzGqw7K6GYmRzd2FWqIIOrQc336osrnCJxAOt86yHUXNDrPB4eLXL66ipsHRvDn03De72Vdcy+3ndM/GbEpsBCeNTmflruLmHXaBZ8Pogqk9Mzno0w4T4Mi7v6d23afNjWUbctdSs2EBA37xYpuzk8ghk5F+arGFKYLY8aejuxxIt8OjEBniO6LUXOx/7CJctWUekTPAdmgH+x+7mOEPrQq5Pk3s2DnE/G0trppiVEt0t81mO4I5ZQCKOcrXaSoG6ncsaY67umuKKXrv/xAIEqZd1KExhvSLI7+k1idn0OnW/ZbY9TQXTx9CUoyFd1bmUWW1k5MRz/xTRjA47djfPz0hkvRO9tWMMhu58PhBfLKuoNkpCzydxi8/oe3+o0fRdJ3lOwMmrXjRsqXdos2F1NucXjvpDpfGkm1HuOLEYSHT0GnNnfPGkZkUzYL1BTQ63UwYmMyNp44M2c2xPfq0w5QuOzVrP/Zq5iCdjTTs+b5diQZDdCKpc2+lbNHTx5arqgnFYKbi21c48u79gMSUlEX/K/7e6e7l/qjfsQytsa5V9x6J7nZSs34BSbOu7PIYtsKdNOSuRY2KJ3b8GZ7qmsT+XT5uqIg97kyKP/qrl0IkQgXdjWzVsk46bZR+/s8OO8xLZgxm2Y4jaC1mcGaDwgkj03vsC9Qes8f0Z/aY7vu7XDt7OGnxkXzwQz61jU5GZyUyf84I+gd5wyiubvRqXxcIi1HlnEkDmv+/taDSr4yHURXkFtd2m8NUFYWfnTCUnwV5Qwg1fdphanYrSN8GE7qzkfqdy9t1cqln/obIQROoWPaaR+7B7UBrrEFrrGl+jbPsAAXPXMfQexeEbLnsrCz06ZIOHsfgKD/QpWNLXefw63dQu2URSB2hGin67wMMuvXtXpGjDYRitDDkt59y5K17sTbVxEcPn4l1zyq/r3dVl3Q4ptk/MYpHr5nOM4t2sudIDZFmlXmTB3LlrPYlMX4qCCGYOzGbuRM7J70cbTEGTp0SYDao6FIya1Q6p4w95vjTEyJRFeHzXl3SbfrlfYE+7TCFono0tFulzQiDCTXI8sDo4TNxlB7Aumel3/Qb8GwEVSx5kcyr/tllmwEissZ4WqS1Gk8xRxE54LgA7wqO2o2fU7d1cfOs++gYB5+7gRF/W+vVGb12y9eUf/Ms7rpyokecSOpZt2IKYW9Kt7Wa6rUf4yw/QOTgScRNmOvVcs6U2J9Bt76F7nKAECgGE3v+cAKuykKfYxkTMjoVGhmWHscT1/Ve6tSPnfgoM+MGJLG1oMJreW02KJw3ZSD9k6IYnZVIVqv46rmTB/LV5kK0FqsoVRH0i48kp5crlbqTPt1nX40IcOGFQsJU/3rk/qhZ/4n3LnJrdA17SfBpGIGQmouq1e9TuvBJUAyen6OoRgxxacQed0aXxqj6/j0foTAA3dGArfBYc4Syxc9R+Npt2A5sxlV5mOrV75P3t7Nw1ZR0afyj2A7vYu8fT6R0wSNUffcmRe/+nty/nuHZbGuFYjSjGEwA9DvvXkSrpiLCFEFaCy2jMD3LfRcex4j+8ZgNClFmA0ZV4eLpg7l+zgjOnJDt4ywB+idF8cdLJ5EUY8ZsUDCqCqOzEvj7lVNDsidQ2+ikttH/BKc36dMzTBSFgb9+jYPP3XhMokJKsq77d4fkX1t/QX1QjUQNntQFQ5vyPp++xqOB0xwzNSBMFhRzFHETz6HfvLuaHUenx/Gz1Ac866ema6Q7Gin78knvm4SuoTmslC1+lv6X/rlLNgAUvnZ7k2gazWO6Kg9TuvBJMi7xo0PURPzkeQCUfPYwrqojGBP7k3buPSRMCaqB1U+GQ+X1fLjmAIUVVkZlJXDh8YNCEnctKKunpKaRIf1iSYkNLo4YG2HisWtncKSqgcp6O4NSY4mJaL/X6qTBKbx92xxKa2xYTGpINNkLK6w8/MlmCsqtSClJjrUQF2lGEXDK2P6cOSGrublzb9C3HSYQNXQqIx/eQEP+RtDdRA6ZjNKeA2xF0glX0BAgdgaeGVDyKfO7ZGf9ruXYCjZ7OynNDYqBIXd+iDltcJeOf5SEaRdjK9zhM2MWqoGIbE86h6M03yN90frNmpuGvf57bHYEd30lzjLfWKzUXNRu+qJNhwkep3nUcf4vsrWgkj+8tx6XW0eXkn3FNSzaXMhT82d2ene/3ubiD++uY39ZPaoicLl1Thnbn9vPGRt0XmT/xKigN4uOIoQImXqm3aVx52s/UG9zNX92S2pslNR4Puv7S+v5dscRHrtmOmo7onzdRZ9ekh9FqEaic6YRPeKEDjtLgNgJc1EiAqjJCYXBd3/c5aqY+p3L0R2+S2UAawic1FESpl1E1OBJKGbPh1QYzAhTBNnz/9Pcf9IQm+I3pQo8ImxdRagGAhVSC9VI5cq32fvHk9h511gKnp2PvTi3y2P+VJBS8uQX23C4NPSma+jWJA0OFy8v3dPp4z72+VZyS2pxuDQaHW5cmiddaMG6ghBZ3v2s3FWM060HTKF3uDUOlNazZl9ZUMerqLPz/OJd/OalVfz9403kFvuGizpKn59hhgIhBBmXPsjhd+6DFtrmwmghccZlRIRAedIQnQiqCVrpjQtFxRAduv6VQjUy8DdvYt2zCuve7zFEJxE/9Xyv1m/G+H5E5Uxrygw4Zo8wRZB48nUUf/I3qn94H6m5iBlzCukX3t+hEIcaGUfEoAk07t/orXxptGBISKf4o4eaZ8D1O5bSsG81w+5fhCm5czu53UllvZ3P1hWwr7iWIWkxnDd1ULelxABY7W7Kan3j6VLC5oKKTh3T5nSzPq/MJz3I4dL4dH0B5x/fexVfHaGs1tZu+aXdpbFxfzkzR7T9eS2pbuTXL63C7nLj1iR5JbWs3lfK/RdN7FRjkqP8KGaYoSB+6gWknX0HijkSxRyFMJqJn3o+/ULUAi1h2sX+tbsVlZgxc0IyxlGEohAzahbpF/yOlNNu8tsnM3v+f4geOQthMHnOOSKW/pf/lYrFz1C57DW0hmp0u5XaTQvJ+8c5nhSuDpB17b8wxqWhWKIRRgvCFEHEgPHYDm5rpU0u0V12j6hbH+NQeT03PruCj9fuZ/OBCj5dX8BNz60gv6TrM5FAmI1KwE2RaHPnNJocLi2gvnqjw588dd8kJyMOi6nt+KRRFSQGESt9bfleGh2u5puIlOBw6fxr4fY+396tTyCEIPX0X5E8+zpPSV5MMmqgZXonMCVlkj3/aQpfu93zgATFZGHAza92uA4+FKgRMQz81cu4rVVoDdWYkrOxH9lDY8EWLylddA3NbqV6zUckn3xN88Ou6mKqVr+Pq+oI0cNneMIaLTasTIkZDH9wJfW7VuCqOkxE9jgkgoKnrkRvnb6lazQeCE6bvCd55utdNDpa9KTUJG5N499f7uBf18/sljFNBpUTR6azcnexVw242ah2eiYYF2kiKcZCSY13SEgRMGVo5xUSe5pJQ1LISoriYLnVb804eKQ0Tg9CJ33zgQq/ZbP1NhdVVofvE0HyP+Mwj6IYLd3WlCJ23GmMfHgTjQc2IwxGIgceh1BU6rZ9Q+nCJ3FVFxE58DjSzr2HiMzuF50HT6jgaE9M25HdfuvrpdOG7eAWwOMwrftWU/DMdZ6yTreT2o2fU/b1Mwy5+xOvfqJCNRA79tjs2VVb5j/XVYhebQQSiO2H/Pek3HukBk2XHWqz1hFuPXsMNQ0OdhRWNeuBnzImg/OnDuzU8YQQ3DFvLA+8twG329Mk12RQiDAZuHZ218NNPYUiBI9ePZ13V+WxdPsR3JqOw62j6xJFCBRFcN8FxwUVMok0G6hp8P0sSul5rrP8zznM7kRqLqTLTtSw45uXXZWr3qH4wwdbxPSWYd23miF3f9xjTvMo5uQBCHz1QYTR3FzlJHWdwldu9VpW645GnOUFVHz7Emlzbwt4fGNcKjFj51C/YymyZazYYCbl9JtDeSohwWJUsWq+S1ajQQko0xAKIkwG/n7l8RRVNVBaa2NASjSJ0V1bhRw3MJlnbjyBT9cVcKSygTHZiZwzeQBxkV1LY+tpLCYD150ygutOGQF4Nsn2l3qaKg/tF9euvK6UkpeX7qGk2ncD1qgqTB+eRkQ7shZtEXaYIUBqLoo//jtVq94GXUONSSL9kj8RN+40Sj75e6sUIIl02ild8E8G3vxqj9oZOXQqhrg0nBWHWmzWCITBRMKMywBwlO33G8+ULge1Gxa06TABsq55gqL3/0jNuk9A6hhiU8i4/C99qmzzKHMnZPPp+gKv5Z/JoHD6+Kwe0UPKSIzCZFBZvrMYp0tj6rBUr8YcHSUzKZrfnBW4ie6PESEEQ/oFXzm0el8pn2846Hc5Pm5AInd0sZNS2GGGgCP//SM1az9qLld015Rw+LXb4erHApRjShoPhq7JbTDoTjsHX7gJZ5V3Z5qIAePIvOqfzct2xWgG6T9+1G4BAJ64beaVD5Nx2YPojkbUqPgeF2NrdLhZvLWQHYeqyEyKYu7EAX6XcVednENhVQMb88ube1KOG5DETacF1lAKJct3FvHYgq1ICbqu887KXM6amM0vTx8VFrDrJJ+vP+h3p91kULjptFFdWo5D2GF2Gc1WT82aD703UvB0Wqpa8UbA9xkTQlfTHQwlnz1MQ+4aaGmnasKSNRpLi7QqU1IWppSBOIr3euVaClMEiScG32VJMZq96srbQmou6ncsw1lxCEvWaKKGTeu0w6i2Ovj1Syux2l04XDpGVfDJ2gL+ceXxjMz0Tu8yGVT+dOlkiqoaOFRhJTMpisykjutGdQar3cVjC7Z6zW41t85Xmws5YWQ6Y7N/3FpMVrsLo6qEVNYiGBqd/rMCDKoS8LmOEHaYncRetJeKpS9hO7zbp13ZUZyVhSRMv5Tq1e97tagTpoh2l7ahxmNDq91BzUnN2o/o/7O/eTmoATc9z/4nLkV3NCB1HalrxIyZQ2LTsj2UuKqLyX/0QrTGWnS3C8VgwJyew+Db3m1Ozu8Ib6zYR02Ds7mLjkuTuDSNRxds5eWbT/b7nozEqG7vn3mowsr73+eRW1zHoLQYBqfG+t1Ucro0vt1+5EfrMHcdrubxBVspboohTstJ445544i2dC5lqqOcODKdA6V1flvPDe3X9T60/zN5mKGkftcK8h45j+q1H2Ev3O4tw3AUIYgYMI6MSx4gYcalnlxFowU1Mp6MS/5E7LjTetRmH2d59HG306dqx5w6kBF/+YHUs2/3pF7pbuq3fs2hl27221yjKxS+cReumlJPo2HNie5oxH54N6ULn2j3ve76Sqx7VuEozW9+7Ie9JX7blZXW2Khp6Hw6SVfILa7llpdWsXR7EQXl9azYWcQbK/a1oUjp/xh3v76aeX//iiueWMKHq/ObK4X6CiU1jfzurbUUVjZ4ZIN1yZrcUu5/Z12P2XDOpGwyEqOaNZgU4cl9vf3ssSGpQQ/PMDuIlJLDb93bdvcjPPG+tLPvQKhG+l/2EOkX3I/WUI0hNqW5hLEnicqZjnXPSh/nGDlkit+Ee1dtKWVfPN5c7inRqNu+hANPXcXQexeExCbdaaMhby1I75iTdDuoXvsx6Rfe7/d9UkqKP3qIqu/eRBjMSM1FRPYYBv7qlYBLQInEaOid+cGzX+/0iqvpEnQ/0hLgycds2XcSPLPTu19f3XyMSquDN1bkUlnv4Ben92ymRVssWF/gI5nh1iQHyurJL6nt0OZNvc3Fpv3lqIpg8pAhXii+AAAgAElEQVQULEHubHskfGeybEcR63LLSIwxc86kAQxICU3OddhhdhB3bRmatcr/k4qKYookYsA40i/4HZb+I449ZbKgmHpPxTHj0j+T98h5yCZpDmEwIQwm+l/uX8Sz8rs30VvXo2su7MW52A5tJyJ7bJdtkrrum+N0lDaUJatXv0/VqneQbmfzplpjwVYKX7uDeZPu580V+7yWZKoiGD8gie/3lPD15sMAnDEhkzljM7st17Ile4/UBHzOqAqEEM15n3MnZTOm1XL8vVV5fuV6v9h4kJ/PGtbmcrfR4WZrQSWK4kk96s6Y4qEKq1dPzaOoiqC0xha0w/xm62H+/eX25r+NlPB/F09kylDfijZ/mAwqp43PZFBqDC4ttHIlYYfZQRRzZMBdZHPKQHL++G0PW+SL1HXqdy6jdstXKOYoEqdfQkTWGIb/aRmVK9/GdnAblsxRJM26EmOc/7paR3Gu31CDUBSclYUhcZiqJYqI7LGepPmWM1/VSNzEuQHfV7H0Zd8ZvubCumcl512dxJ6iVNbllqEqAgmkNbU5+89Xx2Z6eSW1fL+nhD9dOhkhBFJKvt5SyLur8qlucDC0Xyw3njrSZ6OoM0RbjNT46e1oMaq8fPNJfL+nBLtL5/hhqQxM9Z0J5RbX+l1+G1SF4upGhgVo2LtiZxGPfb4NtSk+LZHce/5xHCjzhAUsRgPnTB7AnHH9u6T0aLW7WLr9MPWNLgyK8HGabk1nUJDpUsXVjfz7y+0+N4iHPtzE27fNCartXH5JLQ+8t4EGhwuBQAj47fnHhUSXPOwwO4gaEUP06NlYdy33aWyRNOeGXrTMg9R1Dr3wC6x7V3mW00Kh+vv3SDvvt6ScMj/ozabIwZOw7lnltVkFnh1tS//Qpd1kXfMY+Y9e6BGlc9pQzFEYYlNIm3dPwPe0lBjxQijgbOAPF0+isMJKbnEtafERSCm5/531Xstiu0tjy4FKdh2uZnRWIu//kM/bK/OaxcR2FlZz71treeya6QEdUrCcf/xA3l2V56XMaDYozJs8gOTYCM5rR/FwQEo0hRVWn8m4y60HrHopq7XxaKtdeIA/v78RgypwNdVYF3xVz/ZDVdw5r3P5iYfK67njtdW4NN1LHfMoZoMnWTxYobdvtx9B9zNLFXji02e0KIusstrZvL8Cs1Fl8tBULEYVp1vj3rfWUm/zvtn/9aNNzJs8AFVRmJYT3EzVH2GH2Qmyrn6UguduwHZwW7OERuL0S0mc+bMet8VtrcJdW4YpZSCKyUL9zmXHnCWA1JEuO6WfPkzClPMwxCQHddzEE66gYulLaG5Xc4xRGC3EjDklpGWO5rQhDH/oe2o3LMBRup+IAeOIPe7MNhstx4w6meq1H/os29XIWIzxnrBHVnJ0c6fwd1bm+uiXAzjdGlsLKhmWHsc7LZxl8/MujTeW7+Ohn03p0jleOmMo5XV2Fm85jMngKYWcNSo96LLFy2cOZV1umVeYwWxQmDUqPWAlz7IdR/zOSiU0O0vw3DiW7TjCZTOHdLgXJsDjn2+jwe7yrR5rKimTeGQw7C6teSOmLWwut99lvS6l1w3vw9X5vLZsHwZFNGd4PHj5ZGobnWh+4sNOt87Haw4g8cRaO0uXHKYQ4hLgT8BIYKqUckOA1xUA9YAGuKWUk7sybm+jRsYx5M4PcJTux1l1hIjMkUE7olChO+0cfvNu6rZ+7XHaUift7NuxF+f67cspNRfli58l/aI/BHV8Q1Q8Q+/7gtLPHqZ+5zKEKZLEE39O6um/DPWpoFqiSTzhiqBfn3bOHdRt/wbd3uDJf1VUTzz2in/43cCKizRhNKg+DtFoUImLNFFVHyCDAM/SvauoiuDWuWO59uThFFU30C8+skPdyYemx/Hg5VN46qsdHKlqwGxQmTspm/mnjAj4ngaHOyg1SPDUcO8qrO6ww7Q73ewrrvUbhj7qq51unS83HSK3uJbHrpnebn7t9Jy0gMnnkwd7vmP7imp4Y/k+XJpOy5c98N4GrjtluI/scrNNR+1up4VcW3R1hrkDuBB4PojXzpZSdq7hXx/FnDY4ZJ3UO8qRd+6jbttir42P0oVPEjlkqmdp2jrOKnUqV7yBMTGT5NnXBTWGKTGDrOv+FWrTg0JKSd3WxVQuexm3tYbY8aeTcuqNqJFxGBPSyfnDN1Qsf52GfasxpQwg+ZQbiMj0HyqYNSqDF5fs9nlcEXDS6AwMqhIwRSc1SJmHYIiNNBHbydru4wYl8/LNJ+N0axhUpd2Y49ShqXy2riAo5yAEJEZ3XF5CCXLDzOnWyS+pY8+RmoAx4TX7Snnl2z0UVTVgMqgYVQWXpiOaxjEoguufWUFyjIX+iZFenZ5an0tX2re1R5ccppRyNxAu4+phNHsDtZsW+pRdSqcNV2UhwmDyiT2CJ+ey9PNHSTzhiqCrcEKB7rRRs/4zGvdvxJQ6iMQZl7Y7Iy/74nHKl76EbBJ8qyg/QM26Txh2/yLUiBgMMcn0m3dXUOPHRBj56xVTefD9jTialuZmg8oDl05q3mE+e2I2n2886DMrKyivo6S6MWQyDF0l2FzC0VkJTB+exuq9pc1O02zwOKGWK14hIMps5LhBHV8hmQwqEwcls+lARcCc0qNIPE00/DnM7/cU8/AnW5pDDi7NjVERjB/gyRbYdbiGRqfnHCrq7VRZ7X5rxSWSaIuR08dnsWTb4S7NJAPRUzFMCSwWQkjgeSnlCz00bp9HSont4FZsB7dhTOxPzKiT2s3T1BprPbNIP+i2etLOvYeSjx4KNCCuqsOY04Z01fSgcFuryPvHPDRrFbqzEWG0UP71fxh8+3sBd9rd1irKv3nO64Yg3U7c9RVUrXqHlNN+0WE7Rmcl8s4dpzYvsYf2i/NKKbp+zgi+2HjQ531Ot86b3+3jnvO6Jo/sj5W7inh12V5Ka2ykxUdy/SnDOWFkaFLPhBDce/5xrM0tY9mOI6iKwunjM3FrOo98thWHW0PqkvQEj/pjZ9Or7jx3HHe+tpqaBgeaLtF0iS6lj4KJIkTAJf/LS/f4VOa4dElprQ1d4jObDOSbNV0ycXAKJ4/O4LhBSXy58RB2l5t9RbV+46KdoV2HKYRYAvjrB3+/lPKzIMeZKaUsEkKkAt8IIfZIKb8LMN5NwE0A2dl9T9IglOguBwefvZ6G/RtBSoSiokbGMfiuDzEl9g/4PmN8Goo5Eq31LFIoRA6bSsqcG6jdtBCbn6a9UnP1aLy19PPHcNWWNqcoSZcd6fJU+OT832K/77Ed3OZJSG89g3bZqd+5vFMOEzyxxOEZ8X6fq6izoyoKLs17VqJLj2hZqFnR1HjjqKM4UtXAI59uRZeSWaNC02dACMG0nDSfdJp37ziVQ+X1mI1ql3MUE6MtvHzzyWzaX05JTSP94iP5xydbvDaCVEWQHGNh3MAkv8co8tOKDTwCaG25cbNBweHWUYSnddv1c0Y0b4KdODKdE5tuPuvzynjow00I6HJ1VLsOU0p5apdG8ByjqOnfMiHEJ8BUwK/DbJp9vgAwefLkvlX7FWLKl7xAQ/6G5uWzBHSXncJXb2PIXR8GfJ9QVNIv/iNH3rnvWD6iUFHMEfSbdzcAaWffzsEXfuGVryiMZmLHn44a2bU0mY5Qt2WR33xOZ+l+3NZqv3pHhthk/4nrQumyWF0gYiJMAZeVCZ2I77WHv1mVw63xytK9IXOYgVAVEXReZLDHa5lU/sR1M3ji823sOVKNEIKpw1K5/ezA6pXJMRbK63xDSInRZoyqQqkfDaTUuAhuOWsMq/YUE2kycNr4LIYEqBWfMjSVt2+bww97S7A73Uwemkrm7zt3rt2+JBdCRAGKlLK+6ffTgQe7e9xQIqWk6of/UrHkeTRrNVE50+l33m+7nF5T/cN7vrFGXcNWsAV3Qw2GKN/ZkO5oxHZkN5GDJjDwV69Q/vV/cFYWEjlkCqln3Yo5ZQAAMaNOIuOSP1H88V+Rmht0jdjjziTz5490yeaOItpIDwoUerBkjsaYlOmpEW8psmYwBb1h1VFiIoxMy0llzb4yH+mIy2aENnwhpfQrhAZQUut/tvVjIjs5mieum4HTraEI0W7T3ytn5fDM1zu9shjMRpWrTsoh0qTy+OfbvFOqjArXnTKcqcNSmTosuJzKmAijVw5nZ+lqWtEFwFNACrBQCLFFSnmGECIDeElKORdIAz5p2hgyAO9IKRcFc3ytoQZnVRGmxJ5thdaaks8epnL5a82ztboti7DuWcmw3y/qkmyt9NPtG/BE4nXf5yqWvUrJZw8jFIMngTxzJAN/+VLAJXbizMtJmHYRrupi1Kh41IjQzSqCJWHm5ZR//Yz3jUFRiRw6NaCmkhCCQbe8xcHnb8RetNfjWIWg/8/+FpIKo0Dcde54Hv50CxvyyjGoAinhylnDQhZXPIoQgqQYCxX1vrOq5Jie13/qLoLdoDpzQhaarvP68n3U21xEWwxcdVIOZ03wNHIWQvDqt3sprW0kNS6Ca2cPZ/aYwCGr7kR05xZ8VxmdFiH/e1kWyXNuoN95v+0VG7TGWnbfN8Wn3yWKgYTpl5D58390+thFHzxI1co3fWJ15vRh5Pxhiddj1j2rKHjuBu+SQNVAZPY4htzzSadt6G50t5ODz91AQ56nY40QCobYZAbf8QHG+PZL1ZwVh9AaazFnDG8zmT2UVFsdVFkd9E+KCirZujN8vaWQ/yzynVXdctYYThvfde34UKPpkg9W5/Pp2gIaHS5GZSXyi9NGdmhpX1zdyKvL9rD1QCWxkSYunjaI04871t1eSonDrWM2BFbWDBVCiI2dyQfv2w4z1Szfu7gfiimS7JueJ2bUrB63oWH/RgqevgbdXu/nWUHCjEvJuOwhFKMZZ2UhUtcxJWcH9QfXGmvJe+R83LUl6A7PDrJQDQy+479EZHlLDRx4+mqsu1b4WmC0kPOHb/qk5ndLbIU7sB3agSkpk6icGf4liQPgrDhE1ffv4qopJWbUScROOKvHnGd38tXmQ7yxfB9VVgeJ0WauOXk4Z07o+rKxO3jii20s237Ea2kcYVJ57qZZQaVcldfZ+MVz32Fzupt3uS1GlfOmDOT6OYET8LuLzjrMH0VppO5spGrV273iME2J/QPITABIatZ/hmarx1G0F2fVEcAjBpZ9wzPtLh/VyDiG3b+Iui1f0ZC/AVPKQBKOv8jvRoi7rtzvMYRqwF1f2ecdZkTWGJ+bQDDU7fiWQy/d7AlfaC5qt3yFeckLDLnrQxRT6JLKe4OzJmRz1oRsNF1H7cANpKepaXCwdNsRn/Qep1vng9X53DK3/TDJBz/sx+HSvFKC7C6NT9Yd4NKZQzwNShocLN9ZRE2Dk/EDk8hJj2PZjiIOltczND2Ok0alB93mrbv4UThMwNNgthcwxvcjeuSJWHev9F2W40l1qdv8pddjzopD7H/yckb85Yd2d6QVo5n4KecTP+X8Nl8XM3o2jpI831QbXfdqI/dTQmpuDr92u1cYQjoacZTkUbnyLVLm3NiL1oWOvuQstxyo4L3v8yirtTMmO5ErTvTUwZuakt5boumSvUXBlY7uKKzymwtpUBUOltej6ZI/vLseXUpP3ffaA2iajqoIHG4di1Hl9WV7+ff8E0iO7b04b9/5S7WBMEUSP/m8Xhs/+/qn2mw35g+pa9RsCE2jXYDkOTegRiV47ToLUwTpF/zuRz/TCoTt8C6kn/Qi6bJTuz501zaMh8VbD/PAfzew+UAlR6oa+GZrITe/sBJVCJx+ShEVIfy2o/NHRkKU35xKt6aTFG3hLx9uwu7SmrsrOVwabl02hwDsLo3qBgf//GwL6/PKqPPTLq8n6PMzTGGOJCJrDPFT256BdSeKOZKsa5/E3ViHdcfSoN4jnTZcNSUhs8EQnciw+7+m4tuXqN+5HGNcKslzbiR6+IyQjdHXUIwWvw4T+MneJHoLTdd5frH3JpQuweZ088XGg0wblsra3DKvdnFGg8Il04PrpXDpjMGsyyv1anFnVBXGD0yizub0202qNbqELQWV7Cuuxa3pXDZzKFfOGtaBs+w6fdphqlHxZF39OLHjTusVWYfWpJ9/H/m5q9GdtmPtWAwmBPgslRVzFFFDQtuUyRCdQL9z76HfuYF7RfZ1pK5jP7wTqetEZI9BKIF3oc3pwzDGpeEsP0jL1uzCFEnirKt6wNqeQdMl32wtZOGmQ2ia5JSx/Tl3yoCQaNAES2mNzW93I13CtoOVvPLrk3nxm90s2lKIS9PJTo7mlrljg5Z+yMmI5/cXTuRfC7djtbuQEmaOSOP2c8ZxuLIhYON9fzQ6PCl37/+Qz9B+sW02Btal5P3v8/lo7X6sNheD02K5+czRHRjNm973Qm1gSsoibsJZvW1GM5aMHIbc8xmlnz9KY8EWjAnppJx5C1XLX/Wq2BFGC+b0HKJHntTLFvctGgu2cPD5G9Htnni0MJrJvuEZonOm+329EIIBv3qZA09e3nST8ihYJhx/IXGTzulJ07uVf3y8mXV5Zc3NIg5XWlm1u5jHrp3RIxIa4OmkFKjSKTHagsmg8uuzxvCrM0fj1vROOfNpOWlMHZZKtdVBpNlARNMGzpB+sUSZDdidHWuW4XBpfLquoE2H+eI3u1m46VDzzDmvpI7fvd15UbY+7TB7Eykl9iO70axVRGSPbd68sWTkMOAX3r1DYkbNonLFG1T/8F/QNeKnXUTy7PkdSp35qaPZrRz498/R7dZjDzoaOPjM9Qx/aBWGGP91xpZ+Qxnx1zVY96zEXVdB5NCpzdVMPwXyS2pZm1fmtRR2uHUOlNWzNreUGcP9tXEIPdEWI9Nz0lizr9QrXmk2qlw+81ilkyJEl2a+SlPSfuvHHrhkMr97ey26LnE3bfYoikc6xK3jI652lDpb4Fhmg8PFFxsP+nSdd/ZiP8yfJK6aEg48fTXOikKEoiI1J6lzbyP1jF/7fb1iMJEy5wZS+oBERV+lbssiv1pIUurUbFjQZsmjUA3EjJ7dneb1GjsKq/32b7S7NLYVVPaYwwS469xxPPLpFta1qHS66qRhzBjR/TaM6B/P27fN4fs9JdQ0Ohg/IImh6XFsPlDBwbJ6Xlu216f23qQqzGzj+pTX2v3O0LuSeR52mH4oePZ6HCV5oGvNF7fsq6eIyBxNzOiTe9O0Po3utOGsOoIxLs2n7NFdX+mrQolnx9tdH/puQN2NLiWbD1RQWGElKzmaCYOSOyUklhBl9vulNhkUEnu4TNJiMvDApZOpaXBQbXWQkRjVrSqTrYk0G3yqnCYNTmHS4BRiI038e+F2nJqOlJ5ORYkxFs6fOjDg8VLiLO326ewoYYfZCkfZARwl+T7dcqTTRsW3L4cdph+klJQtfJLyJc8jhILU3SRMu4SMS//cvFkXlTMdoXpq4FuimCOJzpnWG2Z3mnqbi7tfX01pbSNuTWJQBSmxETx27XRiIzpWgTQtJxWjqmDD+/OmCMGp43qnXjo+ytwhCY2e4NRxmWQnR/PZ+gIq6uxMHZbKWROyiTQHdmFRZiNnTchm0eZDPrPTzhIOsrVCa6gJuCPvDqRH/hPEXV+Js6ooqHb/VSvfpnzJ80inDd3RgHQ5qF77ESULjnVGihwwjpjRsxGmY2V0whRB5OBJRA2f2S3n0F08t3gnhyut2JwaLk3H5tQoqmrg2UU7O3wsk0Hln1dPJz0hErNRxWJUSYg285efTSEx+qfTiCMU5GTEc895x/HwVdO4aNrgNp3lUX5x+ihGZyW2+7pgCc8wW2HJHIlPu2iaekmOO60XLOo6mt2Kq7oYY0IGqqXthrGu6mIOvXILtoNbQQiMcWlkXvNEmylS5Yuf8dEJl04bVSvepN959zVvfmXPf5qa9Z9R9f27oGskTL+EhGkX/+gkTr7bVeyrva1LVu4u4d4LOn68gakxvPrrkymsbMCtaQxMjQ16ea9LSVFVAxajoVcrYHoSm9ONqgS3+aQqAqMaus9X2GG2QjFaSL/kTxS9/4AnTUhKhNGCITa523oxdhdS1yj++K9UrXzL0xJO10g6+Vr6nXev3x18qevsf+IynFWHm0MSzopDFDx1FTl/WoYx3n+APdDMW3c2cuiVW0g98zdEZI5EKCoJx19IwvEXhu4kewF/utlAkzSD7NQNYH9pPf9ZtINdh6uxGFXOnJDN9acMb9MpbNxfzj8/3YrN6UbTJUP6xfJ/F08kpYvCbevzynhzxT5KamwMTovh2tkjGNHff6f6niS3uJbHP99KQZkVRfEoTN529jhiIoxtvs9fc+LOEl6S+yFxxqUMvu0d4iadQ+TQqaSdfTvDfv9Vj3YqDwVli56matU7SJejaalsp3LF65QvfdHv6xtyV+Our/CN3+puz6wwAG01GanbtJD8Ry/Auvf7zp1EH2TKsFRa79MoAiYPSemUsyyrtXHX6z+ws7AaKcHm1Fi48SB/+2hzwPcUVTXw5/c3Ut3gwO7yhAb2FdVy75truqSauHxnEQ99sJG9RbXUNjrZfKCS3765mp2FvRuOKq+zcc8bq9lfWo8uJW5NsnpvKb9/e22b51tS00hhZej6UPzPOUzN3kDld29x+M17KF/6Iu6GGr+vixw0kezrn2bInR+QcvqveqX5blepWPqS36Vy5RL/GnSuADFL6XbiKMmnZv1nVCx/DXvRPq/n0y/8P4QpwtP42PfdSKeNI+/e3+nzCBXf7ynmuqeXceZfFnLFk0v4ctOhTjmXX585mvgoc3OvTItRJS7SzG/O6ng3JoBP1h7A1TpX0K2zcX85xQH0br7YeBDNRxxMUmV1sLOwulN2SCl5fvEuX+kMl87LS/d06pihYuHGgz65mG5dcqjCSl5JXcD37TlcgyG8JO8crppS8h6eh2arRx5VMPzqKQbf9RGW9J6tSe1upJToNv8fJHfDsS+UvWgv1n2rMUQnYe4/0m+upDCYqdv2DfU7vvW0WROCuMnzyLzynwghiBw4niF3f0LZwiep2/o1/jLdnGUFuGrLMMYFJynQVRwujXqbi4RoE6qisDa31EvKtbLewXOLd+HWdM6dMrBDx06JjeDV38xmxc4i9pfWMTgttkOtx1yaToPdRUyECVUR5JX4VzU0qgqHK62k++k3WVJjC6iE6K+TezA0Ot3UBmhqkd+GU+oJDpZbcfkp3RTCM9selu5/9ZcYE9rd/v8ph1n80UNeS07psqO5HBx5+z6G3P1RL1sXWoQQmNOH4SjO9XnOkjkKqescfuMuapta0wlF9UhHDJ5E44FNx2amTXIYSN3LDdZuXEjMqJOJbypRtGQMJypnOnXbv/EvYIYk/9ELGfa7hd0a2vA0kdjNV5sPAR798flzhvPZ+oN+Zk4ab67Yx7zJAzq8lLYY1Q5rxOhS8sayvXyyrgBNSswGlWtn5zAsPY5dhdU+DtCl6WQlRfs91sRBSWzML/fR3tZ02el4o8VowGRQsPkpUWxdndPTjMpMYGN+uc/fUNNlm13fx2QnEh9pxuGydVkxEv7HluT1O77182WWNBZsRnf59rr8sZNx6Z8RRgs0N9YSCFMEGRc/QO3GBdRtWdQke2tHdzSg2+qwl+STNu9uTKmDMSZkEDfxbBST75dFOhupWnUsrnn4rd9S8tk/AjhLD+7aUsoXPxfis/Tm+cW7WbT5EE63jtOtU2938ezi3RRWWP2+vsHh9nE6AHnFtby0ZDfPL97FniOdW+K25q3vcvl4XYEn5ujWsdpdvLhkD2nxkRgN3l9Fk0Fh0pCUgN3MTx2XSUK02Wu5aTaqnDKmP/3i2++AfpRGh5v3VuXx6xdX8ru31zJ5SIpPsrrZqPLzE4d24Ez9o+k6H67ez7VPL+OKJ5bw9Fc7qGkI7nt3xoQsLCaDV+z46DXKTvZ/UwFPPuvDVx3PkH4xmAwKFqNKfFTnu/X/T80whRpgN02In2Tdd/TwmQy+47+Uffkv7EX7sPQfSdrZtxGRPZb9T1yG7vSNj0l7HdE505rLPBvyN1AfoKWd1DzLN2flYWo3LPDbYNnr9W4ntVu+ot/593bxzPzjdGt81eQsW+JwaR6H5GcJG2U2+Oj2vLMyl/dW5eFqqipZuOkQZ0/K5henjeq0bZou+XjNfq+a8aO2fb6+gMevncEzi3ays7AKi9HAWROzuHb28IDHs5gMPDX/BN7/IZ9Ve4qJMBo4b+rADukB2V0at768itJaW/M1MxsUBqXFcqCsHqTEoCpcdVIOc8Z1XWfoHx9vYW1uafMs8atNh1izr5QXf3VScyOOQMRGmHj6hhN4eeke1ueVYTaqzJ2YzeUntO/I0+IjefqGEymtacTu0shMiub9uzp3Dv9TDjN+2sVUffem9xdbNRI7dk5gZ/ojJ3LgcQy8+VWfx1tX3BxD8VKzjBw4Hvy0YBOmCBKOvwjw6PUIg7FdhwmgWgLPBrpKvS3QOYFRUVAMtJJrVbly1jCv5XhRVQPvrsrzcroOl2fXes6Y/gwNECtrD7vT7bOxc5SKejuD02J59Br/XZsCERNhZP6cEczvpCbOkq2FlNXZvM/VrZNfUsdLN8/CqKrERZralckNhkMVVi9nCZ5Nm3qbi6XbDnPO5IHtHiM1LoLfXTih0zakdWDmHYif3rSqDfqdezcRA8Z5qk2MZo8j0DVsh3ZQsexVpB6a8qlASCmx7lnFoVdu5dBLN1O37ZtuHzMQ8VMv9Oxst0IYDF7aO0I1kn39UwhTRHO3d8UcSeSgiSRM8zhMU2J/COI8hCmCpNnXh+gMfImPMmMOkLc4KiuBey84jvSESASQFG3mF6eN8tnwWZtb5vf9LrfO6n2lnbYt0mwgJkDZ5KDU3snAWJdX7tXQ9yhGg0J+ST1JMZaQOEuA3KIaFD8183aXxtaDP54Kuv+pGaZiimDwnR9Qv3O5R1hLOkFKXFWHKf707zQW7iD76se6bfziDx+k6vv3kE1L4fody4gZO4es65/q8TbLiwUAAArZSURBVGqXhBmXUrNhAfbC7R7FSoMJFJWs65/2KQ2NGXUSw/+0guq1H+GuryBm5CyiR85qDmNYssZgShuMvWgveM1cBcJk8XR8crtImH4J8VO9S2GcFYeoWP46jpJcooZMIfHEK/2KwAWDqgjmzxnOs4t3NTsCAZiMKtfNHs7Q9DhmjmhbY9ygKn6lFBQhupSeIoTgxtNG8K8vduBo0V3cbFC44dTe0WRKjrWgCOGzGSKl7FKczx+pAWZ3RlUhM7Ht6rNAlNXaKKpuICspusc2pfq0w3SU7mfnnaMxxqWRevbtxE8+t8vHFELQuH8D6G7vEkiXg9o1H1LQWEv29U+FXALBXpJH1aq3kS02l3RnI/Xbl9KYv4GooVNCOl57KAYTg29/l/qdy7Hu/R5DTDIJx18UUCvcEJdK7LhT0Z12IjJHecV8hRAMuuUtCl+/nYY934NQMMSm0P+qf6IYzbhrS4kcOAFjgrezashfz4GnrmpWhGzIXUPFslcYet8XnllrJzhr4gDioyy89d0+yuvs5GTEcd3s4QzpF9xS+oQR/Xjhm10+jyuK4OTRXWuGMWdsJlFmY3MVzaDUGK6fM4JRmZ27QXSVeZMGsGTrYRzuY98DRUBcpCnkNo3JSiA5xkJRVQMts4NURTB3UscUT51ujUc+3cLa3DKMqoLTrXPS6HTunDeu2wXlfhS65HBM8CvppGu6fNz8xy+hMc9/12WhGomdcCbZ1z/d5XFaUrHsVUo++ZsfyV5Byum/pN/594V0vFDiKN1PwbPzcdUUI4SCUA1kXvMEsWPn+LxWs9WhO2wY4lLbnTXv+/MpOErzvR8UKnGT55F93b9CeQodYvnOIh5bsLW5nluXkl+dMYq5E386jYuPsmJnEU8u3A4SNCnpFx/Bg5dNCUprvKNUWe08/MkWdhRWIRCkxFq4+7zxHW6O8eyinXzZanPPbFC4ZMYQrjopJ6hj9IouuRDin8A8wAnkA9dJKX1KZ4QQZwL/AlTgJSnlPzo6lnTaKFnwKIkn/LzL+j7mtCE07t/oNwVGai7qtnyN1lgb0nxBxRIFigHPpWqBakDpw1VEUnOz/4nLcNeXg5TNuZiHXv41w+7/2qf7uRoRG1RVlNZYi7PikL8Bse5c3nXDu8DJozOYOCiZNbmlSAlTh6aSEN232p2FipNGZzBjRD/yS+qINBvaTNHpKonRFh6+ahr1NhdOt0ZitLnDoSgpJV9tKfTNhHDrLFhfELTD7Cxdnb9+A4yRUo4D9gG/a/0CIYQK/Ac4CxgF/EwI0an8DOl24rZ2vdls8inzveRqWyNUQ8ib2saNP8NvbEwoKvFTek9CuD2se1Z50o9ax7k0V5v15e0hDIEdkGIO/eymo8RGmjh9fBZnHJf1k3WWRzGqCiP6x3ers2xJTISRpBhLp+L2ugwsMdHodPt9PJR0yWFKKRdLKY9auQbwl6w1FciTUu6XUjqB94DOeQghUCO73jXFkj6Mgb962as3o/c4KsakruedtUSNjGPAL19EsUQ3/whTBJnXPNHpeF1P4K6v9FsuiebukoywYrIQM/ZUaJXOJYwWkk66utPHDfPTRlUEg/v5X8H0RCw4lBHS64Gv/DzeHyhs8f/DTY91CGGKIGnWVSjG0Nzto4fPZOjvvkQxR4FosYFhiqDfBfehtDED7fSYI05g5MMbyZ7/NFnX/otRD28ifuLckI8TSqKGTkFqvnd0YYokZlTXVDEzr3yYiKwxCFMEiiUGYTATO/4Mkufc2KXjhvlpc+vcMViMKkeTFlRFEGFS+dUZnZfPDZZ2N32EEEsAf40Q75dSftb0mvuBycCFstUBhRCXAGdIKW9o+v9VwFQp5S0BxrsJuKnpv2OAHcGfTreRDFT0thFNhG3xT9gW/4Rt8c9wKWVwouotaHf3REp5alvPCyGuAc4B5rR2lk0cBlp2KcgEitoY7wXghaZjb+jMTlao6St2QNiWQIRt8U/YFv8IITZ05n1dWpI37X7fC5wrpfTfuA/WA8OEEIOEECbgcmBBV8YNEyZMmN6gqzHMp4EY4BshxBYhxHMAQogMIcSXAE2bQr8BvgZ2A+9LKTuuFhUmTJgwvUyXEhqllH5bhUgpi4C5Lf7/JfBlJ4bw3xq85+krdkDYlkCEbfFP2Bb/dMqWPl3pEyZMmDB9if+pbkVhwoQJ0xX6lMMUQvxTCLFHCLFNCPGJEMJvlroQ4kwhxF4hRJ4QIuRF2EKIS4QQO4UQuhAi4K6eEKJACLG9KX7bqV23ENrSrdekaYxEIcQ3Qojcpn/9ZgoLIbSma7JFCBHSDb72zlMIYRZC/Lfp+bVCiIGhHL+DtlwrhChvcS1u6CY7XhFClAkh/KbgCQ//brJzmxBiYnfYEaQtJwshaltckwe6yY4sIcQyIcTupu/PbX5e0/HrIpu0lPvCD3A6YGj6/WHgYT+vUfHUrQ8GTMBWYFSI7RgJDAeWA5PbeF0BkNzN16RdW3rimjSN8whwX9Pv9/n7+zQ9Z+2ma9HueQI3A881/X458N9etOVa4Onu/Hw0jTMLmAjsCPD8XDxFJQKYBqztRVtOBr7ogWuSDkxs+j0GT+l2679Ph69Ln5phyp4utQxsx24p5d5QHrOzBGlLt1+TJs4DXm/6/XXg/G4Yoy2COc+WNn4IzBHd02y0p655u0gpvwPa6sJ7HvCG9LAGiBdCtN0YtPts6RGklMVSyk1Nv9fjydBpXWHY4evSpxxmK7q11DJESGCxEGJjU4VSb9FT1yRNSlkMng8kEEgz1yKE2CCEWCOECKVTDeY8m1/TdPOtBZJCaENHbAG4qGm596EQomMyk6GjL31nAKYLIbYKIb4SQnR7PWNTWGYCsLbVUx2+Lj3eQLgDpZZu4G1/h/DzWIe3+oOxIwhmSimLhBCpeHJR9zTdYXvalpBck/Zs6cBhspuuy2DgWyHEdillfrvvCsI8P4+1Ps+QXYsQ2PI58K6U0iGE+CWeme8p3WBLe/TUNQmGTcAAKaVVCDEX+BQY1l2DCSGigY+A26WUrcXVO3xdetxhyh4uteysHUEeo6jp3zIhxCd4lmkddpghsCUk16Q9W4QQpUKIdCllcdPSxa8ATovrsl8IsRzP3T0UDjOY8zz6msNCCAMQR/csEdu1RUrZskfgi3ji8r1ByD4fXaWl05JSfimEeEYIkSylDHmNuRDCiMdZvi2l/NjPSzp8XfrUklz8iEothRBRQoiYo7/j2bDqrUYhPXVNFgBHW95fA/jMfoUQCUIIc9PvycBMwFfzoXMEc54tbbwY+DbAjbfbbWkVDzsXTxytN1gAXN20KzwNqD0aWulphBD9jsaUhRBT8fig0Daf9RxbAC8Du6WUjwd4WcevS3fvVnVwZysPT0xhS9PP0d3ODODLVrtb+/DMWu7vBjsuwHP3cQClwNet7cCzO7q16Wdnd9gRrC09cU2axkgClgK5Tf8mNj0+GU8nfYAZwPam67IdmB9iG3zOE3gQz00WwAJ80PRZWgcM7sbPa3u2/L3ps7EVWAaM6CY73gWKAVfTZ2U+8Evgl03PCzxNvPOb/iYBMz96wJbftLgma4AZ3WTHCXiW19ta+JO5Xb0u4UqfMGHChAmSPrUkDxMmTJi+TNhhhgkTJkyQhB1mmDBhwgRJ2GGGCRMmTJCEHWaYMGHCBEnYYYYJEyZMkIQdZpgwYcIESdhhhgkTJkyQ/D9LIxt+gysWPgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Creating a network to solve the XOR problem\n",
     "\n",
@@ -686,7 +554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -709,629 +577,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 350 samples, validate on 150 samples\n",
-      "Epoch 1/300\n",
-      "350/350 [==============================] - 1s 3ms/step - loss: 0.7098 - acc: 0.5257 - val_loss: 0.7071 - val_acc: 0.4267\n",
-      "Epoch 2/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.7017 - acc: 0.5457 - val_loss: 0.7006 - val_acc: 0.4467\n",
-      "Epoch 3/300\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.6960 - acc: 0.5657 - val_loss: 0.6954 - val_acc: 0.4667\n",
-      "Epoch 4/300\n",
-      "350/350 [==============================] - 0s 129us/step - loss: 0.6909 - acc: 0.5857 - val_loss: 0.6901 - val_acc: 0.4733\n",
-      "Epoch 5/300\n",
-      "350/350 [==============================] - 0s 102us/step - loss: 0.6861 - acc: 0.5971 - val_loss: 0.6854 - val_acc: 0.5000\n",
-      "Epoch 6/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.6815 - acc: 0.6143 - val_loss: 0.6808 - val_acc: 0.5133\n",
-      "Epoch 7/300\n",
-      "350/350 [==============================] - 0s 113us/step - loss: 0.6767 - acc: 0.6314 - val_loss: 0.6739 - val_acc: 0.5533\n",
-      "Epoch 8/300\n",
-      "350/350 [==============================] - 0s 112us/step - loss: 0.6690 - acc: 0.6629 - val_loss: 0.6616 - val_acc: 0.6467\n",
-      "Epoch 9/300\n",
-      "350/350 [==============================] - 0s 111us/step - loss: 0.6581 - acc: 0.6857 - val_loss: 0.6500 - val_acc: 0.6467\n",
-      "Epoch 10/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.6476 - acc: 0.7086 - val_loss: 0.6407 - val_acc: 0.6267\n",
-      "Epoch 11/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.6383 - acc: 0.7229 - val_loss: 0.6315 - val_acc: 0.6267\n",
-      "Epoch 12/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.6296 - acc: 0.7171 - val_loss: 0.6234 - val_acc: 0.6200\n",
-      "Epoch 13/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.6218 - acc: 0.7086 - val_loss: 0.6159 - val_acc: 0.6133\n",
-      "Epoch 14/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.6137 - acc: 0.6971 - val_loss: 0.6084 - val_acc: 0.6067\n",
-      "Epoch 15/300\n",
-      "350/350 [==============================] - 0s 108us/step - loss: 0.6061 - acc: 0.7029 - val_loss: 0.6020 - val_acc: 0.6067\n",
-      "Epoch 16/300\n",
-      "350/350 [==============================] - 0s 104us/step - loss: 0.5993 - acc: 0.7000 - val_loss: 0.5966 - val_acc: 0.6067\n",
-      "Epoch 17/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.5927 - acc: 0.6971 - val_loss: 0.5917 - val_acc: 0.6067\n",
-      "Epoch 18/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.5863 - acc: 0.7000 - val_loss: 0.5869 - val_acc: 0.6067\n",
-      "Epoch 19/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.5800 - acc: 0.7000 - val_loss: 0.5822 - val_acc: 0.6000\n",
-      "Epoch 20/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.5735 - acc: 0.7000 - val_loss: 0.5776 - val_acc: 0.6000\n",
-      "Epoch 21/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.5675 - acc: 0.7000 - val_loss: 0.5732 - val_acc: 0.6067\n",
-      "Epoch 22/300\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.5616 - acc: 0.6971 - val_loss: 0.5696 - val_acc: 0.6067\n",
-      "Epoch 23/300\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.5560 - acc: 0.7000 - val_loss: 0.5658 - val_acc: 0.6067\n",
-      "Epoch 24/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.5501 - acc: 0.7086 - val_loss: 0.5618 - val_acc: 0.6133\n",
-      "Epoch 25/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.5445 - acc: 0.7114 - val_loss: 0.5579 - val_acc: 0.6133\n",
-      "Epoch 26/300\n",
-      "350/350 [==============================] - 0s 106us/step - loss: 0.5385 - acc: 0.7114 - val_loss: 0.5539 - val_acc: 0.6133\n",
-      "Epoch 27/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.5326 - acc: 0.7171 - val_loss: 0.5499 - val_acc: 0.6200\n",
-      "Epoch 28/300\n",
-      "350/350 [==============================] - 0s 105us/step - loss: 0.5269 - acc: 0.7200 - val_loss: 0.5458 - val_acc: 0.6200\n",
-      "Epoch 29/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.5212 - acc: 0.7229 - val_loss: 0.5418 - val_acc: 0.6200\n",
-      "Epoch 30/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.5155 - acc: 0.7286 - val_loss: 0.5375 - val_acc: 0.6200\n",
-      "Epoch 31/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.5101 - acc: 0.7486 - val_loss: 0.5337 - val_acc: 0.7600\n",
-      "Epoch 32/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.5047 - acc: 0.8543 - val_loss: 0.5293 - val_acc: 0.7667\n",
-      "Epoch 33/300\n",
-      "350/350 [==============================] - 0s 102us/step - loss: 0.4993 - acc: 0.8543 - val_loss: 0.5250 - val_acc: 0.7733\n",
-      "Epoch 34/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.4938 - acc: 0.8571 - val_loss: 0.5209 - val_acc: 0.7733\n",
-      "Epoch 35/300\n",
-      "350/350 [==============================] - 0s 105us/step - loss: 0.4885 - acc: 0.8600 - val_loss: 0.5167 - val_acc: 0.7733\n",
-      "Epoch 36/300\n",
-      "350/350 [==============================] - 0s 109us/step - loss: 0.4830 - acc: 0.8629 - val_loss: 0.5126 - val_acc: 0.7800\n",
-      "Epoch 37/300\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.4776 - acc: 0.8743 - val_loss: 0.5087 - val_acc: 0.7933\n",
-      "Epoch 38/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.4721 - acc: 0.8743 - val_loss: 0.5047 - val_acc: 0.7933\n",
-      "Epoch 39/300\n",
-      "350/350 [==============================] - 0s 127us/step - loss: 0.4665 - acc: 0.8771 - val_loss: 0.5003 - val_acc: 0.7933\n",
-      "Epoch 40/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.4611 - acc: 0.8800 - val_loss: 0.4963 - val_acc: 0.8133\n",
-      "Epoch 41/300\n",
-      "350/350 [==============================] - 0s 108us/step - loss: 0.4560 - acc: 0.8800 - val_loss: 0.4922 - val_acc: 0.8267\n",
-      "Epoch 42/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.4507 - acc: 0.8829 - val_loss: 0.4879 - val_acc: 0.8200\n",
-      "Epoch 43/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.4459 - acc: 0.8829 - val_loss: 0.4846 - val_acc: 0.8267\n",
-      "Epoch 44/300\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.4412 - acc: 0.8771 - val_loss: 0.4803 - val_acc: 0.8267\n",
-      "Epoch 45/300\n",
-      "350/350 [==============================] - 0s 103us/step - loss: 0.4365 - acc: 0.8829 - val_loss: 0.4766 - val_acc: 0.8333\n",
-      "Epoch 46/300\n",
-      "350/350 [==============================] - 0s 112us/step - loss: 0.4315 - acc: 0.8886 - val_loss: 0.4725 - val_acc: 0.8400\n",
-      "Epoch 47/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.4267 - acc: 0.8943 - val_loss: 0.4687 - val_acc: 0.8400\n",
-      "Epoch 48/300\n",
-      "350/350 [==============================] - 0s 106us/step - loss: 0.4219 - acc: 0.8971 - val_loss: 0.4654 - val_acc: 0.8400\n",
-      "Epoch 49/300\n",
-      "350/350 [==============================] - 0s 106us/step - loss: 0.4173 - acc: 0.8943 - val_loss: 0.4615 - val_acc: 0.8400\n",
-      "Epoch 50/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.4130 - acc: 0.8971 - val_loss: 0.4586 - val_acc: 0.8400\n",
-      "Epoch 51/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.4087 - acc: 0.8971 - val_loss: 0.4554 - val_acc: 0.8400\n",
-      "Epoch 52/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.4046 - acc: 0.8971 - val_loss: 0.4516 - val_acc: 0.8400\n",
-      "Epoch 53/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.4009 - acc: 0.8971 - val_loss: 0.4489 - val_acc: 0.8400\n",
-      "Epoch 54/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.3969 - acc: 0.9000 - val_loss: 0.4459 - val_acc: 0.8400\n",
-      "Epoch 55/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.3930 - acc: 0.9000 - val_loss: 0.4423 - val_acc: 0.8400\n",
-      "Epoch 56/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.3889 - acc: 0.9000 - val_loss: 0.4392 - val_acc: 0.8400\n",
-      "Epoch 57/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.3851 - acc: 0.9029 - val_loss: 0.4358 - val_acc: 0.8400\n",
-      "Epoch 58/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.3809 - acc: 0.9057 - val_loss: 0.4324 - val_acc: 0.8467\n",
-      "Epoch 59/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.3773 - acc: 0.9086 - val_loss: 0.4292 - val_acc: 0.8467\n",
-      "Epoch 60/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.3738 - acc: 0.9086 - val_loss: 0.4260 - val_acc: 0.8467\n",
-      "Epoch 61/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.3703 - acc: 0.9086 - val_loss: 0.4221 - val_acc: 0.8533\n",
-      "Epoch 62/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.3672 - acc: 0.9114 - val_loss: 0.4195 - val_acc: 0.8533\n",
-      "Epoch 63/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.3640 - acc: 0.9086 - val_loss: 0.4162 - val_acc: 0.8533\n",
-      "Epoch 64/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.3609 - acc: 0.9114 - val_loss: 0.4136 - val_acc: 0.8533\n",
-      "Epoch 65/300\n",
-      "350/350 [==============================] - 0s 107us/step - loss: 0.3577 - acc: 0.9114 - val_loss: 0.4111 - val_acc: 0.8533\n",
-      "Epoch 66/300\n",
-      "350/350 [==============================] - 0s 119us/step - loss: 0.3542 - acc: 0.9143 - val_loss: 0.4084 - val_acc: 0.8533\n",
-      "Epoch 67/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.3508 - acc: 0.9143 - val_loss: 0.4053 - val_acc: 0.8600\n",
-      "Epoch 68/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.3475 - acc: 0.9143 - val_loss: 0.4026 - val_acc: 0.8600\n",
-      "Epoch 69/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.3444 - acc: 0.9143 - val_loss: 0.4002 - val_acc: 0.8600\n",
-      "Epoch 70/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.3413 - acc: 0.9143 - val_loss: 0.3971 - val_acc: 0.8667\n",
-      "Epoch 71/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.3385 - acc: 0.9171 - val_loss: 0.3952 - val_acc: 0.8667\n",
-      "Epoch 72/300\n",
-      "350/350 [==============================] - 0s 104us/step - loss: 0.3355 - acc: 0.9143 - val_loss: 0.3926 - val_acc: 0.8667\n",
-      "Epoch 73/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.3325 - acc: 0.9229 - val_loss: 0.3894 - val_acc: 0.8667\n",
-      "Epoch 74/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.3295 - acc: 0.9200 - val_loss: 0.3872 - val_acc: 0.8667\n",
-      "Epoch 75/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.3267 - acc: 0.9229 - val_loss: 0.3848 - val_acc: 0.8667\n",
-      "Epoch 76/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.3243 - acc: 0.9229 - val_loss: 0.3825 - val_acc: 0.8667\n",
-      "Epoch 77/300\n",
-      "350/350 [==============================] - 0s 110us/step - loss: 0.3214 - acc: 0.9229 - val_loss: 0.3803 - val_acc: 0.8667\n",
-      "Epoch 78/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.3188 - acc: 0.9229 - val_loss: 0.3782 - val_acc: 0.8667\n",
-      "Epoch 79/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.3163 - acc: 0.9257 - val_loss: 0.3750 - val_acc: 0.8667\n",
-      "Epoch 80/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.3138 - acc: 0.9257 - val_loss: 0.3726 - val_acc: 0.8667\n",
-      "Epoch 81/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.3114 - acc: 0.9286 - val_loss: 0.3702 - val_acc: 0.8667\n",
-      "Epoch 82/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.3091 - acc: 0.9286 - val_loss: 0.3674 - val_acc: 0.8667\n",
-      "Epoch 83/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.3065 - acc: 0.9286 - val_loss: 0.3654 - val_acc: 0.8667\n",
-      "Epoch 84/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.3041 - acc: 0.9286 - val_loss: 0.3632 - val_acc: 0.8667\n",
-      "Epoch 85/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.3020 - acc: 0.9286 - val_loss: 0.3609 - val_acc: 0.8667\n",
-      "Epoch 86/300\n",
-      "350/350 [==============================] - 0s 104us/step - loss: 0.2994 - acc: 0.9286 - val_loss: 0.3588 - val_acc: 0.8800\n",
-      "Epoch 87/300\n",
-      "350/350 [==============================] - 0s 107us/step - loss: 0.2972 - acc: 0.9314 - val_loss: 0.3564 - val_acc: 0.8800\n",
-      "Epoch 88/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.2948 - acc: 0.9314 - val_loss: 0.3543 - val_acc: 0.8800\n",
-      "Epoch 89/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.2927 - acc: 0.9314 - val_loss: 0.3523 - val_acc: 0.8800\n",
-      "Epoch 90/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.2904 - acc: 0.9314 - val_loss: 0.3503 - val_acc: 0.8800\n",
-      "Epoch 91/300\n",
-      "350/350 [==============================] - 0s 102us/step - loss: 0.2883 - acc: 0.9314 - val_loss: 0.3484 - val_acc: 0.8800\n",
-      "Epoch 92/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.2861 - acc: 0.9314 - val_loss: 0.3457 - val_acc: 0.8800\n",
-      "Epoch 93/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.2839 - acc: 0.9314 - val_loss: 0.3429 - val_acc: 0.8800\n",
-      "Epoch 94/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.2818 - acc: 0.9343 - val_loss: 0.3405 - val_acc: 0.8800\n",
-      "Epoch 95/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.2794 - acc: 0.9343 - val_loss: 0.3385 - val_acc: 0.8800\n",
-      "Epoch 96/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.2774 - acc: 0.9314 - val_loss: 0.3366 - val_acc: 0.8800\n",
-      "Epoch 97/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.2754 - acc: 0.9314 - val_loss: 0.3340 - val_acc: 0.8800\n",
-      "Epoch 98/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.2733 - acc: 0.9314 - val_loss: 0.3317 - val_acc: 0.8800\n",
-      "Epoch 99/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.2712 - acc: 0.9371 - val_loss: 0.3301 - val_acc: 0.8800\n",
-      "Epoch 100/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.2694 - acc: 0.9314 - val_loss: 0.3280 - val_acc: 0.8800\n",
-      "Epoch 101/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.2674 - acc: 0.9343 - val_loss: 0.3262 - val_acc: 0.8800\n",
-      "Epoch 102/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.2655 - acc: 0.9400 - val_loss: 0.3242 - val_acc: 0.8800\n",
-      "Epoch 103/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.2636 - acc: 0.9400 - val_loss: 0.3226 - val_acc: 0.8867\n",
-      "Epoch 104/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.2616 - acc: 0.9400 - val_loss: 0.3198 - val_acc: 0.8933\n",
-      "Epoch 105/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.2599 - acc: 0.9400 - val_loss: 0.3176 - val_acc: 0.8933\n",
-      "Epoch 106/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.2582 - acc: 0.9400 - val_loss: 0.3156 - val_acc: 0.8933\n",
-      "Epoch 107/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.2564 - acc: 0.9400 - val_loss: 0.3133 - val_acc: 0.8933\n",
-      "Epoch 108/300\n",
-      "350/350 [==============================] - 0s 95us/step - loss: 0.2545 - acc: 0.9429 - val_loss: 0.3105 - val_acc: 0.8933\n",
-      "Epoch 109/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.2527 - acc: 0.9429 - val_loss: 0.3083 - val_acc: 0.9000\n",
-      "Epoch 110/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.2513 - acc: 0.9429 - val_loss: 0.3071 - val_acc: 0.9067\n",
-      "Epoch 111/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.2494 - acc: 0.9457 - val_loss: 0.3051 - val_acc: 0.9067\n",
-      "Epoch 112/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.2476 - acc: 0.9457 - val_loss: 0.3026 - val_acc: 0.9067\n",
-      "Epoch 113/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.2462 - acc: 0.9514 - val_loss: 0.3008 - val_acc: 0.9000\n",
-      "Epoch 114/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.2444 - acc: 0.9514 - val_loss: 0.2991 - val_acc: 0.9067\n",
-      "Epoch 115/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.2427 - acc: 0.9514 - val_loss: 0.2963 - val_acc: 0.9067\n",
-      "Epoch 116/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.2414 - acc: 0.9514 - val_loss: 0.2947 - val_acc: 0.9067\n",
-      "Epoch 117/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.2395 - acc: 0.9514 - val_loss: 0.2930 - val_acc: 0.9067\n",
-      "Epoch 118/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.2380 - acc: 0.9514 - val_loss: 0.2913 - val_acc: 0.9133\n",
-      "Epoch 119/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.2365 - acc: 0.9571 - val_loss: 0.2894 - val_acc: 0.9133\n",
-      "Epoch 120/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.2351 - acc: 0.9514 - val_loss: 0.2876 - val_acc: 0.9133\n",
-      "Epoch 121/300\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "350/350 [==============================] - 0s 78us/step - loss: 0.2336 - acc: 0.9571 - val_loss: 0.2857 - val_acc: 0.9133\n",
-      "Epoch 122/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.2318 - acc: 0.9543 - val_loss: 0.2843 - val_acc: 0.9200\n",
-      "Epoch 123/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.2304 - acc: 0.9571 - val_loss: 0.2827 - val_acc: 0.9200\n",
-      "Epoch 124/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.2295 - acc: 0.9543 - val_loss: 0.2813 - val_acc: 0.9200\n",
-      "Epoch 125/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.2276 - acc: 0.9571 - val_loss: 0.2796 - val_acc: 0.9200\n",
-      "Epoch 126/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.2262 - acc: 0.9571 - val_loss: 0.2783 - val_acc: 0.9200\n",
-      "Epoch 127/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.2247 - acc: 0.9571 - val_loss: 0.2757 - val_acc: 0.9200\n",
-      "Epoch 128/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.2234 - acc: 0.9543 - val_loss: 0.2744 - val_acc: 0.9200\n",
-      "Epoch 129/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.2218 - acc: 0.9571 - val_loss: 0.2722 - val_acc: 0.9200\n",
-      "Epoch 130/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.2205 - acc: 0.9543 - val_loss: 0.2708 - val_acc: 0.9333\n",
-      "Epoch 131/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.2192 - acc: 0.9571 - val_loss: 0.2695 - val_acc: 0.9333\n",
-      "Epoch 132/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.2174 - acc: 0.9571 - val_loss: 0.2684 - val_acc: 0.9333\n",
-      "Epoch 133/300\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.2163 - acc: 0.9600 - val_loss: 0.2671 - val_acc: 0.9333\n",
-      "Epoch 134/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.2146 - acc: 0.9600 - val_loss: 0.2656 - val_acc: 0.9333\n",
-      "Epoch 135/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.2134 - acc: 0.9629 - val_loss: 0.2639 - val_acc: 0.9333\n",
-      "Epoch 136/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.2122 - acc: 0.9600 - val_loss: 0.2620 - val_acc: 0.9333\n",
-      "Epoch 137/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.2107 - acc: 0.9600 - val_loss: 0.2608 - val_acc: 0.9333\n",
-      "Epoch 138/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.2095 - acc: 0.9600 - val_loss: 0.2590 - val_acc: 0.9400\n",
-      "Epoch 139/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.2080 - acc: 0.9600 - val_loss: 0.2579 - val_acc: 0.9400\n",
-      "Epoch 140/300\n",
-      "350/350 [==============================] - 0s 73us/step - loss: 0.2069 - acc: 0.9600 - val_loss: 0.2566 - val_acc: 0.9400\n",
-      "Epoch 141/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.2056 - acc: 0.9600 - val_loss: 0.2553 - val_acc: 0.9400\n",
-      "Epoch 142/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.2041 - acc: 0.9600 - val_loss: 0.2538 - val_acc: 0.9400\n",
-      "Epoch 143/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.2030 - acc: 0.9600 - val_loss: 0.2516 - val_acc: 0.9400\n",
-      "Epoch 144/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.2017 - acc: 0.9629 - val_loss: 0.2507 - val_acc: 0.9400\n",
-      "Epoch 145/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.2007 - acc: 0.9600 - val_loss: 0.2494 - val_acc: 0.9400\n",
-      "Epoch 146/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1994 - acc: 0.9600 - val_loss: 0.2480 - val_acc: 0.9400\n",
-      "Epoch 147/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1982 - acc: 0.9600 - val_loss: 0.2468 - val_acc: 0.9400\n",
-      "Epoch 148/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1973 - acc: 0.9600 - val_loss: 0.2456 - val_acc: 0.9400\n",
-      "Epoch 149/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.1960 - acc: 0.9629 - val_loss: 0.2439 - val_acc: 0.9400\n",
-      "Epoch 150/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1949 - acc: 0.9600 - val_loss: 0.2429 - val_acc: 0.9400\n",
-      "Epoch 151/300\n",
-      "350/350 [==============================] - 0s 116us/step - loss: 0.1937 - acc: 0.9629 - val_loss: 0.2418 - val_acc: 0.9400\n",
-      "Epoch 152/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.1925 - acc: 0.9600 - val_loss: 0.2403 - val_acc: 0.9333\n",
-      "Epoch 153/300\n",
-      "350/350 [==============================] - 0s 99us/step - loss: 0.1916 - acc: 0.9629 - val_loss: 0.2391 - val_acc: 0.9333\n",
-      "Epoch 154/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.1904 - acc: 0.9629 - val_loss: 0.2376 - val_acc: 0.9333\n",
-      "Epoch 155/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1895 - acc: 0.9629 - val_loss: 0.2364 - val_acc: 0.9333\n",
-      "Epoch 156/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1886 - acc: 0.9629 - val_loss: 0.2349 - val_acc: 0.9333\n",
-      "Epoch 157/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1873 - acc: 0.9629 - val_loss: 0.2343 - val_acc: 0.9333\n",
-      "Epoch 158/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1864 - acc: 0.9629 - val_loss: 0.2329 - val_acc: 0.9333\n",
-      "Epoch 159/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1854 - acc: 0.9629 - val_loss: 0.2319 - val_acc: 0.9333\n",
-      "Epoch 160/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1845 - acc: 0.9629 - val_loss: 0.2307 - val_acc: 0.9333\n",
-      "Epoch 161/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1833 - acc: 0.9629 - val_loss: 0.2297 - val_acc: 0.9333\n",
-      "Epoch 162/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1824 - acc: 0.9629 - val_loss: 0.2285 - val_acc: 0.9333\n",
-      "Epoch 163/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1815 - acc: 0.9629 - val_loss: 0.2272 - val_acc: 0.9333\n",
-      "Epoch 164/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1805 - acc: 0.9629 - val_loss: 0.2265 - val_acc: 0.9333\n",
-      "Epoch 165/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1796 - acc: 0.9629 - val_loss: 0.2256 - val_acc: 0.9333\n",
-      "Epoch 166/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1791 - acc: 0.9629 - val_loss: 0.2245 - val_acc: 0.9333\n",
-      "Epoch 167/300\n",
-      "350/350 [==============================] - 0s 110us/step - loss: 0.1781 - acc: 0.9629 - val_loss: 0.2234 - val_acc: 0.9333\n",
-      "Epoch 168/300\n",
-      "350/350 [==============================] - 0s 100us/step - loss: 0.1772 - acc: 0.9629 - val_loss: 0.2228 - val_acc: 0.9333\n",
-      "Epoch 169/300\n",
-      "350/350 [==============================] - 0s 101us/step - loss: 0.1761 - acc: 0.9629 - val_loss: 0.2214 - val_acc: 0.9333\n",
-      "Epoch 170/300\n",
-      "350/350 [==============================] - 0s 98us/step - loss: 0.1754 - acc: 0.9657 - val_loss: 0.2207 - val_acc: 0.9333\n",
-      "Epoch 171/300\n",
-      "350/350 [==============================] - 0s 97us/step - loss: 0.1745 - acc: 0.9629 - val_loss: 0.2198 - val_acc: 0.9333\n",
-      "Epoch 172/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1740 - acc: 0.9629 - val_loss: 0.2183 - val_acc: 0.9333\n",
-      "Epoch 173/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1727 - acc: 0.9629 - val_loss: 0.2173 - val_acc: 0.9400\n",
-      "Epoch 174/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1720 - acc: 0.9629 - val_loss: 0.2163 - val_acc: 0.9400\n",
-      "Epoch 175/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1711 - acc: 0.9629 - val_loss: 0.2155 - val_acc: 0.9400\n",
-      "Epoch 176/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1702 - acc: 0.9629 - val_loss: 0.2143 - val_acc: 0.9400\n",
-      "Epoch 177/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1693 - acc: 0.9629 - val_loss: 0.2130 - val_acc: 0.9467\n",
-      "Epoch 178/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1687 - acc: 0.9657 - val_loss: 0.2124 - val_acc: 0.9400\n",
-      "Epoch 179/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.1677 - acc: 0.9629 - val_loss: 0.2111 - val_acc: 0.9533\n",
-      "Epoch 180/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1671 - acc: 0.9629 - val_loss: 0.2098 - val_acc: 0.9533\n",
-      "Epoch 181/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1664 - acc: 0.9629 - val_loss: 0.2093 - val_acc: 0.9533\n",
-      "Epoch 182/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1658 - acc: 0.9629 - val_loss: 0.2082 - val_acc: 0.9533\n",
-      "Epoch 183/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1650 - acc: 0.9629 - val_loss: 0.2071 - val_acc: 0.9533\n",
-      "Epoch 184/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1643 - acc: 0.9629 - val_loss: 0.2063 - val_acc: 0.9533\n",
-      "Epoch 185/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1635 - acc: 0.9629 - val_loss: 0.2052 - val_acc: 0.9533\n",
-      "Epoch 186/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1629 - acc: 0.9629 - val_loss: 0.2044 - val_acc: 0.9533\n",
-      "Epoch 187/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1621 - acc: 0.9629 - val_loss: 0.2037 - val_acc: 0.9533\n",
-      "Epoch 188/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.1615 - acc: 0.9629 - val_loss: 0.2028 - val_acc: 0.9533\n",
-      "Epoch 189/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1605 - acc: 0.9629 - val_loss: 0.2024 - val_acc: 0.9533\n",
-      "Epoch 190/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1602 - acc: 0.9657 - val_loss: 0.2011 - val_acc: 0.9533\n",
-      "Epoch 191/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1593 - acc: 0.9629 - val_loss: 0.2007 - val_acc: 0.9533\n",
-      "Epoch 192/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1587 - acc: 0.9629 - val_loss: 0.2001 - val_acc: 0.9533\n",
-      "Epoch 193/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1579 - acc: 0.9657 - val_loss: 0.1995 - val_acc: 0.9533\n",
-      "Epoch 194/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1574 - acc: 0.9629 - val_loss: 0.1989 - val_acc: 0.9533\n",
-      "Epoch 195/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1569 - acc: 0.9629 - val_loss: 0.1986 - val_acc: 0.9533\n",
-      "Epoch 196/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.1561 - acc: 0.9657 - val_loss: 0.1979 - val_acc: 0.9533\n",
-      "Epoch 197/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1555 - acc: 0.9629 - val_loss: 0.1967 - val_acc: 0.9533\n",
-      "Epoch 198/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1548 - acc: 0.9629 - val_loss: 0.1957 - val_acc: 0.9533\n",
-      "Epoch 199/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.1543 - acc: 0.9629 - val_loss: 0.1950 - val_acc: 0.9533\n",
-      "Epoch 200/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1538 - acc: 0.9629 - val_loss: 0.1945 - val_acc: 0.9533\n",
-      "Epoch 201/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1528 - acc: 0.9629 - val_loss: 0.1937 - val_acc: 0.9533\n",
-      "Epoch 202/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.1522 - acc: 0.9629 - val_loss: 0.1926 - val_acc: 0.9533\n",
-      "Epoch 203/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1519 - acc: 0.9629 - val_loss: 0.1927 - val_acc: 0.9533\n",
-      "Epoch 204/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.1511 - acc: 0.9629 - val_loss: 0.1919 - val_acc: 0.9533\n",
-      "Epoch 205/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.1504 - acc: 0.9657 - val_loss: 0.1907 - val_acc: 0.9533\n",
-      "Epoch 206/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1499 - acc: 0.9629 - val_loss: 0.1893 - val_acc: 0.9533\n",
-      "Epoch 207/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1496 - acc: 0.9657 - val_loss: 0.1886 - val_acc: 0.9533\n",
-      "Epoch 208/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.1486 - acc: 0.9657 - val_loss: 0.1875 - val_acc: 0.9533\n",
-      "Epoch 209/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1481 - acc: 0.9657 - val_loss: 0.1866 - val_acc: 0.9533\n",
-      "Epoch 210/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1475 - acc: 0.9657 - val_loss: 0.1858 - val_acc: 0.9533\n",
-      "Epoch 211/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.1468 - acc: 0.9657 - val_loss: 0.1856 - val_acc: 0.9533\n",
-      "Epoch 212/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1465 - acc: 0.9657 - val_loss: 0.1844 - val_acc: 0.9533\n",
-      "Epoch 213/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1458 - acc: 0.9657 - val_loss: 0.1840 - val_acc: 0.9533\n",
-      "Epoch 214/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.1452 - acc: 0.9657 - val_loss: 0.1838 - val_acc: 0.9533\n",
-      "Epoch 215/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1451 - acc: 0.9629 - val_loss: 0.1833 - val_acc: 0.9533\n",
-      "Epoch 216/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1442 - acc: 0.9657 - val_loss: 0.1820 - val_acc: 0.9533\n",
-      "Epoch 217/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1437 - acc: 0.9657 - val_loss: 0.1812 - val_acc: 0.9533\n",
-      "Epoch 218/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1432 - acc: 0.9657 - val_loss: 0.1812 - val_acc: 0.9533\n",
-      "Epoch 219/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1426 - acc: 0.9657 - val_loss: 0.1808 - val_acc: 0.9533\n",
-      "Epoch 220/300\n",
-      "350/350 [==============================] - 0s 96us/step - loss: 0.1419 - acc: 0.9629 - val_loss: 0.1800 - val_acc: 0.9533\n",
-      "Epoch 221/300\n",
-      "350/350 [==============================] - 0s 91us/step - loss: 0.1417 - acc: 0.9657 - val_loss: 0.1796 - val_acc: 0.9533\n",
-      "Epoch 222/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.1412 - acc: 0.9657 - val_loss: 0.1784 - val_acc: 0.9533\n",
-      "Epoch 223/300\n",
-      "350/350 [==============================] - 0s 105us/step - loss: 0.1404 - acc: 0.9657 - val_loss: 0.1780 - val_acc: 0.9533\n",
-      "Epoch 224/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.1397 - acc: 0.9657 - val_loss: 0.1771 - val_acc: 0.9533\n",
-      "Epoch 225/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1395 - acc: 0.9657 - val_loss: 0.1764 - val_acc: 0.9533\n",
-      "Epoch 226/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1389 - acc: 0.9657 - val_loss: 0.1765 - val_acc: 0.9533\n",
-      "Epoch 227/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1382 - acc: 0.9657 - val_loss: 0.1760 - val_acc: 0.9533\n",
-      "Epoch 228/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1381 - acc: 0.9657 - val_loss: 0.1747 - val_acc: 0.9533\n",
-      "Epoch 229/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1372 - acc: 0.9657 - val_loss: 0.1740 - val_acc: 0.9533\n",
-      "Epoch 230/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.1367 - acc: 0.9657 - val_loss: 0.1737 - val_acc: 0.9533\n",
-      "Epoch 231/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1361 - acc: 0.9657 - val_loss: 0.1730 - val_acc: 0.9533\n",
-      "Epoch 232/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1358 - acc: 0.9657 - val_loss: 0.1723 - val_acc: 0.9533\n",
-      "Epoch 233/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1352 - acc: 0.9657 - val_loss: 0.1713 - val_acc: 0.9533\n",
-      "Epoch 234/300\n",
-      "350/350 [==============================] - 0s 92us/step - loss: 0.1347 - acc: 0.9657 - val_loss: 0.1705 - val_acc: 0.9600\n",
-      "Epoch 235/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1342 - acc: 0.9657 - val_loss: 0.1703 - val_acc: 0.9600\n",
-      "Epoch 236/300\n",
-      "350/350 [==============================] - 0s 93us/step - loss: 0.1336 - acc: 0.9657 - val_loss: 0.1692 - val_acc: 0.9600\n",
-      "Epoch 237/300\n",
-      "350/350 [==============================] - 0s 89us/step - loss: 0.1331 - acc: 0.9657 - val_loss: 0.1689 - val_acc: 0.9600\n",
-      "Epoch 238/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1327 - acc: 0.9657 - val_loss: 0.1687 - val_acc: 0.9600\n",
-      "Epoch 239/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1321 - acc: 0.9657 - val_loss: 0.1679 - val_acc: 0.9600\n",
-      "Epoch 240/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1316 - acc: 0.9657 - val_loss: 0.1670 - val_acc: 0.9600\n",
-      "Epoch 241/300\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1311 - acc: 0.9657 - val_loss: 0.1665 - val_acc: 0.9600\n",
-      "Epoch 242/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1305 - acc: 0.9657 - val_loss: 0.1661 - val_acc: 0.9600\n",
-      "Epoch 243/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1303 - acc: 0.9657 - val_loss: 0.1655 - val_acc: 0.9600\n",
-      "Epoch 244/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1296 - acc: 0.9657 - val_loss: 0.1655 - val_acc: 0.9600\n",
-      "Epoch 245/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.1294 - acc: 0.9657 - val_loss: 0.1653 - val_acc: 0.9600\n",
-      "Epoch 246/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1289 - acc: 0.9686 - val_loss: 0.1642 - val_acc: 0.9600\n",
-      "Epoch 247/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1286 - acc: 0.9657 - val_loss: 0.1639 - val_acc: 0.9600\n",
-      "Epoch 248/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1281 - acc: 0.9657 - val_loss: 0.1634 - val_acc: 0.9600\n",
-      "Epoch 249/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1279 - acc: 0.9657 - val_loss: 0.1629 - val_acc: 0.9600\n",
-      "Epoch 250/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.1274 - acc: 0.9657 - val_loss: 0.1621 - val_acc: 0.9600\n",
-      "Epoch 251/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1270 - acc: 0.9657 - val_loss: 0.1615 - val_acc: 0.9600\n",
-      "Epoch 252/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1264 - acc: 0.9657 - val_loss: 0.1615 - val_acc: 0.9600\n",
-      "Epoch 253/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.1263 - acc: 0.9657 - val_loss: 0.1610 - val_acc: 0.9600\n",
-      "Epoch 254/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1257 - acc: 0.9657 - val_loss: 0.1600 - val_acc: 0.9600\n",
-      "Epoch 255/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1253 - acc: 0.9657 - val_loss: 0.1598 - val_acc: 0.9600\n",
-      "Epoch 256/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1251 - acc: 0.9657 - val_loss: 0.1590 - val_acc: 0.9600\n",
-      "Epoch 257/300\n",
-      "350/350 [==============================] - 0s 90us/step - loss: 0.1244 - acc: 0.9657 - val_loss: 0.1587 - val_acc: 0.9600\n",
-      "Epoch 258/300\n",
-      "350/350 [==============================] - 0s 76us/step - loss: 0.1243 - acc: 0.9657 - val_loss: 0.1586 - val_acc: 0.9600\n",
-      "Epoch 259/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1238 - acc: 0.9657 - val_loss: 0.1581 - val_acc: 0.9600\n",
-      "Epoch 260/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1233 - acc: 0.9657 - val_loss: 0.1575 - val_acc: 0.9600\n",
-      "Epoch 261/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.1230 - acc: 0.9657 - val_loss: 0.1568 - val_acc: 0.9600\n",
-      "Epoch 262/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1226 - acc: 0.9657 - val_loss: 0.1569 - val_acc: 0.9600\n",
-      "Epoch 263/300\n",
-      "350/350 [==============================] - 0s 77us/step - loss: 0.1223 - acc: 0.9657 - val_loss: 0.1555 - val_acc: 0.9600\n",
-      "Epoch 264/300\n",
-      "350/350 [==============================] - 0s 80us/step - loss: 0.1217 - acc: 0.9657 - val_loss: 0.1547 - val_acc: 0.9600\n",
-      "Epoch 265/300\n",
-      "350/350 [==============================] - 0s 82us/step - loss: 0.1215 - acc: 0.9657 - val_loss: 0.1551 - val_acc: 0.9600\n",
-      "Epoch 266/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1211 - acc: 0.9657 - val_loss: 0.1548 - val_acc: 0.9600\n",
-      "Epoch 267/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1206 - acc: 0.9657 - val_loss: 0.1540 - val_acc: 0.9600\n",
-      "Epoch 268/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1206 - acc: 0.9657 - val_loss: 0.1533 - val_acc: 0.9600\n",
-      "Epoch 269/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1202 - acc: 0.9686 - val_loss: 0.1534 - val_acc: 0.9600\n",
-      "Epoch 270/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1195 - acc: 0.9657 - val_loss: 0.1528 - val_acc: 0.9600\n",
-      "Epoch 271/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1197 - acc: 0.9657 - val_loss: 0.1524 - val_acc: 0.9600\n",
-      "Epoch 272/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.1190 - acc: 0.9657 - val_loss: 0.1518 - val_acc: 0.9600\n",
-      "Epoch 273/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1188 - acc: 0.9657 - val_loss: 0.1520 - val_acc: 0.9600\n",
-      "Epoch 274/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1184 - acc: 0.9657 - val_loss: 0.1519 - val_acc: 0.9600\n",
-      "Epoch 275/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1181 - acc: 0.9657 - val_loss: 0.1510 - val_acc: 0.9600\n",
-      "Epoch 276/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1178 - acc: 0.9657 - val_loss: 0.1501 - val_acc: 0.9600\n",
-      "Epoch 277/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1173 - acc: 0.9657 - val_loss: 0.1496 - val_acc: 0.9600\n",
-      "Epoch 278/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.1169 - acc: 0.9657 - val_loss: 0.1495 - val_acc: 0.9600\n",
-      "Epoch 279/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1168 - acc: 0.9657 - val_loss: 0.1489 - val_acc: 0.9600\n",
-      "Epoch 280/300\n",
-      "350/350 [==============================] - 0s 75us/step - loss: 0.1165 - acc: 0.9657 - val_loss: 0.1488 - val_acc: 0.9600\n",
-      "Epoch 281/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1162 - acc: 0.9657 - val_loss: 0.1476 - val_acc: 0.9600\n",
-      "Epoch 282/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1158 - acc: 0.9657 - val_loss: 0.1474 - val_acc: 0.9600\n",
-      "Epoch 283/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1155 - acc: 0.9657 - val_loss: 0.1473 - val_acc: 0.9600\n",
-      "Epoch 284/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1153 - acc: 0.9657 - val_loss: 0.1474 - val_acc: 0.9600\n",
-      "Epoch 285/300\n",
-      "350/350 [==============================] - 0s 74us/step - loss: 0.1152 - acc: 0.9686 - val_loss: 0.1472 - val_acc: 0.9600\n",
-      "Epoch 286/300\n",
-      "350/350 [==============================] - 0s 84us/step - loss: 0.1146 - acc: 0.9657 - val_loss: 0.1465 - val_acc: 0.9600\n",
-      "Epoch 287/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1145 - acc: 0.9657 - val_loss: 0.1459 - val_acc: 0.9600\n",
-      "Epoch 288/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1142 - acc: 0.9657 - val_loss: 0.1457 - val_acc: 0.9600\n",
-      "Epoch 289/300\n",
-      "350/350 [==============================] - 0s 83us/step - loss: 0.1139 - acc: 0.9657 - val_loss: 0.1454 - val_acc: 0.9600\n",
-      "Epoch 290/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1136 - acc: 0.9657 - val_loss: 0.1454 - val_acc: 0.9600\n",
-      "Epoch 291/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1133 - acc: 0.9657 - val_loss: 0.1448 - val_acc: 0.9600\n",
-      "Epoch 292/300\n",
-      "350/350 [==============================] - 0s 87us/step - loss: 0.1129 - acc: 0.9657 - val_loss: 0.1443 - val_acc: 0.9600\n",
-      "Epoch 293/300\n",
-      "350/350 [==============================] - 0s 88us/step - loss: 0.1127 - acc: 0.9657 - val_loss: 0.1440 - val_acc: 0.9600\n",
-      "Epoch 294/300\n",
-      "350/350 [==============================] - 0s 79us/step - loss: 0.1124 - acc: 0.9657 - val_loss: 0.1439 - val_acc: 0.9600\n",
-      "Epoch 295/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1121 - acc: 0.9657 - val_loss: 0.1436 - val_acc: 0.9600\n",
-      "Epoch 296/300\n",
-      "350/350 [==============================] - 0s 78us/step - loss: 0.1116 - acc: 0.9657 - val_loss: 0.1429 - val_acc: 0.9600\n",
-      "Epoch 297/300\n",
-      "350/350 [==============================] - 0s 81us/step - loss: 0.1118 - acc: 0.9657 - val_loss: 0.1420 - val_acc: 0.9600\n",
-      "Epoch 298/300\n",
-      "350/350 [==============================] - 0s 85us/step - loss: 0.1113 - acc: 0.9657 - val_loss: 0.1418 - val_acc: 0.9600\n",
-      "Epoch 299/300\n",
-      "350/350 [==============================] - 0s 86us/step - loss: 0.1110 - acc: 0.9657 - val_loss: 0.1414 - val_acc: 0.9600\n",
-      "Epoch 300/300\n",
-      "350/350 [==============================] - 0s 94us/step - loss: 0.1106 - acc: 0.9657 - val_loss: 0.1417 - val_acc: 0.9600\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Instantiating the model\n",
     "model = a_simple_NN()\n",
@@ -1350,29 +598,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XucVXW9//HXZwYGGO4KeJCLYGHlrZIR7WjmLUU7iVYWlHlJRRPPyexkmh7zp9mxc7pJ4gXNotRQOaaUGqGpdSwNFEQuDwxRA+EYIpcEmevn98dnj7Nn2DOzZ9jDnrX2+/l47Mfsvfbae38Wi3nPd3/Xd32XuTsiIpIuZcUuQERECk/hLiKSQgp3EZEUUriLiKSQwl1EJIUU7iIiKaRwFxFJIYW7iEgKKdxFRFKoR7E+eMiQIT5mzJhifbyISCI999xzb7r70PbWK1q4jxkzhoULFxbr40VEEsnMXstnPXXLiIikkMJdRCSF2g13M7vTzP5uZktbed7MbLqZrTKzJWZ2SOHLFBGRjsin5f4zYGIbz58EjMvcpgK37HpZIiKyK9oNd3f/A/BWG6tMAn7u4RlgkJkNL1SBIiLScYXocx8BrMl6vDazTEREiqQQ4W45luW8vJOZTTWzhWa2cMOGDQX4aBERyaUQ49zXAqOyHo8E1uVa0d1nAjMBqqqqdH0/ke5s6VK4775iV5FOn/wkHHpol35EIcJ9LnCxmc0GDgO2uPv6AryviBTTRRfBH/8IluvLueySvfcufrib2S+Bo4EhZrYW+BbQE8DdbwUeAU4GVgHbgXO6qlgR2U1eeimC/T//Ey6/vNjVSCe0G+7uPqWd5x2YVrCKRKR4fvADuOkm+Mc/oLwczjqr2BVJJxVtbhkR6Waqq+H66+Gf/gmOPBImTIDhGtWcVAp3ke5kyRJ4++3ifPaf/gRvvQW//CWccEJxapCCUbiLdBePPw7HH1/cGvbZB447rrg1SEEo3EW6i5kzYc894e67izdC5X3vi752STyFu0hX+OtfYfp0qK/Pb313ePBB+PKX4cQTu7Y2KQkKd5Gu8K1vxQlAe+yR/2uGD49wFykAhbtIoW3aBA88ABdeGMMKRYpAF+sQ6awf/hAGDoQBA5rfRoyIYYXnnlvsCqWEqeUu0hl1dfDf/x2jS3KNcBkzBj784d1elkgjhbtIe9avhwUL4v7++8OgQXDHHbH85pvh1FOLW59IDgp3kfacfjo8/XTcHz4cPvQhePTRmPzpE58obm0lbOtWqKyEHq2k2DvvwMsvNz0ePTp6zQBqa2HHDujfPwY0vf129LClifrcRdqyYkUE+ze+ATNmRGv90UdjVMuiRdCzZ7ErLCmemSh8+XIYOzbOt9qxI5Y1NMD27XFbswYOPhgOOqjp9tGPxuu3bYOjjoL3vAdeeCFOxh0zBp5/vun1jSNYd+yIx3V18bi6Ov4wNN5vXH/7dqipieU1Nc2XV1fH8trapmWN79GVzL0406pXVVX5woULi/LZUiLc4corYdWqzr/HqlXw4ouwdm0Maxw1Ct54I8axv/e9hau1hK1fH0F89925Zz3Ytg0OPBAuuADuuitmyn3ssVi+aRN86lNw223x2kWLml5XWQk33hi9aM8/HxNczp0Lt94Kv/1ttOI3b45199gjZl5oNGJEfCm7/fb4bzRkCHzuc3DLLfG6M8+MHrnG0Afo0ydqvO22+NbQqEePmD151izYsiWW3XJLDKbqDDN7zt2r2l1P4S6p9eyzcPjhcdCzsrLz73PKKXDDDXF/5sy4iMX06YWpUbj11vgidMYZcO21MGVK9ITdey+sWxfBPm9enLTbGFf9+8Mf/gBPPQWXXBLBWl8PV1zRtKuPPx4OOSTu19RES3/Dhmg133YbfOxjEfYHHRQn5v7P/0Trv6EhJsfcsCHqGD8+vrStWROB/9JL8bf9+OPh4x9v2o7774eFC+OPz2c+07R83jz4/e9hv/2aBlCdcEL07nVGvuGOuxflNn78eBfJW22te3V1x27nnedeWem+ZUuxq0+dhx92nzo1dku+VqxwP+EE96OPdn/uOfczznB//nn3k092B/c99nAfNy7ug/uAAe7//M9xf6+94ucHP+h+003uzzzT9L4/+Yn7BRe4P/ZY258/b16sN2tW+7UuWeL+ve+519XF45decr/hhvhv9eqr7tdf775tW/PXbNjgft117m++2Xz5tm2x/muvtf+5+QAWeh4Zq5a7dH+LFkULvLFTsyPOOgt+9rOCl5RGDz4YXSSjR0dv1P77xySVl14a1+24/fZo1brHOVrV1fAf/wF/+1vzPuRhw+Ccc+DHP47+5UZPPRV92P/4B/TrF90gQ4fGgdERI2D16miBP/hgvP8ZZ0Sr+fLL4QtfiBN+/+Vf4Oijd/s/TbeibhlJj/PPh3vugW9+s2MTapWVRUKMHNl1tXVD994bPUcdsW1b9E83NMQ/W/bP00+Hhx+G3r2bZlPYd9/4A7B6dfRBDxvW9F6vvBJ/AHr3jgFFjQYMiD8QM2bAnXfG32v3GKly661x2sAFF8DJJ+/6v0GaqVtGkq262v2uu9zvuMO9Xz/3s88udkVF0dDgPn/+zl0A7u7PPuu+aVPzZdOnR/eFmXtZWcduxxzj/sUvuh9xhPu557pPmBDdGOXl7gcd5L5+ffPPmj3b/f3vd1+5svnyWbPcx4xp3nWSbcUK95Ej3f/8587/u5Qy1C0jiXbLLTHEoNEzz8BhhxWvngJqaIhzorJHVEAcJDzkkOZfTq66Ki6OdOKJMRfZhAnRDbJ1axyQGz8+Dv4dfjg89FCM6DjlFJgzp/Xx35Js6paRZKuqio7cX/86OmKHDi12RW1yh9dfj+Buz/XXx6CbXK66Cq67LoL/+efjancf+Qj8+c/x/OGHxyCgysoYhldXF6NExo+PEZuHHgrz58c/maRTvuGuv+2yezz1VKRWPulXXw/PPRedwKNHd31tu6i+Pg743Xtv/q+55BKYNKn5slmz4Nvfhldfjffaay8YPDjC+rXX4rDD9dfHCTevvhpfbC69NP7+ffWrcQB07lwFuwSFu+weP/lJXKOzqv3jQEAMizjzzK6tqQ3nnhthmw/3+Jt16aVwwAHtrz90aGxey2PDRx4JGzfGiTr9+sV5U9/8JvTtG8F93XVw7LHROl+8OFrrlZXxh+LQQ2Osdkemj5d0U7eM7B4HHRSt8IcfLnYlraqvh698Ja5091//Ff3bH/1ofq/9wAei9b6rtm+HX/wi5iJ7+GH47Gcj6EUaqVtGuo933ok5Wlr2QxTZd78bJ6+uXBlfKjZujN6gRldfvfuvFV1ZGcMBAb70pd372ZIuCnfpei++GM3ixnPBu4Fly+LkmEYf/GD0VV96aVyDo3///FvtIt2Rwl261iuvwI9+FPe7wcUrfvObmFHwd7+LMD/mmOgn/+53m/rAa2ujK6Siori1iuwKhbt0rUsuiSEc48bFMI8iWLIkPnrz5ujLbpzO9Wtfg+99b+f1NSeYpIHCXbrO+vVxVPBrX2veNN5NNmyI7v6qKvj852PaVog+9pEjd22iSJHuTuEuXefnP49m8vnnQ3n5bvnI+vr4qN//Hk46KbpXamtjjHjPnjFPyn777ZZSRIpKV2KSruEes0MdeWQMwN4N/vKXmMDqvPOi+2Xw4Jh58MgjI/SHDYtT9UVKgVruUlg7dsSVDJYvj6saXHFFwT9i4cKYQyX7yjkQMxuWlcX5UqNGxfDG5cujW2bp0rhw0vDhBS9HpFvKK9zNbCJwI1AO3OHuN7R4fh/gTmAo8BZwhruvLXCtkgTnnx+nWEKMJ8y+JM0uWLEihi6edVZcnqyycucTWCsqYoz4ww/HtLEjRzbN9nvUUQUpQyQx2j1D1czKgZeAjwNrgQXAFHdfnrXO/cBv3H2WmR0LnOPuX2zrfXWGagpt3BgTeJ9yCkyeHJ3bBx3U5ksWL45rZ55+eoxSabyYcEtPPx3HZyEOjD79tPrOpTQV8gzVCcAqd1+deePZwCRgedY6+wNfzdx/AniwY+VKKtxzT1wt6aqr4qygdqxaFdeS3LAhhiQOGND6dTX22SfW+fnP4zqbCnaRtuUT7iOANVmP1wItJ9Z+Afg00XVzGtDfzPZ0943ZK5nZVGAqwOgEzPYnHeAend3jx+cM9jfeiPnGGyeFdI+wbmiIecpnz445yA88sO2P+fznu6B2kRTKJ9xzDU5u2Zfz78BNZnY28AfgdaBupxe5zwRmQnTLdKhS6d4WLYIXXoCbb97pqTffjCvNr1zZfPmAAXGm6GGHwTXX7J4yRUpFPuG+FhiV9XgksC57BXdfB3wKwMz6AZ929y2FKlK6iYsuiuZ3Ltu2xUUzp0zZ6amvfz3mH583Dw4+uGn5gAE6kUikq+QT7guAcWY2lmiRTwaafTk2syHAW+7eAFxBjJyRNFm7Fm67LS4L9IEP5F7nox+FQYOaLVq3Lg6YXnBB9K+LyO7Rbri7e52ZXQzMI4ZC3unuy8zsWuJCrXOBo4H/NDMnumWmdWHNsju89lpc2r7RAw9EB/msWfCe9+T1Fu5xsYm6uphiRkR2n7zGubv7I8AjLZZdnXV/DjCnsKVJ0dTUREf4G280X37ssTmDvTHE77mn+fK6umi5X3ll3n8PRKRAdIaq7Ow3v4lgv/HG5iNfssasf+97TRdV2r49Tv0/8cSdzwA98MCYI11Edi+Fu8CMGTEByxNPxIU1nn4aRoyAadOaTfh1112xyqZN8KtfRe4PHBjHUa+8Mq7xuZsnfhSRVijcS92WLXDxxdHEXro0LiDap0/0s2SC/fHHY5DMj38cZ4f27h0HSGfM2G2TPYpIByncS93ixfFz6dKYdWvx4manidbXxyRdGzfGJVDvu09XKBJJAk35W+qef77p/okn7nT+/zPPRLDfcw88+KCCXSQp1HIvdYsWxVHQH/0IPvShZk/V1sL990OPHjHLoogkh8K9lC1YEAdPDzkEPvvZZk+tWxfT5L78coyAHDiwSDWKSKeoW6ZUNTTECJnVq6n/yBEcc0xM4LVpUyweMyZGQ/7wh3Fiqogki1rupaquLk5WOuMM5oy9jCefhCefhFtuiQE006bFDIyHHlrsQkWkMxTupaouJu189PWD+Npl5ey3X1xfY9kyOPfcuLi0iCSXwr1UZcJ9/hM9eJ04KenUU4tbkogUjvrcS1Um3OvowY9+pGAXSRuFe6mqrQUi3DUSRiR9FO6lKqvlPmBAkWsRkYJTuJeqTLjX0lMtd5EUUriXKrXcRVJN4V6qFO4iqaZwL1VZ4a5uGZH0UbiXKrXcRVJN4V6qMuHuZT3o06fItYhIwSncS1VmnHtFZQ9dGk8khRTupSrTcu/VVzNQiKSRwr1UZcK9om/PIhciIl1B4V6qMuHeu59a7iJppHAvVQp3kVRTuJeqTLj36a9wF0kjhXupUriLpJrCvVQp3EVSTeFeouqr1ecukmYK9xJVuz1OYurZR+EukkZ5hbuZTTSzlWa2yswuz/H8aDN7wswWmdkSMzu58KVKIdW+Ey13hbtIOrUb7mZWDswATgL2B6aY2f4tVrsKuM/dPwxMBm4udKFSWHU7MuFeqZOYRNIon5b7BGCVu6929xpgNjCpxToONM4tOBBYV7gSpSu8G+5quYukUj6/2SOANVmP1wKHtVjnGuB3ZvavQF/g+IJUJ12mMdwrKhXuImmUT8s915yB3uLxFOBn7j4SOBn4hZnt9N5mNtXMFprZwg0bNnS8WimYxtEyCneRdMon3NcCo7Iej2TnbpdzgfsA3P3PQG9gSMs3cveZ7l7l7lVDhw7tXMVSEI0td80KKZJO+YT7AmCcmY01swrigOncFuv8DTgOwMw+QIS7mubdWGPLXX3uIunUbri7ex1wMTAPWEGMillmZtea2SmZ1b4GnG9mLwC/BM5295ZdN9KNNFTHOHe13EXSKa/fbHd/BHikxbKrs+4vB44obGnSld49Q7VS57GJpJF+s0tUfXUdNfSkV29dY08kjRTuJaqhto46etC7d7ErEZGuoHAvUQ01CneRNFO4l6jGcO/Vq9iViEhXULiXKFe3jEiqKdxLlKvlLpJqCvcS1dhyL9P/AJFU0q92qaqtpd50ApNIWincS5TX1dFQpnAXSSuFe6mqq6POdKEOkbRSuJcqtdxFUk3hXqKsrg5XuIuklsK9VNXX0VCucBdJK4V7ibJ6dcuIpJnCvURZXR2ulrtIaincS1RZfS0o3EVSS+FeoqxBLXeRNFO4l6iyhjq8h8a5i6SVwr1ElTXUQQ+13EXSSuFeoqLlrnAXSSuFe4kq9zpM4S6SWgr3ElXWUAc9Fe4iaaVwL0G1tdADtdxF0izZ4f7EE3DssVBdXexKEsMdzj8/wn2PvRTuImmV7N/uY4+Nn2vWwHvfW9xaujF3mD8f1q+HP/0JZs2Cm/rW0m9ssne/iLQuub/dr7/edH/LluLVsQtqamDTJthrr6ZldXWwYEH8zDZsGLzvfe2/5yuvwJgxsGIFbNwYy+bPh+uua1rnooug72wNhRRJs+T+dj/88Lt3t63dRN/xRaylHQ0NEdYVFdGKXr8e9tgDLr4Y7r4bHnoIDjggnrvgAnjkkdzvc9tt8IlPtP45v/gFXHEFHHkk/O//Nn9u8mT4znegZ08YORK4qy4eiEgqJTfc33773bvnnLaZW9+MwOxu3nkHJk6Edevg8ccjvH/7W9h7b9iwAerr4cQTm7/mO9+BCROaL7vhhnhte0aNimD/5CfhK1+JZb16wUc+AuXlWSvWqeUukmbJ/e2uqXn37kDfxOrVuzfc3eHUU2HIEPjb36LbZPPm6AKBCPTKSpgzJ1ruvXtHd4k7XHYZ3H57BPvTT8OyZbEOwNixcPzxO3/e4YfDAw/Ajh2t19SvX9T01FNw9NHxma1SuIukWnJ/u7PCfRCbWb++6z7KHc45JwL2wgtj2VNPwdy5cd8s1jGL/mx3uPnmeO688yJwhw2DX/0KqqrgU5+CM8+El1+O9zz88PZr6NsXvvjF/OqdODGPlRTuIqmW3N/umhoarIx6L2Mwm1i3rjBvW18P06ZFa7y8HP7t32LZrFlxq6yEl16Ce+6JVvsVV0SLfMMGGDgw+rYBjjgCtm+PcG906KFN9w84IG5F0dAQN4W7SGrl9dttZhOBG4Fy4A53v6HF8z8Ejsk8rASGufugQha6k5oa6ssr2FzXj8Fs7nS419dHK3vixDiwCXHg8sADYyTLKadEeO+9N7z//XDWWbHOBz8IV18NZ5+d+30///nO1dPMr38NZ5yx89CZXeUeP3VAVSS12g13MysHZgAfB9YCC8xsrrsvb1zH3b+atf6/Ah/uglqbq62lrqyCrTaIf6rYxKJOdss89FC0zisqmnp69tkHFi2Ct96C006Dv/4Vvv3taJV/5jNw2GFw7bXRDdOlfvAD6N8fpkwp/HuXl8MXvlD49xWRbiGflvsEYJW7rwYws9nAJGB5K+tPAb5VmPLaUFNDXVkFW8oHM6yi8y33738fhg+PU/I//ekI+299K3oshg2LA57Zfve7HG+yahW8+GLnCmjN1q3w5JNw/fXwzW8W9r1FJPXyCfcRwJqsx2uBw3KtaGb7AGOB37fy/FRgKsDo0aM7VOhOMuH+dvkghpVv6tQB1RtvjDM2b7oJvvxlKCuDW27pYIu8vh6OOy466QutV6848ioi0kH5hHuuqPNW1p0MzHH3+lxPuvtMYCZAVVVVa++Rn5oaaq2Ct3sOZpz/rd2W+44d0e3Ssyf06QOzZ8Mll0Rr/cILI9ihE10t8+dHsE+fDkcd1alNadWee2bOOBIR6Zh8wn0tMCrr8UigtSidDEzb1aLyUlNDHT3ZVjGI/tWbeOON1kf3PfBAdC/v2BHPX3hhHDT92MfgrrtanNyTr+9/P5r9L74YITx1arS0RUS6gXxmhVwAjDOzsWZWQQT43JYrmdn7gMHAnwtbYitqa6mxCrZXDKZP9WbcnTVrdl5t+fIYuXLwwZHHRx0V3TDvfz88+GA7J/q0Zt26OBPp2Wcj0K+5RsEuIt1Kuy13d68zs4uBecRQyDvdfZmZXQssdPfGoJ8CzHb3XetuyVdNDbVUsKP3IMrraqjkHX7600quvTaefu01+PrXY06x8vIYVThsWJzCf9NN0ZU9qKODNevro2/nzjtjnPiTT2o2ShHplvIa5+7ujwCPtFh2dYvH1xSurDzU1FBNBTv6DAZg8glvMX16JStXxjQEmzbB/ffHqtOmRbBDnOn5jW904vPq66P5vzwzSOiooxTsItJtJfcUxZoaar0nW/rHAcfLpqxh4f+NZMkSWL06GtjHHRfj1y+7rACf99hjEexTp8K++8KkSQV4UxGRrpHocK/2XmwaPBaA91W8wgsvfASIOVyuvDKGNY4b18rr3WPFxknP23PXXXHgdPp09a+LSLeX3HCvrWWH92fr4DHx+JVX3n3qtNPi1qY//jHGQXbEN76hYBeRREhuuNfUUN1QQVm/yriUUVa45+WOO2DAAFi8OPpu2mMWp7KKiCRAosN9R0NFDGUcM6b1cP+//4MvfQm2bWu+/NlnYx7fsWO7ulIRkd0un3Hu3VNNDTsaeka4jx0Lr76ae72ZM+HRR6PlXVbWdPvYx+CrX839GhGRhEtsy91ra3mnvqIp3OfMicHtZVl/r9zhpz+NSxs1XiJJRKQEJDbcqamhhky47z0u5h4YMyb3ut/5zu6sTESk6BIb7l6dFe6f+1zMCFZdvfOKffvC6afv9vpERIopseHerOVeWRlXLBIRESDBB1SttoZaenZu4i8RkZRLbLhTW9vUchcRkWaSGe719VhDg8JdRKQVyQz3zJWsFe4iIrklOtzV5y4ikluiw10tdxGR3JIZ7rW1gMJdRKQ1yQv3J56IK1yjcBcRaU3ywv2FF+KCqCjcRURak7xwHzLk3bs6oCoiklvywn3PPd+9W0OFLowkIpJD8sI9q+WucBcRyU3hLiKSQskL9xbdMj17FrEWEZFuKnnh3r//u3etZ0/MiliLiEg3lbxwz07ziori1SEi0o0lL9yzKdxFRHJSuIuIpFCiw916KdxFRHJJZriXRdllvTRURkQkl7zC3cwmmtlKM1tlZpe3ss5nzWy5mS0zs3sKW2YLAwYAUN4rudf3FhHpSu2Gu5mVAzOAk4D9gSlmtn+LdcYBVwBHuPsBwCVdUGuTadMAaOjTt0s/RkQkqfJpuU8AVrn7anevAWYDk1qscz4ww903Abj73wtbZgvXXccJR2yjobJfl36MiEhS5RPuI4A1WY/XZpZl2w/Yz8yeNrNnzGxioQrMyYytdZWaekBEpBX5dFrnOgfUc7zPOOBoYCTwRzM70N03N3sjs6nAVIDRo0d3uNhs1dUo3EVEWpFPy30tMCrr8UhgXY51HnL3Wnd/BVhJhH0z7j7T3avcvWro0KGdrRlQuIuItCWfcF8AjDOzsWZWAUwG5rZY50HgGAAzG0J006wuZKEtKdxFRFrXbri7ex1wMTAPWAHc5+7LzOxaMzsls9o8YKOZLQeeAL7u7hu7qmhQuIuItCWvgeLu/gjwSItlV2fdd+DSzG23ULiLiLQumWeoonAXEWmLwl1EJIUSGe7uUFOjcBcRaU0iw72mJn4q3EVEcktkuFdXx0+Fu4hIbgp3EZEUSmS4q1tGRKRtiQx3tdxFRNqmcBcRSSGFu4hICincRURSSOEuIpJCCncRkRRSuIuIpJDCXUQkhRTuIiIppHAXEUkhhbuISAop3EVEUiiR4f722/Gzb9/i1iEi0l0lMty3boXevaGiotiViIh0T4kM9y1bYODAYlchItJ9JTLct26FAQOKXYWISPeVyHDfskXhLiLSlkSG+9at6pYREWlLYsNdLXcRkdYlMtzVLSMi0rZEhru6ZURE2pa4cHdXt4yISHsSF+7bt0N9vcJdRKQtiQv3rVvjp7plRERal1e4m9lEM1tpZqvM7PIcz59tZhvMbHHmdl7hSw1btsRPtdxFRFrXo70VzKwcmAF8HFgLLDCzue6+vMWq97r7xV1QYzNquYuItC+flvsEYJW7r3b3GmA2MKlry2pdY7ir5S4i0rp8wn0EsCbr8drMspY+bWZLzGyOmY0qSHU5qFtGRKR9+YS75VjmLR7/Ghjj7gcDjwGzcr6R2VQzW2hmCzds2NCxSjPULSMi0r58wn0tkN0SHwmsy17B3Te6e+b6SNwOjM/1Ru4+092r3L1q6NChnalXLXcRkTzkE+4LgHFmNtbMKoDJwNzsFcxseNbDU4AVhSuxubFj4bTToH//rvoEEZHka3e0jLvXmdnFwDygHLjT3ZeZ2bXAQnefC/ybmZ0C1AFvAWd3VcGTJsVNRERaZ+4tu893j6qqKl+4cGFRPltEJKnM7Dl3r2pvvcSdoSoiIu1TuIuIpJDCXUQkhRTuIiIppHAXEUkhhbuISAop3EVEUqho49zNbAPwWideOgR4s8DlFIu2pXvStnRP2pawj7u3O39L0cK9s8xsYT4D+JNA29I9aVu6J21Lx6hbRkQkhRTuIiIplMRwn1nsAgpI29I9aVu6J21LBySuz11ERNqXxJa7iIi0I1HhbmYTzWylma0ys8uLXU9HmdmrZvaimS02s4WZZXuY2Xwz+2vm5+Bi15mLmd1pZn83s6VZy3LWbmF6Zj8tMbNDilf5zlrZlmvM7PXMvllsZidnPXdFZltWmtmJxal6Z2Y2ysyeMLMVZrbMzL6SWZ64/dLGtiRxv/Q2s7+Y2QuZbfl/meVjzezZzH65N3PxI8ysV+bxqszzYwpSiLsn4kZcKORlYF+gAngB2L/YdXVwG14FhrRY9l/A5Zn7lwPfLXadrdR+FHAIsLS92oGTgUeJ6+8eDjxb7Prz2JZrgH/Pse7+mf9rvYCxmf+D5cXehkxtw4FDMvf7Ay9l6k3cfmljW5K4Xwzol7nfE3g28+99HzA5s/xW4MuZ+xcBt2buTwbuLUQdSWq5TwBWuftqd68BZgNpuCbTJJouKD4LOLWItbTK3f9AXGUrW2u1TwJ+7uEZYFCLSzEWVSvb0ppJwGx3r3b3V4BVxP/FonP39e7+fOb+P4gLTphoAAAClklEQVTLW44ggfuljW1pTXfeL+7ub2ce9szcHDgWmJNZ3nK/NO6vOcBxZma7WkeSwn0EsCbr8Vra3vndkQO/M7PnzGxqZtle7r4e4j84MKxo1XVca7UndV9dnOmuuDOreywR25L5Kv9hopWY6P3SYlsggfvFzMrNbDHwd2A+8c1is7vXZVbJrvfdbck8vwXYc1drSFK45/pLlrShPke4+yHAScA0Mzuq2AV1kSTuq1uA9wAfAtYD388s7/bbYmb9gP8BLnH3rW2tmmNZd9+WRO4Xd6939w8BI4lvFB/ItVrmZ5dsS5LCfS0wKuvxSGBdkWrpFHdfl/n5d+BXxE5/o/Grcebn34tXYYe1Vnvi9pW7v5H5hWwAbqfpK3633hYz60mE4d3u/kBmcSL3S65tSep+aeTum4EniT73QWbWI/NUdr3vbkvm+YHk323YqiSF+wJgXOaIcwVx4GFukWvKm5n1NbP+jfeBE4ClxDaclVntLOCh4lTYKa3VPhc4MzM643BgS2M3QXfVou/5NGLfQGzL5MyIhrHAOOAvu7u+XDL9sj8BVrj7D7KeStx+aW1bErpfhprZoMz9PsDxxDGEJ4DPZFZruV8a99dngN975ujqLin2keUOHoU+mTiK/jJwZbHr6WDt+xJH918AljXWT/StPQ78NfNzj2LX2kr9vyS+FtcSLY1zW6ud+Jo5I7OfXgSqil1/Htvyi0ytSzK/bMOz1r8ysy0rgZOKXX9WXUcSX9+XAIszt5OTuF/a2JYk7peDgUWZmpcCV2eW70v8AVoF3A/0yizvnXm8KvP8voWoQ2eoioikUJK6ZUREJE8KdxGRFFK4i4ikkMJdRCSFFO4iIimkcBcRSSGFu4hICincRURS6P8D3FW/w+tzgV0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Looking at the loss and accuracy on the training and validation sets during the training\n",
     "# This can be done by using Keras callback \"history\" which is applied by default\n",
@@ -1427,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1443,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1510,29 +738,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Neural Net:\t 484 / 500 correct\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "_, ax = plt.subplots(figsize=(6, 6))\n",
     "\n",
@@ -1542,18 +750,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The acuracy on the  5  validation folds: [0.72 0.94 0.95 0.75 0.94]\n",
-      "The Average acuracy on the  5  validation folds: 0.86\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Applying K-fold cross-validation\n",
     "# Here we pass the whole dataset, i.e. features and labels, instead of splitting it.\n",
@@ -1594,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1603,17 +802,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9580000002384186 {'epochs': 500}\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "HP_grid = dict(epochs=[300, 500, 1000])\n",
     "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
@@ -1623,25 +814,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/tarunchadha/anaconda3/envs/mlw-2/lib/python3.6/site-packages/sklearn/model_selection/_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
-      "  DeprecationWarning)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8299999971389771 {'batch_size': 30, 'epochs': 30}\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "HP_grid = {'epochs' : [10, 15, 30], \n",
     "           'batch_size' : [10, 20, 30] }\n",
@@ -1704,22 +879,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "circle = pd.read_csv(\"2d_points.csv\")\n",
     "# Using x and y coordinates as featues\n",
@@ -1771,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1786,17 +948,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000, 28, 28)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Looking at the dataset\n",
     "print(X_train.shape)"
@@ -1804,33 +958,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This digit is:  4\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 250,
-       "width": 253
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
     "import matplotlib.pyplot as plt\n",
@@ -1842,17 +972,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 255\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Look at the data values for a couple of images\n",
     "print(X_train[0].min(), X_train[1].max())"
@@ -1867,17 +989,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000,)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# The labels are the digit on the image\n",
     "print(y_train.shape)"
@@ -1885,7 +999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1921,17 +1035,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000, 10)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from keras.utils.np_utils import to_categorical\n",
     "\n",
@@ -1943,57 +1049,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/20\n",
-      "60000/60000 [==============================] - 4s 60us/step - loss: 0.5733 - acc: 0.8465 - val_loss: 0.3185 - val_acc: 0.9084\n",
-      "Epoch 2/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2526 - acc: 0.9269 - val_loss: 0.2461 - val_acc: 0.9244\n",
-      "Epoch 3/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1955 - acc: 0.9427 - val_loss: 0.1884 - val_acc: 0.9423\n",
-      "Epoch 4/20\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.1595 - acc: 0.9540 - val_loss: 0.1494 - val_acc: 0.9562\n",
-      "Epoch 5/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1353 - acc: 0.9601 - val_loss: 0.1602 - val_acc: 0.9502\n",
-      "Epoch 6/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.1172 - acc: 0.9654 - val_loss: 0.1331 - val_acc: 0.9623\n",
-      "Epoch 7/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1040 - acc: 0.9692 - val_loss: 0.1227 - val_acc: 0.9629\n",
-      "Epoch 8/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0932 - acc: 0.9720 - val_loss: 0.1422 - val_acc: 0.9569\n",
-      "Epoch 9/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0839 - acc: 0.9752 - val_loss: 0.1090 - val_acc: 0.9668\n",
-      "Epoch 10/20\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0771 - acc: 0.9772 - val_loss: 0.1327 - val_acc: 0.9588\n",
-      "Epoch 11/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0701 - acc: 0.9794 - val_loss: 0.1291 - val_acc: 0.9594\n",
-      "Epoch 12/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0643 - acc: 0.9804 - val_loss: 0.1032 - val_acc: 0.9716\n",
-      "Epoch 13/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0592 - acc: 0.9820 - val_loss: 0.1186 - val_acc: 0.9633\n",
-      "Epoch 14/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0547 - acc: 0.9830 - val_loss: 0.0999 - val_acc: 0.9697\n",
-      "Epoch 15/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0501 - acc: 0.9852 - val_loss: 0.1119 - val_acc: 0.9658\n",
-      "Epoch 16/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0461 - acc: 0.9862 - val_loss: 0.0948 - val_acc: 0.9712\n",
-      "Epoch 17/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0431 - acc: 0.9875 - val_loss: 0.1183 - val_acc: 0.9642\n",
-      "Epoch 18/20\n",
-      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0395 - acc: 0.9881 - val_loss: 0.1122 - val_acc: 0.9671\n",
-      "Epoch 19/20\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.0360 - acc: 0.9892 - val_loss: 0.0943 - val_acc: 0.9724\n",
-      "Epoch 20/20\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.0337 - acc: 0.9898 - val_loss: 0.1361 - val_acc: 0.9597\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Building the keras model\n",
     "from keras.models import Sequential\n",
@@ -2020,18 +1078,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000/10000 [==============================] - 0s 49us/step\n",
-      "The [loss, accuracy] on test dataset are:  [0.1360580855519511, 0.9597]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
    ]
@@ -2082,116 +1131,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/50\n",
-      "60000/60000 [==============================] - 3s 53us/step - loss: 1.5752 - acc: 0.8318\n",
-      "Epoch 2/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.8282 - acc: 0.9022\n",
-      "Epoch 3/50\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.6734 - acc: 0.9108\n",
-      "Epoch 4/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.5963 - acc: 0.9154\n",
-      "Epoch 5/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.5494 - acc: 0.9201\n",
-      "Epoch 6/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.5158 - acc: 0.9233\n",
-      "Epoch 7/50\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.4909 - acc: 0.9267\n",
-      "Epoch 8/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.4721 - acc: 0.9300\n",
-      "Epoch 9/50\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.4544 - acc: 0.9332\n",
-      "Epoch 10/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.4393 - acc: 0.9356\n",
-      "Epoch 11/50\n",
-      "60000/60000 [==============================] - 1s 21us/step - loss: 0.4266 - acc: 0.9376\n",
-      "Epoch 12/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.4142 - acc: 0.9395\n",
-      "Epoch 13/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.4044 - acc: 0.9411\n",
-      "Epoch 14/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3966 - acc: 0.9423\n",
-      "Epoch 15/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3868 - acc: 0.9440\n",
-      "Epoch 16/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3792 - acc: 0.9449\n",
-      "Epoch 17/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3711 - acc: 0.9454\n",
-      "Epoch 18/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3640 - acc: 0.9474\n",
-      "Epoch 19/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3578 - acc: 0.9488\n",
-      "Epoch 20/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3535 - acc: 0.9488\n",
-      "Epoch 21/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3472 - acc: 0.9498\n",
-      "Epoch 22/50\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3399 - acc: 0.9514\n",
-      "Epoch 23/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3356 - acc: 0.9519\n",
-      "Epoch 24/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.3325 - acc: 0.9531\n",
-      "Epoch 25/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3278 - acc: 0.9530\n",
-      "Epoch 26/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3234 - acc: 0.9532\n",
-      "Epoch 27/50\n",
-      "60000/60000 [==============================] - 1s 25us/step - loss: 0.3199 - acc: 0.9544\n",
-      "Epoch 28/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3153 - acc: 0.9549\n",
-      "Epoch 29/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.3129 - acc: 0.9555\n",
-      "Epoch 30/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.3087 - acc: 0.9565\n",
-      "Epoch 31/50\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3054 - acc: 0.9569\n",
-      "Epoch 32/50\n",
-      "60000/60000 [==============================] - 2s 25us/step - loss: 0.3028 - acc: 0.9571\n",
-      "Epoch 33/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2991 - acc: 0.9573\n",
-      "Epoch 34/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2964 - acc: 0.9579\n",
-      "Epoch 35/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2932 - acc: 0.9579\n",
-      "Epoch 36/50\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.2911 - acc: 0.9588\n",
-      "Epoch 37/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.2895 - acc: 0.9585\n",
-      "Epoch 38/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2875 - acc: 0.9594\n",
-      "Epoch 39/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2840 - acc: 0.9601\n",
-      "Epoch 40/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2819 - acc: 0.9596\n",
-      "Epoch 41/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2805 - acc: 0.9603\n",
-      "Epoch 42/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2769 - acc: 0.9603\n",
-      "Epoch 43/50\n",
-      "60000/60000 [==============================] - 1s 25us/step - loss: 0.2755 - acc: 0.9616\n",
-      "Epoch 44/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2734 - acc: 0.9612\n",
-      "Epoch 45/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2711 - acc: 0.9618\n",
-      "Epoch 46/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2699 - acc: 0.9616\n",
-      "Epoch 47/50\n",
-      "60000/60000 [==============================] - 1s 22us/step - loss: 0.2698 - acc: 0.9616\n",
-      "Epoch 48/50\n",
-      "60000/60000 [==============================] - 1s 25us/step - loss: 0.2676 - acc: 0.9613\n",
-      "Epoch 49/50\n",
-      "60000/60000 [==============================] - 1s 24us/step - loss: 0.2648 - acc: 0.9621\n",
-      "Epoch 50/50\n",
-      "60000/60000 [==============================] - 1s 23us/step - loss: 0.2644 - acc: 0.9629\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Adding l2 regularization\n",
     "# Building the keras model\n",
@@ -2224,18 +1166,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000/10000 [==============================] - 1s 128us/step\n",
-      "The [loss, accuracy] on test dataset are:  [0.2548498446464539, 0.9646]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
    ]
@@ -2264,116 +1197,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/50\n",
-      "60000/60000 [==============================] - 5s 85us/step - loss: 0.7865 - acc: 0.7668\n",
-      "Epoch 2/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.3798 - acc: 0.8885\n",
-      "Epoch 3/50\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.3082 - acc: 0.9102\n",
-      "Epoch 4/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.2685 - acc: 0.9217\n",
-      "Epoch 5/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.2442 - acc: 0.9288\n",
-      "Epoch 6/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.2250 - acc: 0.9342\n",
-      "Epoch 7/50\n",
-      "60000/60000 [==============================] - 2s 26us/step - loss: 0.2118 - acc: 0.9372\n",
-      "Epoch 8/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.2007 - acc: 0.9394\n",
-      "Epoch 9/50\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1931 - acc: 0.9419\n",
-      "Epoch 10/50\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1837 - acc: 0.9443\n",
-      "Epoch 11/50\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1766 - acc: 0.9462\n",
-      "Epoch 12/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.1748 - acc: 0.9477\n",
-      "Epoch 13/50\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1687 - acc: 0.9492\n",
-      "Epoch 14/50\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1607 - acc: 0.9515\n",
-      "Epoch 15/50\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1608 - acc: 0.9502\n",
-      "Epoch 16/50\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1571 - acc: 0.9528\n",
-      "Epoch 17/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.1526 - acc: 0.9538\n",
-      "Epoch 18/50\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1474 - acc: 0.9554\n",
-      "Epoch 19/50\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1471 - acc: 0.9545\n",
-      "Epoch 20/50\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1446 - acc: 0.9561\n",
-      "Epoch 21/50\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1408 - acc: 0.9572\n",
-      "Epoch 22/50\n",
-      "60000/60000 [==============================] - 2s 27us/step - loss: 0.1360 - acc: 0.9583\n",
-      "Epoch 23/50\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1358 - acc: 0.9581\n",
-      "Epoch 24/50\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1320 - acc: 0.9597\n",
-      "Epoch 25/50\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1315 - acc: 0.9589\n",
-      "Epoch 26/50\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1285 - acc: 0.9605\n",
-      "Epoch 27/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1283 - acc: 0.9602\n",
-      "Epoch 28/50\n",
-      "60000/60000 [==============================] - 2s 28us/step - loss: 0.1272 - acc: 0.9601\n",
-      "Epoch 29/50\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1262 - acc: 0.9603\n",
-      "Epoch 30/50\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1236 - acc: 0.9618\n",
-      "Epoch 31/50\n",
-      "60000/60000 [==============================] - 2s 29us/step - loss: 0.1204 - acc: 0.9623\n",
-      "Epoch 32/50\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1201 - acc: 0.9623\n",
-      "Epoch 33/50\n",
-      "60000/60000 [==============================] - 2s 32us/step - loss: 0.1199 - acc: 0.9618\n",
-      "Epoch 34/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1165 - acc: 0.9637\n",
-      "Epoch 35/50\n",
-      "60000/60000 [==============================] - 2s 32us/step - loss: 0.1173 - acc: 0.9630\n",
-      "Epoch 36/50\n",
-      "60000/60000 [==============================] - 2s 32us/step - loss: 0.1161 - acc: 0.9638\n",
-      "Epoch 37/50\n",
-      "60000/60000 [==============================] - 2s 34us/step - loss: 0.1152 - acc: 0.9635\n",
-      "Epoch 38/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1150 - acc: 0.9639\n",
-      "Epoch 39/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1148 - acc: 0.9631\n",
-      "Epoch 40/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1116 - acc: 0.9641\n",
-      "Epoch 41/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1106 - acc: 0.9651\n",
-      "Epoch 42/50\n",
-      "60000/60000 [==============================] - 2s 33us/step - loss: 0.1109 - acc: 0.9645\n",
-      "Epoch 43/50\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1105 - acc: 0.9647\n",
-      "Epoch 44/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1071 - acc: 0.9667\n",
-      "Epoch 45/50\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1064 - acc: 0.9662\n",
-      "Epoch 46/50\n",
-      "60000/60000 [==============================] - 2s 32us/step - loss: 0.1060 - acc: 0.9666\n",
-      "Epoch 47/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1071 - acc: 0.9668\n",
-      "Epoch 48/50\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1066 - acc: 0.9666\n",
-      "Epoch 49/50\n",
-      "60000/60000 [==============================] - 2s 30us/step - loss: 0.1060 - acc: 0.9660\n",
-      "Epoch 50/50\n",
-      "60000/60000 [==============================] - 2s 31us/step - loss: 0.1033 - acc: 0.9676\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Solution\n",
     "# Adding Dropout\n",
@@ -2407,18 +1233,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000/10000 [==============================] - 2s 199us/step\n",
-      "The [loss, accuracy] on test dataset are:  [0.09923268887351733, 0.9732]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
    ]
@@ -2493,7 +1310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2514,33 +1331,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This item is a:  Pullover\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 250,
-       "width": 253
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
     "import matplotlib.pyplot as plt\n",
@@ -2552,17 +1345,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(60000, 10)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Also we need to reshape the input data such that each sample is a 4D matrix of dimension\n",
     "# (num_samples, width, height, channels). Even though these images are grayscale we need to add\n",
@@ -2580,39 +1365,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_75 (Conv2D)           (None, 26, 26, 6)         60        \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_65 (MaxPooling (None, 13, 13, 6)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_76 (Conv2D)           (None, 11, 11, 16)        880       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_66 (MaxPooling (None, 5, 5, 16)          0         \n",
-      "_________________________________________________________________\n",
-      "flatten_29 (Flatten)         (None, 400)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_186 (Dense)            (None, 120)               48120     \n",
-      "_________________________________________________________________\n",
-      "dense_187 (Dense)            (None, 84)                10164     \n",
-      "_________________________________________________________________\n",
-      "dense_188 (Dense)            (None, 10)                850       \n",
-      "=================================================================\n",
-      "Total params: 60,074\n",
-      "Trainable params: 60,074\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Creating a CNN similar to the one shown in the figure from LeCun paper\n",
     "# In the original implementation Average pooling was used. However, we will use maxpooling as this \n",
@@ -2651,62 +1406,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/20\n",
-      "60000/60000 [==============================] - 39s 654us/step - loss: 0.5778 - acc: 0.7859 - val_loss: 0.4449 - val_acc: 0.8398\n",
-      "Epoch 2/20\n",
-      "60000/60000 [==============================] - 36s 593us/step - loss: 0.3826 - acc: 0.8586 - val_loss: 0.4165 - val_acc: 0.8462\n",
-      "Epoch 3/20\n",
-      "60000/60000 [==============================] - 37s 622us/step - loss: 0.3315 - acc: 0.8765 - val_loss: 0.3604 - val_acc: 0.8732\n",
-      "Epoch 4/20\n",
-      "60000/60000 [==============================] - 38s 625us/step - loss: 0.2993 - acc: 0.8876 - val_loss: 0.3470 - val_acc: 0.8768\n",
-      "Epoch 5/20\n",
-      "60000/60000 [==============================] - 37s 619us/step - loss: 0.2791 - acc: 0.8960 - val_loss: 0.3262 - val_acc: 0.8829\n",
-      "Epoch 6/20\n",
-      "60000/60000 [==============================] - 38s 625us/step - loss: 0.2614 - acc: 0.9016 - val_loss: 0.3000 - val_acc: 0.8913\n",
-      "Epoch 7/20\n",
-      "60000/60000 [==============================] - 37s 622us/step - loss: 0.2463 - acc: 0.9074 - val_loss: 0.3715 - val_acc: 0.8696\n",
-      "Epoch 8/20\n",
-      "60000/60000 [==============================] - 37s 620us/step - loss: 0.2352 - acc: 0.9126 - val_loss: 0.3060 - val_acc: 0.8888\n",
-      "Epoch 9/20\n",
-      "60000/60000 [==============================] - 37s 624us/step - loss: 0.2247 - acc: 0.9162 - val_loss: 0.3020 - val_acc: 0.8901\n",
-      "Epoch 10/20\n",
-      "60000/60000 [==============================] - 38s 625us/step - loss: 0.2152 - acc: 0.9188 - val_loss: 0.3262 - val_acc: 0.8878\n",
-      "Epoch 11/20\n",
-      "60000/60000 [==============================] - 38s 631us/step - loss: 0.2077 - acc: 0.9222 - val_loss: 0.2973 - val_acc: 0.8961\n",
-      "Epoch 12/20\n",
-      "60000/60000 [==============================] - 38s 636us/step - loss: 0.1999 - acc: 0.9245 - val_loss: 0.2893 - val_acc: 0.8997\n",
-      "Epoch 13/20\n",
-      "60000/60000 [==============================] - 40s 661us/step - loss: 0.1926 - acc: 0.9279 - val_loss: 0.3193 - val_acc: 0.8929\n",
-      "Epoch 14/20\n",
-      "59904/60000 [============================>.] - ETA: 0s - loss: 0.1851 - acc: 0.9311"
-     ]
-    },
-    {
-     "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-141-f774d95b4160>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m model_run = model.fit(X_train_prep, y_train_onehot, epochs=20, \n\u001b[0;32m----> 2\u001b[0;31m                       batch_size=64, validation_data=(X_test_prep, y_test_onehot))\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/envs/mlw-2/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1040\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
-      "\u001b[0;32m~/anaconda3/envs/mlw-2/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    210\u001b[0m                         val_outs = test_loop(model, val_f, val_ins,\n\u001b[1;32m    211\u001b[0m                                              \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m                                              verbose=0)\n\u001b[0m\u001b[1;32m    213\u001b[0m                         \u001b[0mval_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m                         \u001b[0;31m# Same labels assumed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/envs/mlw-2/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mtest_loop\u001b[0;34m(model, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m    390\u001b[0m                 \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 392\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    393\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    394\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\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~/anaconda3/envs/mlw-2/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/envs/mlw-2/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/anaconda3/envs/mlw-2/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "num_epochs = 10\n",
     "model_run = model.fit(X_train_prep, y_train_onehot, epochs=num_epochs, \n",
@@ -2729,123 +1431,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/linux-graphics/anaconda3/envs/machine_learning_workshop/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: get_ipython_dir has moved to the IPython.paths module since IPython 4.0.\n",
-      "  if __name__ == '__main__':\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>\n",
-       "    \n",
-       "    @import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
-       "    \n",
-       "    @import url('http://fonts.googleapis.com/css?family=Kameron');\n",
-       "    @import url('http://fonts.googleapis.com/css?family=Crimson+Text');\n",
-       "    \n",
-       "    @import url('http://fonts.googleapis.com/css?family=Lato');\n",
-       "    @import url('http://fonts.googleapis.com/css?family=Source+Sans+Pro');\n",
-       "    \n",
-       "    @import url('http://fonts.googleapis.com/css?family=Lora'); \n",
-       "\n",
-       "    \n",
-       "    body {\n",
-       "        font-family: 'Lora', Consolas, sans-serif;\n",
-       "       \n",
-       "        -webkit-print-color-adjust: exact important !;\n",
-       "        \n",
-       "      \n",
-       "       \n",
-       "    }\n",
-       "    \n",
-       "    .alert-block {\n",
-       "        width: 95%;\n",
-       "        margin: auto;\n",
-       "    }\n",
-       "    \n",
-       "    .rendered_html code\n",
-       "    {\n",
-       "        color: black;\n",
-       "        background: #eaf0ff;\n",
-       "        background: #f5f5f5; \n",
-       "        padding: 1pt;\n",
-       "        font-family:  'Source Code Pro', Consolas, monocco, monospace;\n",
-       "    }\n",
-       "    \n",
-       "    p {\n",
-       "      line-height: 140%;\n",
-       "    }\n",
-       "    \n",
-       "    strong code {\n",
-       "        background: red;\n",
-       "    }\n",
-       "    \n",
-       "    .rendered_html strong code\n",
-       "    {\n",
-       "        background: #f5f5f5;\n",
-       "    }\n",
-       "    \n",
-       "    .CodeMirror pre {\n",
-       "    font-family: 'Source Code Pro', monocco, Consolas, monocco, monospace;\n",
-       "    }\n",
-       "    \n",
-       "    .cm-s-ipython span.cm-keyword {\n",
-       "        font-weight: normal;\n",
-       "     }\n",
-       "     \n",
-       "     strong {\n",
-       "         background: #f5f5f5;\n",
-       "         margin-top: 4pt;\n",
-       "         margin-bottom: 4pt;\n",
-       "         padding: 2pt;\n",
-       "         border: 0.5px solid #a0a0a0;\n",
-       "         font-weight: bold;\n",
-       "         color: darkred;\n",
-       "     }\n",
-       "     \n",
-       "    \n",
-       "    div #notebook {\n",
-       "        # font-size: 10pt; \n",
-       "        line-height: 145%;\n",
-       "        }\n",
-       "        \n",
-       "    li {\n",
-       "        line-height: 145%;\n",
-       "    }\n",
-       "\n",
-       "    div.output_area pre {\n",
-       "        background: #fff9d8 !important;\n",
-       "        padding: 5pt;\n",
-       "       \n",
-       "       -webkit-print-color-adjust: exact; \n",
-       "        \n",
-       "    }\n",
-       " \n",
-       "    \n",
-       " \n",
-       "    h1, h2, h3, h4 {\n",
-       "        font-family: Kameron, arial;\n",
-       "    }\n",
-       "    \n",
-       "    div#maintoolbar {display: none !important;}\n",
-       "    </style>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "#REMOVEBEGIN\n",
     "# THE LINES BELOW ARE JUST FOR STYLING THE CONTENT ABOVE !\n",
-- 
GitLab